Smart energy unit device with load flexible adjustment function and load adjustment method

Abstract

The invention discloses a smart energy unit device with load flexible adjustment and a load adjustment method. The smart energy unit device comprises a main control module and a function module. The main control module comprises a core processing unit, a power supply module, a man-machine interaction module, a hardware encryption module and various communication interfaces; the core processing unit comprises a central processing unit and a memory and is responsible for executing load identification, intelligent adjustment and decision-making functions; the power supply module comprises a main power supply and a back-up power supply and provides power supply for the unit in a normal or power-off condition; the man-machine interaction module provides a display screen and a key interface for a user to configure and adjust; and the hardware encryption module is used for guaranteeing the security of data storage, transmission and interaction. According to the invention, the flexibility and accuracy of load adjustment are enhanced; and an intelligent algorithm and a machine learning mechanism enable the system to dynamically adapt to load fluctuation, and energy supply is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of smart energy, and particularly to a smart energy unit device with flexible load regulation and a load regulation method. Background Art

[0002] In the prior art, load regulation methods mostly rely on static rules or preset algorithm models, such as load management through simple load regulation curves or time - period - based regulation strategies. These methods mainly focus on energy conservation or peak - shaving and valley - filling, but often lack sufficient adaptability to complex power load fluctuations.

[0003] In addition, although modular design is widely used in the hardware field, in smart energy units, how to flexibly configure functional modules, adjust system parameters according to business scenarios, and achieve adaptive regulation under multiple business scenarios has not been fully solved.

[0004] Therefore, we propose a smart energy unit device with flexible load regulation and a load regulation method to solve the above - mentioned problems. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the problems existing in the prior smart energy unit device with flexible load regulation, the present invention is proposed.

[0007] Therefore, the purpose of the present invention is to provide a smart energy unit device with flexible load regulation and a load regulation method, which enhance the flexibility and accuracy of load regulation: intelligent algorithms and machine - learning mechanisms enable the system to dynamically adapt to load fluctuations and optimize energy supply.

[0008] To solve the above - mentioned technical problems, the present invention provides the following technical solution: A smart energy unit device with flexible load regulation includes a main control module and functional modules;

[0009] The main control module includes a core processing unit, a power supply module, a human - machine interaction module, a hardware encryption module, and multiple communication interfaces;

[0010] The core processing unit includes a central processing unit and a memory, and is responsible for executing load identification, intelligent regulation, and decision - making functions;

[0011] The power supply module includes a main power supply and a backup power supply, which provide power supply for the unit under normal or power - off conditions;

[0012] The human - machine interaction module provides a display screen and button interfaces for users to configure and adjust.

[0013] The hardware encryption module ensures the security of data storage, transmission, and interaction.

[0014] As a preferred solution of the intelligent energy unit device for load flexible adjustment described in the present invention, wherein: the functional modules adopt a modular design and can flexibly configure multiple interfaces according to different business scenarios, including remote communication interfaces, local communication interfaces, data acquisition interfaces, tele - signaling, tele - control, and tele - adjustment interfaces.

[0015] As a preferred solution of the intelligent energy unit device for load flexible adjustment described in the present invention, wherein: the intelligent energy unit device has a load prediction function, including the prediction of the user's total load curve and the user's adjustable load prediction:

[0016] For the prediction of the user's total load curve, through the differential analysis of the total load curve prediction result and the demand response baseline, a label indicating whether the user has response potential on the current day is formed to support the strategy configuration of the master station's user invitation and load control.

[0017] For the prediction of the user's adjustable load, through the user's adjustable load prediction data, based on the statistical data of the power change during the flexible load regulation of adjustable devices of the same scale, the predicted power of the user's response to the air - conditioning load is inferred to support the measurement of the user's demand response adjustment potential in the master station and the splitting of control indicators based on the user's adjustment potential.

