Micro-grid load cooperative regulation and control method based on electrothermal coupling

Abstract

The invention provides a micro-grid load cooperative regulation and control method based on electrothermal coupling, and belongs to the technical field of power grid control, and the method comprises the steps: obtaining a plurality of historical operation data and historical equipment state data of an electrothermal coupling micro-grid, and collecting the real-time operation data and real-time equipment state data of the electrothermal coupling micro-grid in real time, acquiring real-time electric heating requirements of all users; determining an operation parameter range matrix and a preset adjustable range matrix of each device; determining a real-time operation parameter adjustable matrix and a real-time device adjustable matrix of each device; determining a minimum objective function and a balance function; and obtaining a macroscopic regulation and control instruction, and determining and executing a real-time regulation and control instruction. The electric heating load collaborative optimization regulation and control can be realized, the heat supply efficiency and the power supply efficiency are improved, the empirical judgment deviation is avoided, the regulation and control accuracy and timeliness are improved, the adaptability of the micro-grid to complex working conditions is enhanced, the energy configuration is optimized, the operation cost is reduced, and the system stability and the energy utilization efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid control technology, and in particular to a microgrid load coordinated control method based on electrothermal coupling. Background Art

[0002] With the transformation of energy structures and the development of distributed energy, electric-thermal coupled microgrids have emerged due to their ability to efficiently utilize multiple energy sources. Early microgrid regulation and control were primarily based on single-energy management, lacking coordination between electric and thermal energy. With the intensification of energy supply and demand and the growing diversification of user demands, traditional regulation and control methods are unable to meet the complex operational requirements of microgrids. Existing technologies lack systematic integration and analysis of historical and real-time data, resulting in information silos. Regulation and control strategies are unable to effectively balance economic efficiency and stability, and struggle to reconcile macroeconomic directives with the microgrid's own operational objectives. More scientific and efficient coordinated regulation and control methods are urgently needed.

[0003] Therefore, the present invention provides a microgrid load coordinated control method based on electrothermal coupling. Summary of the Invention

[0004] The present invention provides a method for coordinated load control of a microgrid based on electrothermal coupling. The method analyzes historical operating data to determine an operating parameter range matrix, combines real-time operating data to determine a real-time operating parameter adjustable matrix, analyzes all historical device status data to determine a preset adjustable range matrix for each device, combines real-time device status data to determine a real-time device adjustable matrix for each device, determines a minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrices of all devices, determines a balance function based on real-time electrothermal demand and the real-time device adjustable matrices of all devices, and determines and executes real-time control instructions for the electrothermal coupling microgrid based on macro-control instructions, the minimum objective function, the balance function, and the macro-control instructions. This method can achieve coordinated optimized control of electrothermal loads, improve heating and power supply efficiency, avoid empirical judgment bias, enhance control accuracy and timeliness, enhance the adaptability of the microgrid to complex operating conditions, optimize energy allocation, reduce operating costs, and improve system stability and energy utilization efficiency.

[0005] The present invention provides a microgrid load coordinated control method based on electrothermal coupling, comprising:

[0006] Step 1: Obtain multiple historical operating data and historical equipment status data of the electrothermal coupled microgrid, collect real-time operating data and real-time equipment status data of the electrothermal coupled microgrid in real time, and obtain the real-time electric and heating demand of all users;

[0007] Step 2: Determine an operating parameter range matrix based on all historical operating data, and determine a preset adjustable range matrix for each device based on all historical device status data;

[0008] Step 3: Determine the real-time operating parameter adjustable matrix based on the real-time operating data and the operating parameter range matrix, and determine the real-time device adjustable matrix for each device based on the real-time device status data and the preset adjustable range matrix for each device;

[0009] Step 4: Determine the minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix of all devices, and determine the balance function based on the real-time electric and heating demand and the real-time device adjustable matrix of all devices;

[0010] Step 5: Obtain macro-control instructions. Based on the minimum objective function, balance function and macro-control instructions, determine and execute the real-time control instructions of the electrothermal coupled microgrid.

[0011] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, multiple historical operation data and historical device status data of the electrothermal coupling microgrid are obtained, and real-time operation data and real-time device status data of the electrothermal coupling microgrid are collected in real time, including:

[0012] Obtaining historical operation data and historical equipment status data for multiple specified operation periods, wherein the historical operation data includes operation tags and historical operation sub-data of multiple operation parameters, and the historical equipment status data includes equipment tags and historical equipment status sub-data of multiple equipment;

[0013] The real-time operation data includes operation tags of multiple operation parameters and real-time operation sub-data, wherein the operation tags include power parameters and thermal parameters;

[0014] The real-time device status data includes device tags of multiple devices and real-time device status sub-data, wherein the device tags include power devices, thermal devices, and electric-heat conversion devices.

