Electric boiler self-adaptive frequency modulation control method and device considering power grid frequency fluctuation

Abstract

The invention discloses an electric boiler self-adaptive frequency modulation control method and device considering power grid frequency fluctuation, and aims at the problems of power grid inertia reduction and frequency stability deterioration caused by high-proportion new energy access. And adaptively identifying a frequency fluctuation mode, and dynamically selecting or fusing a primary / secondary frequency modulation control strategy. And an electric boiler thermal dynamic model is further introduced, a power instruction is optimized by adopting model prediction control, and rapid, smooth and accurate frequency modulation response is realized on the premise of strictly meeting the heat supply temperature constraint of a user. The system is composed of a frequency monitor, a central adaptive controller, a power execution unit and a temperature feedback module, has the advantages of excellent response performance, good user experience, high reliability, easy clustering deployment and the like, and is suitable for an application scene in which a large-scale flexible load participates in power grid frequency adjustment.

Description

Technical Field

[0001] This invention relates to the field of power system automation and control technology, and in particular to an adaptive frequency regulation control method and device for electric boilers that takes into account grid frequency fluctuations. Background Technology

[0002] With the advancement of the "dual carbon" target, the penetration rate of intermittent new energy sources such as wind power and photovoltaics in the power grid continues to increase, and traditional synchronous generators are gradually being replaced, resulting in a significant decrease in the rotational inertia of the power grid. As a core indicator for measuring power quality and system stability, the dynamic characteristics of power grid frequency directly affect the safe operation of the power system. In related technologies, a multi-level frequency stability control system has been constructed through the coordinated operation of generation-side frequency regulation and load-side demand response. Specifically, this system covers the entire process from frequency monitoring and deviation calculation to power regulation, including key aspects such as frequency deviation analysis based on Δf(t)=f(t)-f_N and frequency change rate assessment based on ROCOF(t)=d(Δf(t)) / dt. Among these, electric boilers, with their high power and thermal energy storage characteristics, have become an important flexible resource for load-side frequency regulation, and their thermal energy storage capacity enables the cross-timescale conversion of electrical and thermal energy.

[0003] However, most existing frequency control methods for electric boilers directly employ step or fixed proportional control strategies without establishing a two-parameter characteristic analysis framework for Δf(t) and ROCOF(t), resulting in significant limitations in control response. Specifically, when |Δf(t)| exceeds the dead zone threshold, the electric boiler adjusts power using a fixed K_pr1 coefficient, failing to consider the multimodal characteristics of grid frequency events (such as steady-state fluctuations under small disturbances, primary frequency regulation under large disturbances, secondary frequency regulation recovery, and frequency crises). This control method may trigger sudden power changes (such as instantaneous changes of ±400kW) in Mode II (primary frequency regulation under large disturbances), causing secondary impacts on the grid; in Mode IV (frequency crisis), fixed parameters are difficult to dynamically match the grid's support requirements. Meanwhile, existing technologies have not fully exploited the physical characteristics of the thermal inertia time constant C×dT / dt=ηP_elec-Q_loss(T) of electric boilers, leading to coupling conflicts between power regulation and heating quality. When temperature fluctuations exceed the comfort range of [75℃, 95℃], users' willingness to participate in frequency regulation is significantly reduced. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose an adaptive frequency regulation control method for electric boilers that takes into account power grid frequency fluctuations.

[0006] Another objective of this invention is to provide an adaptive frequency control device for electric boilers that takes into account power grid frequency fluctuations.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention proposes an adaptive frequency regulation control method for an electric boiler that considers grid frequency fluctuations, comprising: S1: Real-time acquisition of power grid frequency data and filtering and noise reduction processing, calculation of frequency deviation and frequency change rate; S2, based on the frequency deviation and frequency change rate, identify the multimodal characteristics of power grid frequency fluctuations, and classify fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises; S3 dynamically adjusts the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generates the corresponding active power regulation command, and achieves a smooth transition between different frequency regulation modes through a weighted fusion strategy; S4, combining the thermal dynamic model of the electric boiler and the preset heating temperature constraint range, perform model predictive control optimization on the active power adjustment command to generate a smooth power command sequence that satisfies thermal inertia constraints. S5, the first power value in the smooth power command sequence is sent out and executed through a power electronic switching device to realize continuous adjustment of the electric boiler power and grid frequency support.

[0010] In one embodiment of the present invention, S1 includes: S11, a low-pass digital filter is used to filter and denoise the original frequency data. The cutoff frequency of the low-pass filter is set to be less than 5% of the fundamental frequency of the power grid. S12, based on formula Calculate the frequency deviation and use the formula Calculate the rate of change of frequency.

[0011] In one embodiment of the present invention, S2 includes: S21 defines small-disturbance steady-state fluctuations as and ; S22 defines the crisis of rapidly deteriorating frequency as and Exceeding within 2 seconds .

[0012] In one embodiment of the present invention, S3 includes: S31, when identified as mode IV, the primary frequency modulation coefficient is... Temporarily adjusted to ,in This is the preset base value; S32, through formula To achieve weighted fusion, among which and Calculated dynamically based on the ratio of frequency deviation to rate of change.

[0013] In one embodiment of the present invention, S4 includes: S41, based on formula Predict the temperature trajectory over the next 20 minutes; S42, constrain the heating temperature range Set as And the risk of temperature exceeding the limit is minimized through a penalty function.

[0014] To achieve the above objectives, a second aspect of the present invention provides an adaptive frequency regulation control device for an electric boiler that takes into account power grid frequency fluctuations, comprising: The frequency data acquisition and processing module is used to acquire power grid frequency data in real time, perform filtering and noise reduction processing, and calculate frequency deviation and frequency change rate. The multimodal feature identification and classification module is used to identify the multimodal features of power grid frequency fluctuations based on the frequency deviation and frequency change rate, and classify the fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises. The frequency regulation coefficient dynamic adjustment and command generation module is used to dynamically adjust the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generate the corresponding active power regulation command, and achieve smooth transition between different frequency regulation modes through a weighted fusion strategy. The thermal dynamic model prediction and optimization module is used to combine the thermal dynamic model of the electric boiler and the preset heating temperature constraint range to perform model prediction control optimization on the active power adjustment command and generate a smooth power command sequence that meets the thermal inertia constraint. The power command execution module is used to send the first power value in the smooth power command sequence through a power electronic switching device to achieve continuous adjustment of the electric boiler power and grid frequency support.

[0015] The method and apparatus of this invention can adaptively adjust the control strategy according to the multimodal characteristics of power grid frequency fluctuations, realize rapid and smooth adjustment of electric boiler power, and at the same time optimize the heating quality through thermal dynamic model optimization, effectively improving frequency support capability and user comfort.