[0018] As a preferred solution of the intelligent energy unit device for load flexible adjustment described in the present invention, wherein: the intelligent energy unit device executes demand response instructions on the monitored objects and feeds back the execution results, provides invitation, baseline load curve, prediction curve, execution curve, and demand response effect information display for users, and provides interactive operations for users to determine whether to participate in the response, including resource management, control execution, effect monitoring, and historical query:

[0019] A) Resource management: It should be able to manage the device name, the customer transformer to which it belongs, device type, rated power, adjustable power, and control mode of the adjustable devices connected to the intelligent energy unit through the intelligent energy unit interface or auxiliary interaction means.

[0020] B) Control execution: When the intelligent energy unit receives a demand response start event sent by the master station platform, it issues a control instruction within the adjustable capacity range of the devices participating in the demand response to adjust the operating power of the devices or suspend the operation of the devices.

[0021] C) Effect monitoring: On the display interface of the intelligent energy unit, the load baseline, target load curve, and actual load curve are displayed in the form of curves according to the control execution number, and the execution situation of the demand response is analyzed by comparison.

[0022] D) Historical query: Select the historical time period on the display interface of the intelligent energy unit, and query and statistics the situation of participating in demand response within the selected time period, including the participation time, response volume, load curve comparison, and effectiveness.

[0023] A load regulation method for an intelligent energy unit device with flexible load regulation includes the following steps:

[0024] Step 1: Identify and classify the connected loads through the core processing unit to determine the regulation requirements of different loads;

[0025] Step 2: According to the load type and its change trend, run an intelligent algorithm through the core processing unit to dynamically adjust the output of the energy unit;

[0026] Step 3: Automatically adjust according to the preset load regulation strategy, including peak-valley load regulation, emergency load response, and energy-saving mode.

[0027] As a preferred solution of the intelligent energy unit device with flexible load regulation according to the present invention, wherein: the intelligent algorithm run by the core processing unit in Step 2 includes a load prediction algorithm and a load regulation algorithm;

[0028] The load prediction algorithm includes:

[0029] 1) Input variables:

[0030] Historical load data: L t , representing the load value at the past time t;

[0031] Time feature: time period T t ;

[0032] Environmental data: E t , external factors affecting load demand;

[0033] 2) Load prediction formula:

[0034] Prediction model based on linear regression:

[0035] L t+1 =αL t +βT t +γE t +∈

[0036] Wherein:

[0037] L t+1 is the load prediction value for the next moment;

[0038] α, β, γ are regression coefficients obtained through training data;

[0039] ∈ is the error term;

[0040] The load regulation algorithm adopts a fuzzy control algorithm, including:

[0041] S1. Fuzzy control input and output:

[0042] Input variable: Load prediction error ΔL = L predict -L actual ;

[0043] Output variable: Regulation amount ΔP, representing the power to adjust the energy supply;

[0044] S2. Fuzzy rule setting:

[0045] If ΔL is positive and large, then the output ΔP is increased;

[0046] If ΔL is negative and small, then the output ΔP is decreased;

[0047] S3. By fuzzifying the input, map the input variable ΔL and the output variable ΔP to fuzzy sets, obtain the fuzzy value of the regulation amount through rule inference, and then perform defuzzification to obtain the final regulation result.

[0048] As a preferred scheme of the load regulation method of the intelligent energy unit device for load flexible regulation of the present invention, wherein: the load regulation strategy includes peak-valley load regulation:

[0049] Peak-valley load regulation includes peak-valley regulation execution:

[0050] During the peak load period, the system will take load reduction measures and preferentially reduce non-core loads;

[0051] During the valley load period, through the regulation strategy, the system allows renewable energy or low-cost energy to enter the system to achieve load charging / supplementation;

[0052] The regulation formula during the peak period is:

[0053] L adjusted = L current ·(1 - β peak )

[0054] Wherein: L adjusted is the regulated load;

[0055] L current is the current load;

[0056] β peak is the reduction coefficient, representing the proportion of load reduction during the peak period;

[0057] The regulation formula during the valley period is:

[0058] L adjusted = L current ·(1 + β valley )

[0059] Where: β valley is the increase coefficient, representing the proportion of the load increase during the low - valley period.