[0015] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, an operating parameter range matrix is ​​determined based on all historical operating data, and a preset adjustable range matrix of each device is determined based on all historical device status data, including:

[0016] Extracting historical operating sub-data of each operating parameter from the historical operating data of all specified operating cycles, determining operating range data of each operating parameter, analyzing the operating range data of each operating parameter, and determining an adjustable operating parameter range of each operating parameter;

[0017] determining an operating parameter feature vector based on all operating parameters, and determining an operating parameter range matrix based on the operating parameter feature vector and adjustable operating parameter ranges of all operating parameter features in the operating parameter feature vector;

[0018] Extract all historical device status sub-data of each device in the historical device status data of all specified operation cycles, and determine the device range data of each device;

[0019] Analyze device range data for each device to determine multiple device parameters for each device and a preset adjustable range for each device parameter;

[0020] An adjustable device feature vector for each device is determined based on all device parameters of each device, and a preset adjustable range matrix for each device is determined based on the adjustable device feature vector for each device and the preset adjustable range of each adjustable device feature in the adjustable device feature vector.

[0021] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, a real-time operating parameter adjustable matrix is ​​determined based on real-time operating data and an operating parameter range matrix, including:

[0022] Preprocess the real-time operation data and real-time equipment status data of the electrothermal coupled microgrid respectively;

[0023] Based on the operating parameter feature vector, feature extraction is performed on the real-time operating sub-data of all operating parameters in the real-time operating data to determine the real-time operating parameter vector;

[0024] Determining whether a characteristic value of each operating parameter feature in the real-time operating parameter vector is within an adjustable operating parameter range in the corresponding operating parameter range matrix, and performing a first adjustable mark on a feature corresponding to each operating parameter characteristic value within the adjustable operating parameter range;

[0025] Determining a real-time adjustable operating parameter vector based on all operating parameter features with a first adjustable flag, and determining a real-time adjustable operating value vector based on the feature values ​​of all features in the real-time adjustable operating parameter vector;

[0026] Determining, based on the real-time adjustable operating parameter vector and the adjustable operating parameter range, a real-time operating parameter adjustable range for each operating parameter feature having a first adjustable mark in the real-time adjustable operating parameter vector;

[0027] A real-time operating parameter adjustable matrix is ​​determined based on the real-time adjustable operating parameter vector, the real-time adjustable operating characteristic value vector, and the real-time operating parameter adjustable ranges of all operating parameter characteristics with the first adjustable mark.

[0028] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, a real-time device adjustable matrix of each device is determined based on real-time device status data and a preset adjustable range matrix of each device, including:

[0029] Based on the adjustable device feature vector of each device, feature extraction is performed on the device status sub-data of each device in the preprocessed real-time device status data to determine the real-time device parameter adjustable vector of each device;

[0030] Determining whether a characteristic value of each adjustable device feature in the real-time device parameter adjustable vector of each device is within a preset adjustable range in the corresponding preset adjustable range matrix, and assigning a second adjustable mark to the feature corresponding to each adjustable device characteristic value within the preset adjustable range;

[0031] determining a real-time device adjustable vector for each device based on all adjustable device features having a second adjustable flag for each device, and determining a real-time device adjustable value vector based on the feature values ​​of all adjustable device features in the real-time device adjustable vector;

[0032] Determining, based on the adjustable device feature vector of each device and a preset adjustable range matrix, a real-time feature adjustable range of each device based on each adjustable device feature with a second adjustable flag in the adjustable device feature vector;

[0033] A real-time device adjustable matrix for each device is determined based on the real-time device adjustable vector for each device and the real-time feature adjustable ranges of all adjustable device features with the second adjustable flag.

[0034] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, the minimum objective function is determined based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix of all devices, including:

[0035] Calculating the stable value of the operating parameter of each eigenvalue in the real-time adjustable operating value vector based on the real-time operating parameter adjustable matrix;

[0036] Calculating a device characteristic stability value for each eigenvalue in a real-time device adjustable value vector based on a real-time device adjustable matrix for each device;

[0037] A minimum objective function is determined based on the stable values ​​of the operating parameters of all eigenvalues ​​in the real-time adjustable operating value vector and the stable values ​​of the device characteristics of all eigenvalues ​​in the real-time device adjustable value vector.