[0016] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing an adaptive frequency regulation control method for an electric boiler considering power grid frequency fluctuations as described in the first aspect embodiment.

[0017] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an adaptive frequency regulation control method for an electric boiler considering grid frequency fluctuations as described in the first aspect embodiment.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] Figure 1 This is a flowchart of an adaptive frequency regulation control method for an electric boiler that takes into account power grid frequency fluctuations according to an embodiment of the present invention; Figure 2 This is a structural diagram of the control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall structure of the adaptive frequency regulation control system for an electric boiler according to an embodiment of the present invention; Figure 4 This is a logic judgment diagram for frequency fluctuation multimodal recognition according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the power command optimization principle based on model predictive control according to an embodiment of the present invention; Figure 6 This is a structural diagram of an adaptive frequency regulation control device for an electric boiler that takes into account power grid frequency fluctuations according to an embodiment of the present invention; Figure 7 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The following description, with reference to the accompanying drawings, describes an adaptive frequency regulation control method and apparatus for an electric boiler that takes into account power grid frequency fluctuations, according to an embodiment of the present invention.

[0023] Example 1 Figure 1 This is a flowchart of an adaptive frequency regulation control method for an electric boiler considering power grid frequency fluctuations according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: S1 collects power grid frequency data in real time and performs filtering and noise reduction processing, and calculates frequency deviation and frequency change rate.

[0024] Specifically, this invention uses a high-precision frequency measurement device (such as a PMU or smart meter) to measure the power grid frequency signal. Real-time acquisition is performed, with sampling frequencies typically set to 1000Hz or higher to ensure accurate capture of instantaneous frequency changes. The acquired raw frequency data often contains high-frequency noise, such as quantization errors from measuring equipment or harmonic interference caused by nonlinear loads in the power grid, thus requiring filtering and noise reduction. In some implementations, first-order or second-order low-pass digital filters can be used, with cutoff frequencies typically set to 2Hz to preserve the dynamic characteristics of frequency changes while effectively suppressing high-frequency noise. The filtering algorithm can be based on FIR or IIR structures, with FIR filters preferred to ensure linear phase response and avoid introducing additional phase distortion.

[0025] Furthermore, the system calculates the frequency deviation. ,in The rated frequency of the power grid, usually 1000 MHz Frequency deviation reflects the degree of deviation between the current frequency and the standard frequency, and is a fundamental indicator for judging the operating status of the power grid. Furthermore, the system also calculates the rate of frequency change. ROCOF is used to assess the rate of frequency fluctuations, thereby identifying the urgency of frequency events. ROCOF calculations typically employ differential methods, such as first-order backward difference or central difference, with a sampling interval of [missing information]. This ensures a rapid response to frequency changes.

[0026] Furthermore, it provides high-quality input data for subsequent frequency mode identification and adaptive control strategy generation. Through accurate frequency deviation and rate of change calculation, the system can effectively distinguish between normal fluctuations and abnormal events, thereby achieving intelligent perception of the power grid status and laying the foundation for precise frequency regulation control of electric boilers.

[0027] Furthermore, S1 includes: S11, a low-pass digital filter is used to filter and denoise the original frequency data. The cutoff frequency of the low-pass filter is set to be less than 5% of the fundamental frequency of the power grid.

[0028] Specifically, using a low-pass digital filter to filter and denoise the raw frequency data is a fundamental step in realizing adaptive frequency control of electric boilers. This step uses digital signal processing technology to remove frequency signal jitter caused by measurement noise, high-frequency interference, or sampling errors, thereby improving the accuracy and stability of subsequent frequency deviation and rate of change calculations.

[0029] Furthermore, low-pass digital filters can employ FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) structures, the specific choice depending on the system's requirements for phase distortion and computational complexity. FIR filters exhibit linear phase characteristics, making them suitable for scenarios sensitive to signal phase; while IIR filters require less computation for the same performance, making them suitable for embedded systems or industrial controllers with high real-time requirements. The filter design must ensure that the cutoff frequency is below 5% of the fundamental frequency of the power grid, i.e., for a rated frequency of... For the power grid, the cutoff frequency should be set to This setting effectively preserves the low-frequency components of frequency variations while filtering out high-frequency noise, such as transient disturbances caused by PMU sampling errors or sudden changes in local load.

[0030] Furthermore, the sampling frequency of the filter The frequency is typically set above 1000Hz to satisfy the Nyquist sampling theorem and ensure complete capture of the frequency signal. The filter order... Typically, the filter order is between 4th and 8th order to achieve a balance between computational efficiency and filtering performance. In practical applications, the filter parameters can be designed and simulated using MATLAB or Python's SciPy library to ensure that its amplitude-frequency response curve is within acceptable limits. It exhibits good attenuation characteristics in the vicinity.

[0031] Furthermore, since even minute fluctuations in the power grid frequency signal (e.g., ±0.05Hz) can trigger frequency modulation response, if noise in the raw data is not effectively filtered out, it will lead to misjudgments and unnecessary power regulation actions, affecting system stability and equipment lifespan. Through low-pass filtering, the system can obtain a smooth and reliable frequency signal, which is crucial for subsequent frequency deviation analysis. and frequency change rate The computation provides reliable input, thereby supporting the accurate execution of multimodal recognition and adaptive control strategies.

[0032] S12, based on formula Calculate the frequency deviation and use the formula Calculate the rate of change of frequency.

[0033] Specifically, frequency deviation With the rate of change of frequency This step involves acquiring the power grid frequency signal in real time using a high-precision frequency measurement device (such as a PMU or smart meter). and its rated frequency (usually) The difference is calculated to obtain the frequency deviation. This deviation reflects the degree to which the power grid frequency deviates from its normal operating value and is the basis for judging the type and severity of frequency events.

[0034] Furthermore, the rate of change of frequency The calculation is performed by analyzing the filtered frequency signal. Perform time differentiation, i.e. This is used to characterize the rate of frequency change. In practical implementations, since frequency signals are discrete sampled data, the differentiation operation is usually implemented using the finite difference method, such as the first-order backward difference method. ,in The sampling interval is typically set to 10ms to 100ms to balance real-time performance and stability.

[0035] Furthermore, to improve the signal-to-noise ratio of frequency signals, calculations can be performed... and Previously, the raw frequency data was low-pass filtered to remove high-frequency noise and retain the main frequency fluctuation characteristics. The cutoff frequency of the filter is usually set to 0.1Hz to 0.5Hz to adapt to the typical time scale of power grid frequency changes.

[0036] Furthermore, frequency deviation and rate of change, as input features of the multimodal recognition algorithm, directly affect the switching and fusion of subsequent control strategies. By accurately calculating these two indicators, the system can perceive the dynamic characteristics of the power grid frequency in real time, providing reliable data support for subsequent primary frequency regulation, secondary frequency regulation, and model predictive control. This enables rapid, smooth, and precise load regulation, improving the stability of the power grid frequency and the economy and comfort of electric boiler operation.