[0060] As a preferred solution of the load adjustment method of the intelligent energy unit device for load flexible adjustment described in the present invention, where: the load adjustment strategy further includes emergency load response:

[0061] Load prediction and real - time monitoring: The core processing unit quickly identifies abnormal load conditions based on real - time load data and historical trends;

[0062] When the load fluctuation exceeds the set threshold, when the load increase exceeds 5% or reaches 90% of the maximum load, an emergency load response is triggered. The system preferentially shuts down unnecessary loads, or switches non - core loads to the standby mode. The emergency response formula:

[0063] L adjusted = L current ·(1 - β emergency )

[0064] Where: L adjusted is the adjusted load;

[0065] L current is the current load;

[0066] β emergency is the emergency load response coefficient, which is dynamically adjusted according to the severity of the load fluctuation.

[0067] As a preferred solution of the load adjustment method of the intelligent energy unit device for load flexible adjustment described in the present invention, where: the load adjustment strategy further includes an energy - saving mode: On the premise of ensuring the core functions, the system preferentially shuts down high - energy - consuming non - core devices. During the low - power - demand period, the system charges the energy storage device, and releases the stored energy during the load peak to avoid overloading of the traditional power grid;

[0068] Energy - saving mode formula:

[0069] L adjusted = L current ·(1 - β emergy_saving )

[0070] Where: L adjusted is the adjusted load;

[0071] L current is the current load;

[0072] β emergy_saving is the energy-saving adjustment coefficient, which is dynamically adjusted according to the load status, power factor, and energy storage status.

[0073] Advantages of the present invention:

[0074] The present invention improves energy utilization efficiency: By means of flexible load regulation and multi-energy complementarity, the utilization efficiency of energy is improved, and energy waste is avoided.

[0075] Enhance the flexibility and accuracy of load regulation: The intelligent algorithm and machine learning mechanism enable the system to dynamically adapt to load fluctuations and optimize energy supply.

[0076] Improve system security: The hardware encryption module ensures the security of data storage, transmission, and interaction, and avoids potential security risks.

[0077] Adapt to different application scenarios: Through modular design, the system can be flexibly adjusted according to actual business needs and support multiple business scenarios. Description of the Drawings

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0079] Figure 1 is the structural block diagram of the intelligent energy unit device and load regulation method for load flexible regulation of the present invention. Detailed Embodiments

[0080] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0081] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0082] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0083] Next, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0084] Referring Figure 1 , a smart energy unit device with load flexible adjustment is provided, including a main control module and a function module;

[0085] The main control module includes a core processing unit, a power module, a human-machine interaction module, a hardware encryption module, and multiple communication interfaces;

[0086] The core processing unit includes a central processor and a memory, and is responsible for executing load identification, intelligent adjustment, and decision-making functions;

[0087] The power module includes a main power supply and a backup power supply, which provide power supply for the unit under normal or power-off conditions;

[0088] The human-machine interaction module provides a display screen and a key interface for users to configure and adjust;

[0089] The hardware encryption module ensures the security of data storage, transmission, and interaction.

[0090] As a preferred solution of the smart energy unit device with load flexible adjustment of the present invention, wherein: the function module adopts a modular design and can flexibly configure multiple interfaces according to different business scenarios, including remote communication interfaces, local communication interfaces, data acquisition interfaces, tele-signaling, tele-control, and tele-adjustment interfaces.

[0091] Among them, the smart energy unit device has a load prediction function, including user total load curve prediction and user adjustable load prediction:

[0092] For the user total load curve prediction, through the difference analysis between the total load curve prediction result and the demand response baseline, a label indicating whether the user has response potential on the current day is formed to support the strategy configuration of the main station user invitation and load control;

[0093] For the user adjustable load prediction, through the user adjustable load prediction data, based on the statistical data of the power change during the flexible load regulation of adjustable devices of the same scale, the predicted power of the user's response air-conditioning load is inferred to support the measurement of the user demand response adjustment potential of the main station and the splitting of the regulation index based on the user adjustment potential.