[0038] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, a balancing function is determined based on real-time electrothermal demand and the real-time device adjustable matrix of all devices, including:

[0039] Based on the real-time electricity and heat demands of all users, determine the real-time electricity demand and real-time heat demand of the electrothermal coupled microgrid;

[0040] determining a power balancing function based on real-time power demand and a real-time device adjustable matrix of all devices having a device tag of power;

[0041] Determine a thermal balance function based on real-time thermal demand and a real-time device adjustable matrix of all devices with a thermal tag;

[0042] Based on the power balance function and the thermal balance function, the balance function of the electrothermal coupled microgrid is determined.

[0043] According to the microgrid load coordinated control method based on electrothermal coupling provided by the present invention, a macro-control instruction is obtained, and based on the minimum objective function, the balance function and the macro-control instruction, a real-time control instruction of the electrothermal coupling microgrid is determined and executed, including:

[0044] Build an optimization model based on the minimum objective function and the balance function, and determine the optimal operation strategy based on the output of the optimization model;

[0045] Adjusting the macro-control instructions based on the optimal operation strategy to determine the real-time control instructions of the electrothermal coupled microgrid, wherein the real-time control instructions include multiple real-time sub-control instructions;

[0046] Each real-time sub-control instruction in the real-time control instruction is sent to the corresponding device and executed.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] By analyzing historical operating data, the operating parameter range matrix is ​​determined. This is combined with real-time operating data to determine the real-time operating parameter adjustable matrix. All historical device status data is analyzed to determine the preset adjustable range matrix for each device. The real-time device adjustable matrix for each device is then combined with real-time device status data to determine the minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix for all devices. A balance function is determined based on real-time electric and heating demand and the real-time device adjustable matrices for all devices. Based on the macro-control instructions, the minimum objective function, the balance function, and the macro-control instructions, real-time control instructions for the electric and thermal coupled microgrid are determined and executed. This system can achieve coordinated optimization and control of electric and thermal loads, improve heating and power supply efficiency, avoid empirical judgment bias, enhance control accuracy and timeliness, enhance the microgrid's adaptability to complex operating conditions, optimize energy allocation, reduce operating costs, and improve system stability and energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 It is a flow chart of a microgrid load coordinated control method based on electrothermal coupling provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1:

[0053] The embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, such as Figure 1 Shown, including:

[0054] Step 1: Obtain multiple historical operating data and historical equipment status data of the electrothermal coupled microgrid, collect real-time operating data and real-time equipment status data of the electrothermal coupled microgrid in real time, and obtain the real-time electric and heating demand of all users;

[0055] Step 2: Determine an operating parameter range matrix based on all historical operating data, and determine a preset adjustable range matrix for each device based on all historical device status data;

[0056] Step 3: Determine the real-time operating parameter adjustable matrix based on the real-time operating data and the operating parameter range matrix, and determine the real-time device adjustable matrix for each device based on the real-time device status data and the preset adjustable range matrix for each device;

[0057] Step 4: Determine the minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix of all devices, and determine the balance function based on the real-time electric and heating demand and the real-time device adjustable matrix of all devices;

[0058] Step 5: Obtain macro-control instructions. Based on the minimum objective function, balance function and macro-control instructions, determine and execute the real-time control instructions of the electrothermal coupled microgrid.

[0059] In this embodiment, based on historical operating data, the fluctuation range of each operating parameter is statistically analyzed, outliers are eliminated, and an operating parameter range matrix is ​​determined. Based on historical device status data and combined with device design and operating specifications, a preset adjustable range matrix for each device is determined.

[0060] In this embodiment, real-time operating data is compared with an operating parameter range matrix to identify adjustable parameters and their ranges, forming a real-time operating parameter adjustment matrix. Similarly, a real-time device adjustment matrix is ​​determined for each device based on real-time device status data and a preset adjustable range matrix. These two matrices accurately reflect the current adjustable resources of the microgrid.

[0061] In this embodiment, a minimum objective function is constructed based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix. In combination with the real-time electric and thermal demand and the device adjustable matrix, an electric power and thermal balance function is established to ensure the balance of energy supply and demand in the microgrid and achieve stable operation.