[0037] S2, based on the frequency deviation and frequency change rate, identify the multimodal characteristics of power grid frequency fluctuations, and classify fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises.

[0038] Specifically, based on frequency deviation and rate of change of frequency The multimodal identification and classification step analyzes the dynamic characteristics of the power grid frequency in real time and classifies frequency fluctuation events into four typical modes: small disturbance steady-state fluctuation, large disturbance primary frequency regulation process, secondary frequency regulation recovery process, and frequency deterioration crisis, thereby providing a basis for the adaptive switching of subsequent control strategies.

[0039] Furthermore, this identification process relies on real-time processing and feature extraction of the frequency signal. First, the frequency signal is acquired using a high-precision frequency measurement device (such as a PMU). After filtering and noise reduction processing to eliminate high-frequency noise interference, ensure The accuracy, among which Typically 50Hz. Then, the rate of frequency change is calculated. These two parameters are used to characterize the rate and trend of frequency change. Together, they constitute the characteristic vector of frequency fluctuation. , as input for modality classification.

[0040] Furthermore, modal classification relies on a set multi-level threshold. For example, small-disturbance steady-state fluctuations are typically defined as... and It is very small and suitable for scenarios with slight load fluctuations; a large disturbance in a single frequency regulation process manifests as... and A significant increase is usually caused by generator tripping or heavy load switching; the characteristic of the secondary frequency regulation recovery process is that the frequency stabilizes in a steady state deviating from the rated value after the primary frequency regulation, such as... Stable at Nearby, further adjustments by AGC are needed; a sharp deterioration in frequency indicates a crisis. Extremely high, and A rapid increase indicates that the system is facing a severe power deficit and requires an urgent response.

[0041] Furthermore, this step is widely applicable to power grids with a high proportion of renewable energy integration, especially in operating environments with frequent frequency fluctuations and diverse disturbance types. Through multimodal identification, the system can distinguish different types of frequency events, thereby avoiding frequent actions under normal fluctuations and responding quickly during frequency crises, improving the reliability and effectiveness of frequency regulation.

[0042] Furthermore, this step significantly enhances the intelligence level of frequency regulation control in electric boilers. By introducing rule-based or lightweight machine learning (such as decision trees and support vector machines) recognition algorithms, the system can achieve rapid and accurate classification of frequency events, providing data support for the adaptive adjustment of subsequent control strategies. In addition, this recognition mechanism helps decouple power regulation from user heating quality, ensuring that user experience is not sacrificed while providing frequency regulation services, thereby enhancing the availability and responsiveness of load-side frequency regulation resources.

[0043] Furthermore, S2 includes: S21 defines small-disturbance steady-state fluctuations as and .

[0044] Specifically, the adaptive identification and classification of frequency fluctuation modes is based on real-time acquisition and preprocessing of frequency data. By classifying power grid frequency fluctuation events through a multimodal recognition algorithm, a basis can be provided for the adaptive selection of subsequent control strategies.

[0045] Furthermore, mode I is defined as a small-disturbance steady-state fluctuation, characterized by: and This mode is typically caused by slow changes in grid load or small-scale disturbances. The frequency deviation is small and the rate of change is gradual, indicating that the system is in a relatively stable state and no immediate frequency adjustment is required. In this situation, the electric boiler can maintain its base power operation or only make minor power adjustments to maintain system balance.

[0046] Furthermore, modality recognition can employ rule-based logical judgments or lightweight machine learning algorithms (such as decision trees and support vector machines). Rule-based methods typically set multiple threshold levels, for example, the dead zone of a single frequency modulation is... The steady-state deviation of the second frequency modulation is The frequency degradation threshold is Through real-time comparison and Based on the numerical value, the system can quickly determine the modality to which the current frequency event belongs.

[0047] Furthermore, this step is widely applicable to grid environments with a high proportion of renewable energy sources. Because power sources such as wind and solar power are intermittent and fluctuating, grid frequencies are prone to small, persistent deviations. By identifying Mode I, the system can avoid unnecessary frequent power adjustments, thereby reducing mechanical wear on the electric boiler, improving operating efficiency, and minimizing the impact on the user's heating experience.

[0048] Furthermore, the technical value of this step lies in achieving closed-loop control of "precise sensing - intelligent response." By distinguishing different frequency fluctuation types, the system can avoid "over-adjustment" or "under-adjustment," ensuring that only the minimum necessary adjustments are made under small disturbances, thereby improving the economy and stability of control. At the same time, this identification mechanism provides input for the subsequent generation of multi-mode control strategies and is a core prerequisite for realizing the flexible frequency regulation capability of electric boilers.

[0049] S22 defines the crisis of rapidly deteriorating frequency as and Exceeding within 2 seconds .

[0050] Specifically, the determination of a crisis of rapid frequency deterioration is based on the rate of change of the power grid frequency. and frequency deviation The joint condition is specifically defined as: when and Exceeding within 2 seconds At that time, the system determined that the current power grid was in a state of rapid frequency deterioration.

[0051] Furthermore, The calculation is based on the preprocessed frequency signal. By calculating its instantaneous rate of change in real time This is typically achieved using a difference algorithm or a sliding window differentiation method. To avoid noise interference, the frequency signal needs to be low-pass filtered before the difference calculation; the cutoff frequency of the filter is generally set to... Within this range, high-frequency jitter is filtered out while preserving the frequency variation trend. Frequency deviation Then, by comparing the real-time frequency value with the rated frequency... The difference is calculated.

[0052] Furthermore, threshold This is based on the warning range for rapid frequency changes set in power grid frequency safety operation standards (such as IEEE 1547 or GB / T 30137), and The range of change within 2 seconds This reflects a severe power imbalance event that may occur in the system within a short period of time. This joint condition can effectively distinguish between normal frequency fluctuations and emergency frequency events, avoid false triggering, and improve the accuracy of system response.

[0053] Furthermore, this judgment logic is embedded in the data processing and modal recognition module of the central adaptive control processing unit. Through real-time sampling and calculation, combined with multimodal recognition algorithms (such as rule-based threshold judgment or lightweight machine learning models), intelligent diagnosis of the power grid frequency status is achieved. When the system identifies Mode IV (frequency deterioration crisis), it will immediately activate the primary frequency regulation control module and temporarily increase the primary frequency regulation coefficient K_{pr1} to enhance the power regulation capability of the electric boiler, thereby providing rapid frequency support to the power grid.

[0054] Furthermore, by setting strict frequency change rate and deviation thresholds, the system can respond in the early stages of rapid frequency deterioration, preventing the frequency from deviating further from the safe operating range. Simultaneously, this judgment mechanism provides a clear basis for switching subsequent control strategies, ensuring that the electric boiler can participate in frequency regulation in the optimal manner under different frequency events, achieving a dual guarantee of rapid response and user heating quality.