[0094] Among them, the intelligent energy unit device executes the demand response instruction on the monitored object and feeds back the execution result, provides invitation, reference load curve, prediction curve, execution curve, and demand response effect information display for users, and provides interactive operations for users to participate or not in the response, including resource management, control execution, effect monitoring, and historical query:

[0095] A) Resource management: It should be able to manage the device name, the customer transformer to which it belongs, the device type, the rated power, the adjustable power, and the control method of the adjustable devices connected to the intelligent energy unit through the intelligent energy unit interface or auxiliary interaction means;

[0096] B) Control execution: When the intelligent energy unit receives the demand response start event issued by the master station platform, it issues a control instruction within the range of the regulation capacity of the devices participating in the demand response to adjust the operating power of the device or suspend the operation of the device;

[0097] C) Effect monitoring: On the display interface of the intelligent energy unit, display the load baseline, target load curve, and actual load curve in the form of curves according to the control execution number, and compare and analyze the execution of the demand response;

[0098] D) Historical query: Select the historical time period on the display interface of the intelligent energy unit, and query and count the situation of participating in the demand response within the selected time period, including the participation time, response volume, load curve comparison, and effectiveness.

[0099] Furthermore, the adjustment method of the intelligent energy unit device for load flexible adjustment includes the following steps:

[0100] Step 1: Identify and classify the connected loads through the core processing unit to determine the adjustment requirements of different loads;

[0101] Step 2: According to the load type and its change trend, run an intelligent algorithm through the core processing unit to dynamically adjust the output of the energy unit;

[0102] Step 3: Perform automatic adjustment according to the preset load adjustment strategy, including peak-valley load adjustment, emergency load response, and energy-saving mode.

[0103] Among them, the intelligent algorithm run by the core processing unit in Step 2 includes a load prediction algorithm and a load adjustment algorithm;

[0104] The load prediction algorithm includes:

[0105] 1) Input variables:

[0106] Historical load data: L t , representing the load value at the past time t;

[0107] Time feature: time period Tt ;

[0108] Environmental data: E t , external factors affecting load demand;

[0109] 2) Load forecasting formula:

[0110] Forecasting model based on linear regression:

[0111] L t+1 = αL t + βT t + γE t + ∈

[0112] Where:

[0113] L t+1 is the load forecast value for the next moment;

[0114] α, β, γ are regression coefficients obtained through training data;

[0115] ∈ is the error term;

[0116] The load adjustment algorithm adopts a fuzzy control algorithm, including:

[0117] S1. Fuzzy control input and output:

[0118] Input variable: Load forecast error ΔL = L predict - L actual ;

[0119] Output variable: Adjustment amount ΔP, representing the power to adjust the energy supply;

[0120] S2. Fuzzy rule setting:

[0121] If ΔL is positive and large, then the output ΔP increases;

[0122] If ΔL is negative and small, then the output ΔP decreases;

[0123] S3. By fuzzifying the input, map the input variable ΔL and the output variable ΔP to fuzzy sets, obtain the fuzzy value of the adjustment amount through rule inference, and then perform defuzzification to obtain the final adjustment result.

[0124] Specifically, the load adjustment strategy includes peak-valley load adjustment:

[0125] Peak-valley load adjustment includes peak-valley adjustment execution:

[0126] During peak load periods, the system will take load reduction measures and preferentially reduce non-core loads;

[0127] During the low-load period, through the regulation strategy, the system allows renewable energy or low-cost energy to enter the system to achieve the charging / supplementation of the load.

[0128] The regulation formula during the peak period is:

[0129] L adjusted = L current ·(1 - β peak )

[0130] Where: L adjusted is the regulated load;

[0131] L current is the current load;

[0132] β peak is the reduction coefficient, indicating the proportion of load reduction during the peak period;

[0133] The regulation formula during the low-load period is:

[0134] L adjusted = L current ·(1 + β valley )

[0135] Where: β valley is the increase coefficient, indicating the proportion of load increase during the low-load period.