[0062] In this embodiment, after obtaining macro-control instructions, they are used as constraints, along with the minimum objective function and balance function, to construct an optimization model. The optimization algorithm solves the problem, obtaining the optimal operating parameters for each device. These instructions are then generated into real-time control instructions, encompassing sub-instructions such as device power adjustment and operating mode switching. These instructions are then sent to the corresponding device for execution, and dynamic adjustments are made through real-time feedback, achieving closed-loop control.

[0063] The above technical solution has the following beneficial effects: determining an operating parameter range matrix by analyzing acquired historical operating data, determining a real-time operating parameter adjustable matrix based on real-time operating data, determining a preset adjustable range matrix for each device by analyzing all acquired historical device status data, determining a real-time device adjustable matrix for each device based on real-time device status data, determining a minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrices of all devices, determining a balance function based on real-time electric and heating demand and the real-time device adjustable matrices of all devices, and determining and executing real-time control instructions for the electric and thermal coupled microgrid based on macro-control instructions, the minimum objective function, the balance function, and the macro-control instructions. This method can achieve coordinated optimization and control of electric and thermal loads, improve heating and power supply efficiency, avoid empirical judgment bias, enhance control accuracy and timeliness, enhance the microgrid's adaptability to complex operating conditions, optimize energy allocation, reduce operating costs, and improve system stability and energy utilization efficiency.

[0064] Example 2:

[0065] The embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, which obtains multiple historical operation data and historical device status data of the electrothermal coupling microgrid, and collects real-time operation data and real-time device status data of the electrothermal coupling microgrid in real time, including:

[0066] Obtaining historical operation data and historical equipment status data for multiple specified operation periods, wherein the historical operation data includes operation tags and historical operation sub-data of multiple operation parameters, and the historical equipment status data includes equipment tags and historical equipment status sub-data of multiple equipment;

[0067] The real-time operation data includes operation tags of multiple operation parameters and real-time operation sub-data, wherein the operation tags include power parameters and thermal parameters;

[0068] The real-time device status data includes device tags of multiple devices and real-time device status sub-data, wherein the device tags include power devices, thermal devices, and electric-heat conversion devices.

[0069] In this embodiment, historical data for multiple specified operating periods is acquired. These periods can be flexibly set based on factors such as seasonal variations, peak and trough periods of electricity consumption, and equipment maintenance cycles. For example, data can be collected for different periods of each month, week, or even day over the past year to comprehensively cover various operating conditions.

[0070] In this embodiment, the operating parameters can be bus voltage deviation, system frequency deviation, grid interaction power, and voltage total harmonic distortion rate, etc., whose operating labels are power parameters, or they can be heating network supply and return water temperature difference, heating network flow, pipe network pressure drop, and thermal inertia equivalent energy storage, etc., whose operating labels are thermal parameters.

[0071] The beneficial effects of the above technical solution are: obtaining multiple historical operating data and historical equipment status data of the electrothermal coupling microgrid, and real-time collection of real-time operating data and real-time equipment status data of the electrothermal coupling microgrid, which can provide data support for determining the operating parameter range matrix and the real-time operating parameter adjustable matrix.

[0072] Example 3:

[0073] An embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, which determines an operating parameter range matrix based on all historical operating data, and determines a preset adjustable range matrix for each device based on all historical device status data, including:

[0074] Extracting historical operating sub-data of each operating parameter from the historical operating data of all specified operating cycles, determining operating range data of each operating parameter, analyzing the operating range data of each operating parameter, and determining an adjustable operating parameter range of each operating parameter;

[0075] determining an operating parameter feature vector based on all operating parameters, and determining an operating parameter range matrix based on the operating parameter feature vector and adjustable operating parameter ranges of all operating parameter features in the operating parameter feature vector;

[0076] Extract all historical device status sub-data of each device in the historical device status data of all specified operation cycles, and determine the device range data of each device;

[0077] Analyze device range data for each device to determine multiple device parameters for each device and a preset adjustable range for each device parameter;

[0078] An adjustable device feature vector for each device is determined based on all device parameters of each device, and a preset adjustable range matrix for each device is determined based on the adjustable device feature vector for each device and the preset adjustable range of each adjustable device feature in the adjustable device feature vector.

[0079] This example uses historical operating data from multiple specified operating cycles to extract historical sub-data for each operating parameter. This data records the specific values ​​of the parameter at different points in the past. Through statistical analysis, the maximum, minimum, average, and standard deviation of the parameter are calculated to determine the operating range of each parameter, clearly presenting the parameter's variation range.