[0055] S3 dynamically adjusts the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generates the corresponding active power regulation command, and achieves a smooth transition between different frequency regulation modes through a weighted fusion strategy.

[0056] Specifically, based on the grid frequency fluctuation mode identified in step S200, the system dynamically adjusts the primary frequency regulation coefficient K_{pr1} and the secondary frequency regulation integral coefficient K_{sec}, and generates corresponding active power regulation commands. A weighted fusion strategy is used to achieve a smooth transition between different frequency modulation modes.

[0057] Furthermore, in the primary frequency control module, the system employs a dynamic proportional control strategy with dead time. When the frequency deviation... At this time, the electric boiler does not participate in frequency regulation to avoid unnecessary power disturbances. At that time, the system dynamically adjusts the primary frequency regulation coefficient K_{pr1} according to the current mode and calculates the power regulation amount. The adjustment of K_{pr1} is based on factors including the severity of frequency fluctuations, the current available adjustable capacity of the electric boiler, and the system's response speed requirements. For example, in the event of a sharp frequency deterioration (Mode IV), K_{pr1} can be temporarily increased to its baseline value. It is 1.5 times that of the previous one, to enhance frequency modulation support capabilities.

[0058] Furthermore, the secondary frequency regulation control module follows the AGC instructions issued by the power grid dispatch center. Or local points control strategy Power regulation commands are generated. During the frequency recovery phase (Mode III), the system prioritizes the use of AGC commands to achieve gradual recovery of the steady-state frequency. Simultaneously, the value of K_{sec} can be dynamically adjusted based on the frequency change trend to avoid conflicts with primary frequency modulation commands.

[0059] Furthermore, in Mode II, the system primarily uses primary frequency modulation. In Mode III, second-order frequency modulation is dominant. During mode I or mode switching, the system employs a weighted fusion strategy: , in and These are dynamic weighting coefficients, based on frequency deviation. The ROCOF value is calculated in real time to ensure the continuity and stability of control commands.

[0060] Furthermore, this approach is applicable to frequency regulation scenarios with a high proportion of renewable energy connected to the grid, especially under conditions of frequent frequency fluctuations and significant changes in amplitude and rate, significantly improving the response accuracy and adaptability of electric boilers. By dynamically adjusting control parameters, the system can effectively avoid over- or under-adjustment, reduce sudden changes in power commands, thereby reducing secondary impacts on the power grid and improving the economy and safety of electric boiler operation. In addition, this strategy provides basic commands for subsequent model predictive control (MPC), a crucial prerequisite for achieving "thermal-electric decoupling" control.

[0061] Furthermore, S3 includes: S31, when identified as mode IV, the primary frequency modulation coefficient is... Temporarily adjusted to ,in This is the preset base value.

[0062] Specifically, when the system identifies a Mode IV (frequency deterioration crisis), the primary frequency modulation coefficient K_{pr1} is temporarily adjusted to... Where K_{pr1_{base}} is a preset base value. This step is a key control mechanism to enable electric boilers to respond quickly and provide stronger frequency support capabilities when the power grid frequency fluctuates significantly.

[0063] Furthermore, this adjustment strategy is based on frequency deviation. and rate of change of frequency The system monitors the real-time results. When the modal recognition module determines that the current frequency event belongs to Mode IV, i.e., the frequency change rate is extremely high and the frequency deviation is increasing rapidly, the system will trigger the parameter adaptive adjustment mechanism of the primary frequency modulation control module. Specifically, the primary frequency modulation control module originally uses a fixed proportional control strategy, and its adjustment amount is... Based on this, the present invention introduces a dynamic gain adjustment mechanism, by... Increase to This enhances the electric boiler's response to frequency changes, thereby providing greater frequency regulation power support in a short period of time.

[0064] Furthermore, The settings need to comprehensively consider the rated power of the electric boiler, its thermal inertia time constant, and the grid frequency regulation requirements. For example, in the embodiment, Set as Corresponding to the first frequency modulation dead zone When entering mode IV, this coefficient is temporarily amplified to This allows the power regulation of the electric boiler to increase by 50% under the same frequency deviation, thus more effectively suppressing further frequency deterioration.

[0065] Furthermore, this adjustment mechanism is applicable to emergency situations where the power grid encounters sudden large power shortages and rapid frequency drops. For example, when a large generating unit trips or the output of new energy sources drops sharply, the system frequency may drop by more than [amount missing] within seconds. At this time, mode IV is triggered, and the electric boiler increases... It rapidly reduces its own power, providing upward frequency regulation capability to the power grid and assisting in frequency recovery. This mechanism can be used in conjunction with thyristor power regulators or solid-state relays (SSRs) to achieve millisecond-level power regulation response.

[0066] Furthermore, this step significantly improves the electric boiler's response capability under frequency crises. By dynamically amplifying the primary frequency regulation gain, the system can provide stronger power regulation capabilities when the frequency deteriorates rapidly, effectively curbing further frequency drops and preventing frequency instability or even collapse. Simultaneously, since this adjustment is triggered only in Mode IV, it avoids over-response during normal operation or minor fluctuations, thus balancing control accuracy and system stability. This mechanism demonstrates the innovative value of this invention in terms of frequency regulation response speed and control strategy adaptability.

[0067] S32, through formula To achieve weighted fusion, among which and Calculated dynamically based on the ratio of frequency deviation to rate of change.

[0068] Specifically, in step S300, the control mode is fused, which is achieved through the formula:

[0069] The weighted fusion of primary and secondary frequency modulation control signals enables smooth switching and coordinated response of control strategies in Mode I or transient states.

[0070] Furthermore, the v weight coefficient and It is not a fixed value, but rather based on the current frequency deviation. With the rate of change of frequency The ratio is dynamically adjusted. Specifically, this ratio reflects the relationship between the instantaneous severity of frequency fluctuations and their sustained trend, thus providing a quantitative basis for switching control strategies. For example, when the frequency deviation is small but the rate of change is high, the system tends to increase the response weight of primary frequency modulation to quickly suppress further frequency deterioration; while when the frequency deviation is large but the rate of change tends to stabilize, the weight of secondary frequency modulation is increased to gradually restore the frequency to the rated value.

[0071] Furthermore, and The value range of is usually limited to [0,1], and satisfies . This ensures the continuity and stability of control commands. In practical engineering, this ratio can be processed through linear interpolation or nonlinear mapping functions, such as using the Sigmoid function or piecewise linear functions, to map the ratio of frequency deviation to rate of change into weighting coefficients, thereby achieving continuous transition of the control strategy.