[0136] Furthermore, the load regulation strategy also includes emergency load response:

[0137] Load forecasting and real-time monitoring: The core processing unit quickly identifies abnormal load conditions based on real-time load data and historical trends;

[0138] When the load fluctuation exceeds the set threshold, when the load increase exceeds 5% or reaches 90% of the maximum load, an emergency load response is triggered. The system preferentially shuts down unnecessary loads or switches non-core loads to the standby mode. The emergency response formula:

[0139] L adjusted = L current ·(1 - β emergency )

[0140] Where: L adjusted is the regulated load;

[0141] L current is the current load;

[0142] β emergency is the emergency load response coefficient, which is dynamically adjusted according to the severity of the load fluctuation.

[0143] Among them, the load regulation strategy also includes an energy-saving mode: on the premise of ensuring the core functions, the system preferentially shuts down high-energy-consuming non-core devices. During the low valley of power demand, the system charges the energy storage device, and releases the stored energy during the peak load to avoid overloading of the traditional power grid;

[0144] Energy-saving mode formula:

[0145] L adjusted = L current ·(1 - β emergy_saving )

[0146] Where: L adjusted is the adjusted load;

[0147] L current is the current load;

[0148] β emergy_saving is the energy-saving adjustment coefficient, which is dynamically adjusted according to the load status, power factor and energy storage status.

[0149] Through the above three load regulation strategies (peak-valley load regulation, emergency load response and energy-saving mode), the intelligent energy unit can flexibly adjust under different power demand scenarios to ensure the efficient, safe and energy-saving operation of the power grid. Each regulation strategy has clear trigger conditions, adjustment processes and decision formulas, and realizes automatic adjustment through intelligent algorithms and real-time data.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A smart energy unit device with flexible load regulation, characterized in that: Including main control module and functional module; The main control module includes a core processing unit, a power module, a human-computer interaction module, a hardware encryption module and multiple communication interfaces; The core processing unit includes a central processor and memory, which is responsible for performing load identification, intelligent regulation and decision-making functions; The power module includes a main power supply and a backup power supply, which provide power to the unit in normal or power failure situations; The human-computer interaction module provides a display screen and key interface for users to configure and adjust; Hardware encryption module ensures the security of data storage, transmission and interaction.

2. The intelligent energy unit device with load flexible regulation according to claim 1 is characterized in that: The functional module adopts a modular design and can flexibly configure multiple interfaces according to different business scenarios, including a remote communication interface, a local communication interface, a data acquisition interface, a remote signaling interface, a remote control interface, and a remote adjustment interface.

3. The intelligent energy unit device with load flexible regulation according to claim 2 is characterized in that: The smart energy unit device has a load forecasting function, including user total load curve forecasting and user adjustable load forecasting: For the total load curve forecast of users, the difference analysis between the total load curve forecast result and the demand response baseline is used to form a label of whether the user has the response potential on that day, which supports the strategy configuration of user invitation and load control of the master station; For the user adjustable load forecast, through the user adjustable load forecast data, based on the statistical data of power changes during flexible load regulation of adjustable equipment of the same scale, the user response air-conditioning load forecast power is inferred, supporting the calculation of the user demand response regulation potential of the main station and the splitting of regulation indicators based on the user regulation potential.

4. The intelligent energy unit device with load flexible regulation according to claim 3 is characterized in that: The smart energy unit device executes the demand response instruction on the monitored object and feeds back the execution result, provides the user with invitation, benchmark load curve, forecast curve, execution curve, demand response effect information display, and provides the user with interactive operations to respond whether to participate, including resource management, control execution, effect monitoring, and historical query: A) Resource management: The device name, customer transformer, device type, rated power, adjustable power, and control method of the controllable devices connected to the smart energy unit should be managed through the smart energy unit interface or auxiliary interactive means; B) Control execution: The smart energy unit receives the demand response start event issued by the master station platform, initiates control instructions according to the control capability range of the participating demand response equipment, adjusts the equipment operating power or suspends the equipment operation; C) Effect monitoring: Based on the control execution number, the load baseline, target load curve and actual load curve are displayed in the form of curves on the display interface of the smart energy unit to compare and analyze the execution of demand response; D) Historical query: Select a historical time period on the smart energy unit display interface to query the statistics of participation in demand response within the selected time period, including participation time, response amount, load curve comparison, and whether it is effective.