[0080] In this embodiment, the operating range data is analyzed, combined with the operating characteristics of the microgrid and the technical specifications of the equipment, and abnormal data caused by special circumstances such as faults and extreme operating conditions are eliminated to determine the actual adjustable range of the parameter under normal operating conditions, that is, the adjustable operating parameter range.

[0081] In this embodiment, all operating parameters are integrated to form an operating parameter feature vector, which contains various parameter information required for microgrid operation. The adjustable operating parameter range corresponding to each operating parameter feature is then combined with it to construct an operating parameter range matrix.

[0082] In this embodiment, the operating parameter range matrix is ​​a 3×M1 matrix, where M1 represents the number of operating parameter features. The first row of the operating parameter range matrix is ​​the operating parameter feature vector, the second row is the adjustable lower limits of all operating parameter features in the operating parameter feature vector, and the third row is the adjustable upper limits of all operating parameter features in the operating parameter feature vector.

[0083] In this embodiment, historical device status data for all specified operating cycles is processed. For each device (covering power equipment, thermal equipment, and electric-to-heat conversion equipment), all historical device status sub-data is extracted, including device operating time, start and stop times, key component performance parameters, and fault records. By organizing and analyzing this data, all device parameter and device scope data for each device are determined, comprehensively reflecting the device's status changes and performance during historical operation.

[0084] This example further analyzes device range data to identify key parameters that impact device operation and adjustability, such as the generator's power adjustment range and the boiler's heating temperature adjustment range. Combining device design parameters, historical operating experience, and technical specifications, a preset adjustable range for each device parameter is determined, clarifying the device's adjustability under normal conditions.

[0085] In this embodiment, all device parameters of each device are integrated into an adjustable device feature vector to reflect the device's adjustment characteristics. Then, combined with the preset adjustable range of each adjustable device feature, a preset adjustable range matrix for each device is constructed.

[0086] In this embodiment, the preset adjustable range matrix is ​​a 3×iM2 matrix, iM1 represents the number of adjustable device features in the adjustable device feature vector of the i-th device, the first row of the preset adjustable range matrix is ​​the adjustable device feature vector, the second row is the adjustable lower limit of all adjustable device features in the adjustable device feature vector, and the third row is the adjustable upper limit of all adjustable device features in the adjustable device feature vector.

[0087] The beneficial effects of the above technical solution are: determining the operating parameter range matrix based on all historical operating data, and determining the preset adjustable range matrix of each device based on all historical device status data, which can accurately define the microgrid parameters and device adjustment range, provide quantitative standards for real-time regulation, enhance the scientificity, accuracy and reliability of system regulation, and improve heating efficiency and power supply efficiency.

[0088] Example 4:

[0089] An embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, which determines a real-time operating parameter adjustable matrix based on real-time operating data and an operating parameter range matrix, including:

[0090] Preprocess the real-time operation data and real-time equipment status data of the electrothermal coupled microgrid respectively;

[0091] Based on the operating parameter feature vector, feature extraction is performed on the real-time operating sub-data of all operating parameters in the real-time operating data to determine the real-time operating parameter vector;

[0092] Determining whether a characteristic value of each operating parameter feature in the real-time operating parameter vector is within an adjustable operating parameter range in the corresponding operating parameter range matrix, and performing a first adjustable mark on a feature corresponding to each operating parameter characteristic value within the adjustable operating parameter range;

[0093] Determining a real-time adjustable operating parameter vector based on all operating parameter features with a first adjustable flag, and determining a real-time adjustable operating value vector based on the feature values ​​of all features in the real-time adjustable operating parameter vector;

[0094] Determining, based on the real-time adjustable operating parameter vector and the adjustable operating parameter range, a real-time operating parameter adjustable range for each operating parameter feature having a first adjustable mark in the real-time adjustable operating parameter vector;

[0095] A real-time operating parameter adjustable matrix is ​​determined based on the real-time adjustable operating parameter vector, the real-time adjustable operating characteristic value vector, and the real-time operating parameter adjustable ranges of all operating parameter characteristics with the first adjustable mark.

[0096] In this embodiment, real-time operation data and real-time device status data may contain noise, missing values, or outliers during the collection process, thus requiring preprocessing. For real-time operation data, filtering algorithms are used to remove noise caused by electromagnetic interference, interpolation methods are used to fill missing parameter values, and statistical analysis is used to identify and correct outliers. Similar processing is performed on real-time device status data to ensure data accuracy and integrity.