[0072] Furthermore, it is applicable to transitional phases where the grid frequency fluctuates slightly or switches modes. For example, when there is a slight frequency shift caused by fluctuations in renewable energy output, the system can simultaneously consider the rapid response of primary frequency regulation and the steady-state adjustment of secondary frequency regulation, avoiding abrupt changes in control commands, reducing the impact on the power regulation of electric boilers, and improving the smoothness and stability of frequency regulation response.

[0073] Furthermore, this weighted fusion mechanism effectively solves the "hard switching" problem of traditional control strategies during mode switching, improving the adaptability of electric boilers in complex frequency fluctuation scenarios. Through dynamic adjustment... and The system can ensure frequency regulation performance while taking into account the thermal inertia characteristics of the electric boiler and the heating quality on the user side, achieving the optimal balance between "thermal-electric decoupling" and "frequency regulation response".

[0074] S4. Combining the thermal dynamic model of the electric boiler and the preset heating temperature constraint range, the active power adjustment command is optimized by model predictive control to generate a smooth power command sequence that satisfies thermal inertia constraints.

[0075] Specifically, the system combines the thermal dynamic model of the electric boiler with a preset heating temperature constraint range to adjust the active power command. Model predictive control optimization is performed to generate a smooth power command sequence that satisfies thermal inertia constraints. This step is crucial for ensuring that electric boilers can balance response speed and heating quality when participating in grid frequency regulation.

[0076] Furthermore, the thermal dynamic model of the electric boiler, based on its thermal balance characteristics, adopts a first-order inertial element for modeling, and its mathematical expression is:

[0077] in, This indicates the system's heat capacity (unit: kJ / °C). The electrothermal conversion efficiency is typically 0.95 to 0.98. This refers to the input electrical power (unit: kW). This represents the heat loss function, which is typically proportional to the temperature difference. This model is used to predict the future temperature trend of an electric boiler over a given power command sequence.

[0078] Furthermore, the system sets a hard constraint range for the user-side heating temperature as follows: ,For example To ensure that heating quality is not affected, the MPC optimization module employs a rolling optimization strategy. Its prediction time domain is typically set to 15-30 minutes, the control time domain to 5-10 minutes, and the sampling period to 1-5 seconds. The optimization objective function aims to minimize the power command tracking error while penalizing temperature out-of-bounds errors; its form can be expressed as:

[0079] in, To predict the number of steps, and These are the weighting coefficients for power error and temperature deviation, respectively. To predict temperature, For reference temperature (e.g.) ).

[0080] Furthermore, this step applies to power regulation scenarios involving electric boilers participating in primary frequency regulation, secondary frequency regulation, or frequency restoration processes in the power grid. Through MPC optimization, the system can generate a power command sequence that satisfies frequency regulation requirements without violating heating temperature constraints, based on the current temperature state and future predictions, when the grid frequency changes rapidly. For example, in a frequency drop event, the system can predict whether the electric boiler's temperature will exceed the limit after power reduction. This avoids a decline in heating quality due to insufficient thermal inertia.

[0081] Furthermore, this step effectively achieves "thermal-electric decoupling" control, meaning that while meeting the grid's frequency regulation requirements, it also ensures the stability of the heating temperature on the user side. By introducing a thermal dynamics model and constraint handling mechanism, the system can avoid sudden changes in power commands, reduce mechanical losses caused by frequent equipment start-ups and shutdowns, and improve the smoothness and robustness of control. In addition, this optimization process supports the electric boiler to adaptively adjust its power response under different operating conditions, enhancing its frequency regulation adaptability and practicality in high-proportion renewable energy grid integration.

[0082] Furthermore, S4 includes: S41, based on formula Predict the temperature trajectory over the next 20 minutes.

[0083] Specifically, based on the formula Predicting the temperature trajectory over the next 20 minutes involves constructing a thermal dynamic model of the electric boiler and combining it with the current operating status and control commands to make rolling predictions of temperature change trends, thus providing a foundation for subsequent model predictive control (MPC).

[0084] Furthermore, this thermal dynamics model treats the electric boiler as a system with thermal inertia, its core principle based on the theories of energy conservation and heat conduction. In the formula, This indicates the system's heat capacity (unit: kJ / ℃). Temperature of the thermal storage body (unit: °C). The electrothermal conversion efficiency is typically 0.95 to 0.98. This refers to the input electrical power (unit: kW). This represents the heat loss power, whose magnitude has a non-linear relationship with temperature, and can usually be modeled as... ,in This is the heat loss coefficient (unit: kW / ℃). Ambient temperature (unit: °C).

[0085] Furthermore, the system first collects the current temperature data of the electric boiler. And in conjunction with the power adjustment command generated in step S300 Calculate the actual input power. ,in The rated power of the electric boiler is set (e.g., 2MW). Then, numerical integration methods (such as the Euler method or the Runge-Kutta method) are used to solve the heat balance equation, predicting the temperature change trajectory over the next 20 minutes. The choice of the prediction time domain needs to consider both the timeliness of the frequency regulation response and the influence of thermal inertia, and is usually set to 15-30 minutes to ensure that the grid regulation requirements are met without violating the user's temperature comfort constraints.

[0086] Furthermore, heat capacity Calculations are typically based on the heat storage medium (such as water or phase change material) and its mass in the electric boiler. For example, if the heat storage medium is 10 tons of water, then... Heat loss coefficient This can be obtained through on-site identification or simulation modeling, and is generally within the range of 10~50kW / ℃. Temperature constraint range. Usually set to This is to ensure that the heating quality for users is not affected.

[0087] Furthermore, it is particularly suitable for power grid environments with a high proportion of renewable energy integration, where electric boilers participate as flexible loads in primary and secondary frequency regulation. By predicting temperature changes in advance, the system can optimize before power regulation commands are issued, avoiding drastic temperature fluctuations caused by sudden power changes, thereby improving the smoothness and controllability of frequency regulation response.

[0088] Furthermore, this step achieves synergistic optimization of "power regulation" and "temperature control," which is a core means of ensuring a balance between user heating quality and grid frequency stability. Through model prediction, the system can ensure that the temperature remains within an acceptable range for users while meeting grid regulation requirements, thereby improving the feasibility and user acceptance of electric boilers participating in frequency regulation.

[0089] S42, constrain the heating temperature range Set as And the risk of temperature exceeding the limit is minimized through a penalty function.

[0090] Specifically, the heating temperature constraint range Set as Minimizing the risk of temperature exceeding limits through a penalty function is a crucial step in ensuring heating quality when electric boilers participate in grid frequency regulation. This step, based on the thermal dynamic model of the electric boiler, employs model predictive control to optimize power regulation commands, ensuring that while meeting grid frequency regulation requirements, the heating temperature constraints on the user side are not violated.