5. A load regulation method for a smart energy unit device with flexible load regulation, characterized in that: The following steps are involved: Step 1: The core processing unit identifies and classifies the connected loads to determine the regulation requirements of different loads; Step 2: According to the load type and its changing trend, the core processing unit runs an intelligent algorithm to dynamically adjust the output of the energy unit; Step 3: Automatically adjust according to the preset load regulation strategy, including peak and valley load regulation, emergency load response, and energy-saving mode.

6. The load regulation method of the smart energy unit device with load flexible regulation according to claim 5 is characterized in that: In step 2, the core processing unit runs intelligent algorithms including load prediction algorithm and load regulation algorithm; The load forecasting algorithm includes: 1) Input variables: Historical load data: L t , represents the load value at time t in the past; Time characteristics: time period T t ; Environmental data: E t , external factors that affect load demand; 2) Load forecasting formula: Prediction model based on linear regression: L t+1 =αL t +βT t +γE t +∈ in: L t+1 is the load forecast value at the next moment; α, β, and γ are regression coefficients obtained through training data; ∈ is the error term; The load regulation algorithm adopts a fuzzy control algorithm, including: S1. Fuzzy control input and output: Input variables: Load forecast error ΔL = L predict -L actual ; Output variable: regulation amount ΔP, which represents the power of adjusting energy supply; S2. Fuzzy rule setting: If ΔL is positive and large, then the output ΔP is increasing; If ΔL is negative and small, then the output ΔP is decreasing; S3. By fuzzifying the input, the input variable ΔL and the output variable ΔP are mapped into fuzzy sets, and the fuzzy value of the adjustment amount is obtained through rule thrust, and then defuzzification is performed to obtain the final adjustment result.

7. The load regulation method of the smart energy unit device with load flexible regulation according to claim 5 is characterized in that: The load regulation strategy includes peak and valley load regulation: Peak-valley load regulation includes peak-valley regulation execution: During peak load periods, the system will take load reduction measures to prioritize reducing non-core loads; During the load valley period, the system allows renewable energy or low-cost energy to enter the system through regulation strategies to achieve load charging / supplementation; The peak adjustment formula is: L adjusted =L current ·(1-b peak ) Where: L adjusted is the regulated load; L current is the current load; β peak is the reduction factor, which indicates the proportion of load reduction during peak hours; The trough adjustment formula is: L adjusted =L current ·(1+β valley ) Where: β valley It is the increase coefficient, which indicates the proportion of load increase during the off-peak period.

8. The load regulation method of the smart energy unit device with load flexible regulation according to claim 5 is characterized in that: The load regulation strategy also includes emergency load response: Load forecasting and real-time monitoring: The core processing unit quickly identifies abnormal load conditions based on real-time load data and historical trends; When the load fluctuation exceeds the set threshold, the emergency load response is triggered when the load increase exceeds 5% or reaches 90% of the maximum load. The system will give priority to shutting down unnecessary loads or switching non-core loads to standby mode. The emergency response formula is: L adjusted =L current ·(1-b emergency ) Where: L adiusted is the regulated load; L current is the current load; β emergency It is the emergency load response factor, which is dynamically adjusted according to the severity of load fluctuations.

9. The load regulation method of the smart energy unit device with load flexible regulation according to claim 5, characterized in that: The load regulation strategy also includes an energy-saving mode: the system will prioritize shutting down non-core devices with high energy consumption while ensuring core functions. When power demand is low, the system will charge energy storage devices and release the stored energy when the load is high, thus avoiding overloading of the traditional power grid. Energy saving mode formula: L adjusted =L current ·(1-b emergy_saving ) Where: L adiusted is the regulated load; L current is the current load; β emergy_saving It is the energy-saving adjustment coefficient, which is dynamically adjusted according to the load status, power factor and energy storage status.

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