[0097] In this embodiment, the constructed operating parameter feature vector is used as a reference to extract the real-time operating sub-data of each operating parameter (electrical and thermal parameters) from the real-time operating data. The sub-data are then integrated according to the structure and order of the feature vector to form a real-time operating parameter vector. This vector reflects the specific status of the current operating parameters of the microgrid.

[0098] In this embodiment, each operating parameter feature value in the real-time operating parameter vector is compared with the adjustable operating parameter range of the corresponding feature in the operating parameter range matrix. If the feature value is within the adjustable range, the feature is marked as first adjustable.

[0099] In this embodiment, operating parameter features with a first adjustable mark are screened out to form a real-time adjustable operating parameter vector; the actual characteristic values ​​corresponding to these features are extracted to form a real-time adjustable operating value vector, focusing on the currently adjustable operating parameters and their values ​​of the microgrid.

[0100] In this embodiment, based on the adjustable operating parameter range in the operating parameter range matrix and combined with the real-time adjustable operating parameter vector, the real-time operating parameter adjustable range of each operating parameter feature with a first adjustable mark at the current moment is further clarified, and the adjustment interval of the adjustable parameter is refined. For example, some parameters can only be adjusted upward, then the real-time operating parameter adjustable range of the parameter is composed of the characteristic value of the parameter to the upper limit of the adjustable operating parameter range of the parameter.

[0101] In this embodiment, the real-time adjustable operating parameter vector, the real-time adjustable operating value vector, and the real-time operating parameter adjustable range of each parameter are integrated to construct a real-time operating parameter adjustable matrix.

[0102] In this embodiment, the real-time operating parameter adjustable matrix can be expressed as:

[0103]

[0104] Among them, MOP represents the real-time operation parameter adjustable matrix, VC op represents the real-time adjustable operating parameter vector, CO op Represents the real-time adjustable operating value vector, LCO op Indicates the lower limit vector of the real-time operating parameters, UCO op represents the real-time adjustable upper limit vector of the operating parameters, and N1 represents the number of operating parameter features with the first adjustable mark in the real-time adjustable operating parameter vector.

[0105] In this embodiment, the real-time operating parameter adjustable matrix is ​​a 4×N1 matrix, the first row of the real-time operating parameter adjustable matrix is ​​a real-time adjustable operating parameter vector, the second row is a real-time adjustable operating value vector, the third row is the adjustable lower limit of the real-time operating parameter adjustable range of all operating parameter characteristics with the first adjustable mark in the real-time adjustable operating parameter vector, and the fourth row is the adjustable upper limit of the real-time operating parameter adjustable range of all operating parameter characteristics with the first adjustable mark in the real-time adjustable operating parameter vector.

[0106] The beneficial effects of the above technical solution are: determining the real-time operating parameter adjustable matrix based on real-time operating data and the operating parameter range matrix can further realize dynamic and precise parameter control, provide quantitative standards for real-time control, so as to improve heating efficiency and power supply efficiency, and enhance the scientificity, accuracy and reliability of system control.

[0107] Example 5:

[0108] An embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, which determines a real-time device adjustable matrix for each device based on real-time device status data and a preset adjustable range matrix for each device, including:

[0109] Based on the adjustable device feature vector of each device, feature extraction is performed on the device status sub-data of each device in the preprocessed real-time device status data to determine the real-time device parameter adjustable vector of each device;

[0110] Determining whether a characteristic value of each adjustable device feature in the real-time device parameter adjustable vector of each device is within a preset adjustable range in the corresponding preset adjustable range matrix, and assigning a second adjustable mark to the feature corresponding to each adjustable device characteristic value within the preset adjustable range;

[0111] determining a real-time device adjustable vector for each device based on all adjustable device features having a second adjustable flag for each device, and determining a real-time device adjustable value vector based on the feature values ​​of all adjustable device features in the real-time device adjustable vector;

[0112] Determining, based on the adjustable device feature vector of each device and a preset adjustable range matrix, a real-time feature adjustable range of each device based on each adjustable device feature with a second adjustable flag in the adjustable device feature vector;

[0113] A real-time device adjustable matrix for each device is determined based on the real-time device adjustable vector for each device and the real-time feature adjustable ranges of all adjustable device features with the second adjustable flag.