[0091] Furthermore, firstly, the system sets a hard temperature constraint range based on actual heating demand. This range reflects the minimum and maximum tolerable temperatures for users' heating comfort. The thermal dynamic model of the electric boiler is a first-order inertial system, and its heat balance equation is:

[0092] in, Heat capacity (unit: kJ / °C) The electrothermal conversion efficiency is typically 0.95 to 0.98. This refers to the input electrical power of the electric boiler (unit: kW). This is a heat loss function, typically related to ambient temperature and the surface area of ​​the heat storage body, and can be modeled as a linear or nonlinear function.

[0093] Furthermore, during the MPC optimization process, the system uses the current temperature... As an initial state, and based on the aforementioned thermal dynamics model, the power command sequence to be executed is predicted within a finite time domain (e.g., 15-30 minutes). The subsequent temperature change trajectory. The optimization objective function typically includes two parts: first, minimizing the deviation between the power command and the desired command; and second, suppressing temperature overshooting behavior through a penalty function. The penalty term can be expressed as:

[0094] in, To predict the number of control steps in the time domain, and The penalty coefficient is used to adjust the penalty intensity for exceeding the lower and upper limits of the temperature range. By solving this optimization problem, the system can output a set of optimal power command sequences. The first control step instruction will be sent to the power actuator.

[0095] Furthermore, this approach is widely applicable to power grids with a high proportion of renewable energy integration, where electric boilers participate in primary and secondary frequency regulation as flexible loads. By setting reasonable temperature constraint ranges and introducing penalty mechanisms, the system can avoid drastic temperature changes caused by rapid power adjustments during grid frequency fluctuations, thereby improving user comfort and equipment operational stability.

[0096] Furthermore, through the rolling optimization mechanism of MPC, the system can ensure that the heating temperature is always within the acceptable range for users while meeting the frequency regulation requirements of the power grid, thus achieving the control objective of "thermal-electric decoupling" and improving the reliability and adaptability of electric boiler frequency regulation.

[0097] S5, the first power value in the smooth power command sequence is sent out and executed through a power electronic switching device to realize continuous adjustment of the electric boiler power and grid frequency support.

[0098] Specifically, smooth the power command sequence The first power value is sent to the power electronic switching device of the electric boiler, realizing continuous power regulation and grid frequency support. This step is the execution terminal of the entire control method, and its technical implementation directly determines the accuracy, speed, and stability of the frequency regulation response.

[0099] Furthermore, the power electronic switching devices can employ thyristor power regulators or solid-state relays (SSRs). Thyristor power regulators, due to their phase angle control or cycle control capabilities, can achieve millisecond-level power regulation accuracy, making them suitable for scenarios with high requirements for frequency modulation response speed and continuity. Specifically, when the central controller outputs a power command... Subsequently, the command is transmitted to the power modulation unit via the CAN bus or Ethernet interface. The modulation unit adjusts the conduction angle of the thyristor in real time according to the command value, thereby controlling the heating power of the electric boiler. For example, in an electric boiler with a rated power of 2MW, if the current command is 1.6MW, the power regulator will adjust the conduction period of the AC voltage to make the actual output power continuously change within a 0.1% accuracy range, ensuring the smoothness of power regulation.

[0100] Furthermore, the power actuator must possess rapid response capabilities, typically with a control cycle within 10ms, to meet the response time requirements of primary frequency regulation in the power grid (generally requiring 90% power adjustment to be completed within 2-5 seconds). Simultaneously, the drive and protection circuits must integrate overcurrent, overvoltage, and overheat protection mechanisms to ensure safe operation of the equipment under extreme conditions. For example, when the output current is detected to exceed 120% of the rated value or the temperature sensor feedback value exceeds the set range... When this happens, the system will automatically limit power output or trigger a protective shutdown.

[0101] Furthermore, in practical applications, this step is typically deployed within the local control cabinet of the electric boiler, interacting with the remote monitoring platform via industrial communication protocols (such as Modbus TCP and MQTT). In power grids with a high proportion of renewable energy integration, this step ensures the rapid response capability of the electric boiler as a flexible load, enabling it to adjust power output promptly when grid frequency deviations occur, providing dynamic frequency support to the grid, thereby improving the system's frequency stability and operational reliability.

[0102] The multi-modal adaptive frequency regulation control method for electric boilers in this invention can adaptively adjust the control strategy according to the dynamic characteristics of power grid frequency fluctuations, thereby achieving rapid, smooth, and precise adjustment of the electric boiler power, while ensuring that the heating temperature is within the acceptable range for users, and improving frequency stability and system reliability.

[0103] Example 2 To achieve the aforementioned objectives, this invention proposes another adaptive frequency regulation control method for electric boilers that considers grid frequency fluctuations. The core idea of ​​this method is to monitor the grid frequency in real time, determine the type and severity of frequency events using a multimodal recognition algorithm, and then adaptively select or fuse primary and secondary frequency regulation control modes. Based on the electric boiler's thermal dynamics model and heating demand constraints, the power regulation command is dynamically optimized, and finally precisely executed through power electronic switching devices. This method is as follows... Figure 2 The specific steps shown are as follows: Step S100, real-time acquisition and preprocessing of power grid frequency data includes: continuously acquiring frequency signals from the power grid point of common coupling using a high-precision frequency measurement device (such as a PMU or smart meter). The acquired raw frequency data undergoes preprocessing, including: denoising and filtering: using digital filters (such as low-pass filters) to remove high-frequency measurement noise. Frequency deviation is calculated. ,in The rated frequency (e.g., 50Hz). Calculate the rate of frequency change: It is used to assess the drasticness of frequency changes.

[0104] Step S200, adaptive identification and classification of frequency fluctuation modes, as follows: Figure 4 The data shown includes: based on preprocessed frequency data ( , Design a multimodal identifier to classify power grid frequency fluctuation events into the following typical modes: Mode I: Small disturbance steady-state fluctuations: It remains within a relatively small range (e.g., within ±0.05Hz). The value is very small. This is usually due to normal minor fluctuations in the load. Mode II: Large disturbance primary frequency regulation process: If the frequency dead zone (e.g., ±0.1Hz) is exceeded within a short period of time, and The value is relatively large. This is usually caused by events such as generator tripping or heavy load switching. Mode III: Secondary frequency regulation recovery process: After the primary frequency regulation operation, the frequency fails to recover to the rated value, but instead stabilizes in a new steady state that deviates from the rated value (e.g., (Stabilizing at 0.2Hz), secondary frequency modulation (AGC) intervention is required. Mode IV: Rapid frequency degradation crisis: Extremely high A rapid increase indicates a severe power deficit in the system, posing a risk of frequency collapse. The identification algorithm can employ a rule-based approach (setting...). and Multi-level thresholds can be used, and lightweight machine learning algorithms (such as decision trees and support vector machines) can be introduced for more refined pattern recognition.

[0105] Step S300, the multi-mode adaptive control strategy generation includes: adaptively selecting or weightedly fusing different control strategies based on the frequency modes identified in step S200, and generating a total active power regulation command. Primary frequency control module: Employs proportional control with dead time. Its basic form is: when... (During the first frequency modulation dead zone) .when hour, .in, This represents the primary frequency modulation factor (MW / Hz). Adaptive improvement: Instead of a fixed value, it is dynamically adjusted based on the frequency mode and the available adjustable capacity of the electric boiler. For example, under Mode IV (frequency crisis), the value can be temporarily increased. To provide stronger support. Secondary frequency regulation control module: Receives Automatic Generation Control (AGC) commands from the power grid dispatch center. Alternatively, steady-state frequency deviation can be eliminated through local integral control. or (When there is no AGC signal). Adaptive improvement: Integral coefficient of secondary frequency modulation. The response speed to AGC commands can be adjusted based on the required smoothness of frequency recovery. During periods of rapid frequency change, the weight of secondary frequency modulation is reduced to avoid interference with primary frequency modulation. Control mode fusion: When mode II is identified, primary frequency modulation control takes precedence. When identified as Mode III, secondary frequency modulation control is the primary method. When in Mode I or a transitional state, weighted fusion can be used: Among them, the weighting coefficient and Dynamic calculations based on frequency deviation and rate of change ensure a smooth control transition.

[0106] Step S400, power command optimization and constraint processing based on thermal dynamic model as follows: Figure 5 As shown, this includes: establishing a thermal dynamic model of the electric boiler: treating the electric boiler as a first-order inertial element, its heat balance equation is:

[0107] in, For heat capacity, This refers to the water temperature or the temperature of the heat storage body. For electrothermal efficiency, For electrical power, This represents heat loss. The model is used to predict temperature trends over a future period under a given power command. Heating constraints are set: upper and lower limits of acceptable temperature are defined for the user. This is a hard constraint that must be followed. Model predictive control (MPC) optimization includes: as generated in step S300... As the desired power change. At the current temperature. As the initial state, the thermal dynamics model is used to predict the execution within a finite time domain (e.g., 15-30 minutes). The temperature trajectory following the command sequence. The optimization problem is constructed as follows: the objective function is to minimize the error in tracking the power command while penalizing temperature out-of-bounds errors. By solving this optimization problem, a set of optimal, thermally constrained, smooth power command sequences is obtained. The first value in the sequence is taken as the final actual instruction to be executed.

[0108] Step S500, power modulation and execution includes: adjusting the optimized power command... The power control system is then sent to the electric boiler. For electric boilers heated by resistance wire, thyristor power regulators or solid-state relays (SSRs) are typically used for zero-crossing triggering or phase angle triggering to achieve continuous or stepped power regulation. The control cycle is extremely short (millisecond level), ensuring rapid frequency regulation response.

[0109] In one embodiment of the present invention, an adaptive frequency regulation control system for an electric boiler that implements the above method is proposed, such as... Figure 3As shown, the system is a hierarchical distributed structure, mainly including the following devices and units: Frequency monitoring and acquisition device: High-precision frequency sensor: Installed at the power grid connection point of the electric boiler for real-time measurement of the power grid frequency. Preferably, the device has PMU functionality, which can provide synchronous phasor data. Data acquisition unit (DAU): Performs analog-to-digital conversion and preliminary filtering on the frequency sensor signal. Central adaptive control processing device: Usually implemented by an industrial-grade PLC, embedded industrial computer, or dedicated controller, internally running the software of the control algorithm. It includes the following functional modules: Communication interface module: Responsible for data communication with the frequency monitoring device, power execution device, upper-level scheduling system (receiving AGC commands), and remote monitoring center. Data processing and modal recognition module: Executes steps S100 and S200 to complete the preprocessing and modal classification of frequency data. Adaptive control strategy calculation module: The core calculation unit, executes step S300 to calculate the initial power command based on the identified modes. Model predictive controller (MPC) module: Executes step S400, embeds the thermal dynamic model of the electric boiler, performs constrained optimization calculations, and outputs a safe and optimized power command. Human-Machine Interface (HMI): Used for local parameter setting, status monitoring, and manual intervention. Power Modulation and Actuation Device: Power Electronic Switching Unit: The "hands and feet" of the system. Depending on the capacity and control accuracy requirements of the electric boiler, the following can be selected: Solid State Relay (SSR): Used for on / off control or zero-crossing trigger power regulation; low cost, suitable for scenarios where control smoothness requirements are not high. Thyristor Power Regulator: Employs phase angle control or cycle control, enabling continuous and smooth power regulation with superior control performance. Drive and Protection Circuit: Provides drive signals for the power electronic switch and integrates overcurrent, overvoltage, and overheat protection functions. Temperature Monitoring Device: Temperature Sensor: Installed at the inlet, outlet, or key locations of the electric boiler's water inlet or heat storage body to monitor temperature values ​​in real time. This data is then fed back to the central control unit for updating the thermal model and verifying constraints. Remote monitoring and management platform: A software platform located in the cloud or dispatch center, used to centrally manage thousands of electric boilers participating in frequency regulation within a region, enabling functions such as cluster coordinated control, performance evaluation, and revenue settlement.

[0110] The technical principle of this invention lies in the deep integration of adaptive control theory, model predictive control (MPC), power system frequency regulation mechanism, and electric boiler thermodynamics. The adaptive and multimodal identification principle: By analyzing frequency deviation and its rate of change in real time, the system can act like a "smart doctor," "diagnosing" the "health condition" (frequency anomaly) of the power grid (modal identification), and then "prescribing the right medicine" (selecting a control strategy), avoiding the limitations of a single prescription and improving the intelligence and effectiveness of control. The model predictive control (MPC) principle: This is the core of achieving "thermal-electric decoupling." MPC is a model-based feedforward-feedback optimization control strategy. It utilizes an internal thermal dynamic model to proactively predict the impact of current control actions on the future system state (temperature). Through rolling optimization, it can track the power grid frequency regulation command as closely as possible while meeting temperature constraints. This means that the system can "intelligently" utilize the thermal inertia of the electric boiler to adjust power during frequency fluctuations, and as long as the temperature is restored to the comfortable range within the inertial time constant, it will not affect the heating experience of the end user.