[0114] In this embodiment, each device has a corresponding adjustable device feature vector, which contains information about the key adjustable parameters of the device. Based on this vector, targeted extraction of preprocessed real-time device status data is performed. For example, for a generator (electrical equipment), real-time data corresponding to the adjustable device feature vector, such as its speed and output power, is extracted; for a boiler (thermal equipment), data such as its combustion temperature and steam pressure are extracted. This data is then integrated to form a real-time adjustable device parameter vector for each device, reflecting the current operating parameter status of the device.

[0115] In this embodiment, the value of each adjustable device feature in the real-time device parameter adjustable vector for each device is compared with the preset adjustable range of the corresponding feature in the preset adjustable range matrix for that device. If the feature value is within the preset range, indicating that the device parameter is adjustable, the feature is marked as adjustable. For example, if the heating power of a heat pump is within its preset adjustable range, the heating power feature is marked, thereby filtering out the device's currently adjustable parameters.

[0116] In this embodiment, adjustable device features with the second adjustable mark are screened out to form a real-time device adjustable vector for each device; then, actual feature values ​​corresponding to these features are extracted to form a real-time device adjustable value vector.

[0117] In this embodiment, the real-time feature adjustable range of each adjustable device feature with a second adjustable flag is further determined at the current moment by combining the adjustable device feature vector of each device with a preset adjustable range matrix. The adjustable device features with a second adjustable flag are considered to have adjustment limits, such as upward adjustment only, downward adjustment only, or bidirectional adjustment. For example, the output power of some devices can only be increased from the current level to meet additional demand, while certain parameters of some devices may only be adjusted downward to avoid damage or failure. Parameters of other devices can also be adjusted bidirectionally based on actual conditions, enabling more flexible control.

[0118] In this embodiment, the real-time device adjustable vector, the real-time device adjustable value vector, and the real-time feature adjustable range of each adjustable device feature of each device are integrated together to construct a real-time device adjustable matrix for each device.

[0119] In this embodiment, the real-time device adjustable matrix can be expressed as:

[0120]

[0121] Among them, MEP a represents the real-time device adjustable matrix of the i-th device, represents the real-time device adjustable vector of the i-th device, represents the real-time device adjustable value vector of the i-th device, represents the real-time feature adjustable lower limit vector of the i-th device, represents the real-time feature adjustable upper limit vector of the i-th device, and iN2 represents the number of adjustable device features with the second adjustable flag in the real-time device adjustable vector of the i-th device.

[0122] In this embodiment, the real-time device adjustable matrix is ​​a 3×iN2 matrix, the first row of the real-time device adjustable matrix contains the real-time device adjustable vector, the second row contains the real-time device adjustable value vector, the third row contains the adjustable lower limit of the real-time feature adjustable range of the adjustable device feature with the second adjustable mark in the real-time device adjustable vector, and the fourth row contains the adjustable upper limit of the real-time feature adjustable range of the adjustable device feature with the second adjustable mark in the real-time device adjustable vector.

[0123] The beneficial effects of the above technical solution are: based on the real-time device status data and the preset adjustable range matrix of each device, the real-time device adjustable matrix of each device is determined, which can realize the refined analysis and regulation of the operating status of a single device, improve the accuracy of device regulation and the stability of the overall operation of the microgrid, optimize the utilization efficiency of equipment resources, and improve the heating efficiency and power supply efficiency.

[0124] Example 6:

[0125] The embodiment of the present invention provides a microgrid load coordinated control method based on electrothermal coupling, which determines the minimum objective function based on a real-time operating parameter adjustable matrix and a real-time device adjustable matrix of all devices, including:

[0126] Calculating the stable value of the operating parameter of each eigenvalue in the real-time adjustable operating value vector based on the real-time operating parameter adjustable matrix;

[0127] Calculating a device characteristic stability value for each eigenvalue in a real-time device adjustable value vector based on a real-time device adjustable matrix for each device;

[0128] A minimum objective function is determined based on the stable values ​​of the operating parameters of all eigenvalues ​​in the real-time adjustable operating value vector and the stable values ​​of the device characteristics of all eigenvalues ​​in the real-time device adjustable value vector.