[0111] The embodiments of this invention also have the following technical effects: Superior frequency regulation performance: Adaptive control makes the frequency regulation response of the electric boiler more accurate and smoother, reducing over-adjustment and oscillation, and providing better support for the power grid. Higher reliability: Multi-modal recognition capability enables the system to distinguish between normal fluctuations and severe faults, avoiding unnecessary frequent actions, extending equipment life, and providing strong support during real crises. Lossless user experience: Through MPC optimization, heating quality is fundamentally guaranteed, eliminating users' concerns about participating in frequency regulation, and facilitating the promotion of flexible load frequency regulation services. Enhanced adaptability: Adaptive parameters and models can adapt to changes in the power grid structure and the aging of the electric boiler itself, maintaining optimal long-term performance. Easy cluster application: The design concept of this system is easily expandable, providing a standardized interface for the power grid dispatch center, facilitating the aggregation and collaborative control of massive numbers of distributed electric boilers.

[0112] Example 3 To achieve the above embodiments, such as Figure 6 As shown, this embodiment also provides an adaptive frequency regulation control device 10 for electric boilers that considers power grid frequency fluctuations, including: The frequency data acquisition and processing module 100 is used to acquire power grid frequency data in real time, perform filtering and noise reduction processing, and calculate frequency deviation and frequency change rate. The multimodal feature recognition and classification module 200 is used to identify the multimodal features of power grid frequency fluctuations based on the frequency deviation and frequency change rate, and classify the fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises. The frequency regulation coefficient dynamic adjustment and instruction generation module 300 is used to dynamically adjust the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generate the corresponding active power regulation instruction, and achieve smooth transition between different frequency regulation modes through a weighted fusion strategy. The thermal dynamic model prediction and optimization module 400 is used to combine the thermal dynamic model of the electric boiler and the preset heating temperature constraint range to perform model prediction control optimization on the active power adjustment command and generate a smooth power command sequence that meets the thermal inertia constraint. The power command execution module 500 is used to send the first power value in the smooth power command sequence through a power electronic switching device to realize continuous adjustment of the power of the electric boiler and support of the power grid frequency.

[0113] Furthermore, the frequency data acquisition and processing module 100 is also used for: A low-pass digital filter is used to filter and denoise the original frequency data. The cutoff frequency of the low-pass filter is set to be less than 5% of the fundamental frequency of the power grid. Based on formula Calculate the frequency deviation and use the formula Calculate the rate of change of frequency.

[0114] Furthermore, the multimodal feature recognition and classification module 200 is also used for: Small disturbance steady-state fluctuations are defined as and ; Define the crisis of rapid frequency deterioration as and Exceeding within 2 seconds .

[0115] The device of this invention can adaptively adjust the control strategy according to the multimodal characteristics of power grid frequency fluctuations, realize rapid and smooth adjustment of electric boiler power, and at the same time optimize the heating quality through thermal dynamic model optimization, effectively improving frequency support capability and user comfort.

[0116] Example 4 To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 7 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the adaptive frequency regulation control method for electric boilers considering grid frequency fluctuations described above.

[0117] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an adaptive frequency regulation control method for an electric boiler considering grid frequency fluctuations as described in the foregoing embodiments.

[0118] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An adaptive frequency regulation control method for an electric boiler considering power grid frequency fluctuations, characterized in that, include: S1: Real-time acquisition of power grid frequency data and filtering and noise reduction processing, calculation of frequency deviation and frequency change rate; S2, based on the frequency deviation and frequency change rate, identify the multimodal characteristics of power grid frequency fluctuations, and classify fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises; S3 dynamically adjusts the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generates the corresponding active power regulation command, and achieves smooth transition between different frequency regulation modes through a weighted fusion strategy; S4, combining the thermal dynamic model of the electric boiler and the preset heating temperature constraint range, perform model predictive control optimization on the active power adjustment command to generate a smooth power command sequence that satisfies thermal inertia constraints. S5, the first power value in the smooth power command sequence is sent out and executed through a power electronic switching device to realize continuous adjustment of the electric boiler power and grid frequency support.

2. The method as described in claim 1, characterized in that, S1 includes: S11, a low-pass digital filter is used to filter and denoise the original frequency data. The cutoff frequency of the low-pass filter is set to be less than 5% of the fundamental frequency of the power grid. S12, based on formula Calculate the frequency deviation and use the formula Calculate the rate of change of frequency.

3. The method as described in claim 1, characterized in that, S2 includes: S21 defines small-disturbance steady-state fluctuations as and ; S22 defines the crisis of rapidly deteriorating frequency as and Exceeding within 2 seconds .

4. The method as described in claim 1, characterized in that, The S3 includes: S31, when identified as mode IV, the primary frequency modulation coefficient is... Temporarily adjusted to ,in This is the preset base value; S32, through formula To achieve weighted fusion, among which and Calculated dynamically based on the ratio of frequency deviation to rate of change.

5. The method as described in claim 1, characterized in that, The S4 includes: S41, based on formula Predict the temperature trajectory over the next 20 minutes; S42, constrain the heating temperature range Set as And the risk of temperature exceeding the limit is minimized through a penalty function.

6. An adaptive frequency regulation control device for an electric boiler that considers power grid frequency fluctuations, characterized in that, include: The frequency data acquisition and processing module is used to acquire power grid frequency data in real time, perform filtering and noise reduction processing, and calculate frequency deviation and frequency change rate. The multimodal feature identification and classification module is used to identify the multimodal features of power grid frequency fluctuations based on the frequency deviation and frequency change rate, and classify the fluctuation events into small disturbance steady-state fluctuations, large disturbance primary frequency regulation processes, secondary frequency regulation recovery processes, or frequency deterioration crises. The frequency regulation coefficient dynamic adjustment and command generation module is used to dynamically adjust the primary frequency regulation coefficient and the secondary frequency regulation integral coefficient according to the identified frequency fluctuation mode, generate the corresponding active power regulation command, and achieve smooth transition between different frequency regulation modes through a weighted fusion strategy. The thermal dynamic model prediction and optimization module is used to combine the thermal dynamic model of the electric boiler and the preset heating temperature constraint range to perform model prediction control optimization on the active power adjustment command and generate a smooth power command sequence that meets the thermal inertia constraint. The power command execution module is used to send the first power value in the smooth power command sequence through a power electronic switching device to achieve continuous adjustment of the electric boiler power and grid frequency support.

7. The apparatus as claimed in claim 6, characterized in that, The frequency data acquisition and processing module is also used for: A low-pass digital filter is used to filter and denoise the original frequency data. The cutoff frequency of the low-pass filter is set to be less than 5% of the fundamental frequency of the power grid. Based on formula Calculate the frequency deviation and use the formula Calculate the rate of change of frequency.

8. The apparatus as claimed in claim 6, characterized in that, The multimodal feature recognition and classification module is also used for: Small disturbance steady-state fluctuations are defined as and ; Define the crisis of rapid frequency deterioration as and Exceeding within 2 seconds .

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the adaptive frequency regulation control method for electric boilers considering power grid frequency fluctuations as described in any one of claims 1-5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements an adaptive frequency regulation control method for electric boilers that takes into account grid frequency fluctuations as described in any one of claims 1-5.

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