[0129] In this embodiment, the calculation formula for calculating the stable value of the operating parameter of each eigenvalue in the real-time adjustable operating value vector based on the real-time adjustable operating parameter matrix can be expressed as:

[0130]

[0131] in, represents the stable value of the operating parameter of the j-th eigenvalue in the real-time adjustable operating value vector based on the first offset value and the second offset value, represents the jth eigenvalue in the real-time adjustable running value vector, Indicates the jth lower limit in the real-time operating parameter adjustable lower limit vector, represents the jth upper limit in the real-time operating parameter adjustable upper limit vector, represents the first offset value of the jth eigenvalue in the real-time adjustable operating value vector based on the jth lower limit in the real-time operating parameter adjustable lower limit vector, represents the second offset value of the jth eigenvalue in the real-time adjustable operating value vector based on the jth upper limit in the real-time operating parameter adjustable upper limit vector, represents the operation label of the jth operation parameter feature in the real-time adjustable operation parameter vector, represents the operating parameter weight of the jth operating parameter feature in the real-time adjustable operating parameter vector, W1 represents the power parameter weight, W2 represents the thermal parameter weight, The j-th operating parameter feature of the real-time adjustable operating parameter vector represents the label parameter weight based on the operating label.

[0132] In this embodiment, the device characteristic stability value of each eigenvalue in the real-time device adjustable value vector is calculated based on the real-time device adjustable matrix of each device. The calculation formula can be expressed as:

[0133]

[0134] in, represents the device characteristic stable value of the kth characteristic value in the real-time device adjustable value vector of the i-th device based on the third offset value and the fourth offset value, represents the kth eigenvalue in the real-time device adjustable value vector of the i-th device, represents the kth lower limit in the real-time feature adjustable lower limit vector of the i-th device, represents the kth upper limit in the real-time feature adjustable upper limit vector of the i-th device, represents the third offset value of the kth characteristic value in the real-time device adjustable value vector of the i-th device based on the kth lower limit in the real-time characteristic adjustable lower limit vector, represents the fourth offset value of the kth characteristic value in the real-time device adjustable value vector of the i-th device based on the kth upper limit in the real-time characteristic adjustable upper limit vector, The label device weight representing the kth eigenvalue in the real-time device adjustable value vector of the i-th device, The device label representing the kth tunable device feature in the real-time device tunable vector for the i-th device, represents the device characteristic weight of the kth eigenvalue in the real-time device adjustable value vector of the i-th device, W3 represents the power device weight, W4 represents the thermal device weight, and W5 represents the electric-heat conversion device weight. Represents the label device weight of the kth eigenvalue in the real-time device adjustable value vector of the i-th device.

[0135] In this embodiment, the minimum objective function is determined based on the stable values ​​of the operating parameters of all eigenvalues ​​in the real-time adjustable operating value vector and the stable values ​​of the device characteristics of all eigenvalues ​​in the real-time device adjustable value vector. The calculation formula can be expressed as:

[0136]

[0137] Among them, OF represents the minimum objective function, CT represents the economic cost, RT represents the operational stability, α represents the economic adjustment factor, and β represents the stability adjustment factor. represents the unit cost of the jth operating parameter feature in the real-time adjustable operating parameter vector, represents the unit cost of the kth adjustable device feature in the real-time device adjustable vector of the i-th device, N1 represents the number of operating parameter features with the first adjustable flag in the real-time adjustable operating parameter vector, iN2 represents the number of adjustable device features with the second adjustable flag in the real-time device adjustable vector of the i-th device, and Nu represents the number of devices.

[0138] The beneficial effects of the above technical solution are as follows: based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix of all devices, the minimum objective function is determined, which can comprehensively consider the system operation and equipment status, improve energy utilization efficiency and system stability, provide a scientific and efficient decision-making basis for the intelligent control of microgrids, and improve heating efficiency and power supply efficiency.

[0139] Example 7:

[0140] The embodiment of the present invention provides a microgrid load coordinated control method based on electric and thermal coupling, which determines a balancing function based on real-time electric and thermal demand and a real-time device adjustable matrix of all devices, including:

[0141] Based on the real-time electricity and heat demands of all users, determine the real-time electricity demand and real-time heat demand of the electrothermal coupled microgrid;

[0142] determining a power balancing function based on real-time power demand and a real-time device adjustable matrix of all devices having a device tag of power;

[0143] Determine a thermal balance function based on real-time thermal demand and a real-time device adjustable matrix of all devices with a thermal tag;

[0144] Based on the power balance function and the thermal balance function, the balance function of the electrothermal coupled microgrid is determined.

[0145] In this implementation, real-time electricity and heat usage data for all users is first collected and analyzed to determine the current power and heat demands of different users. For example, industrial users may require large amounts of electricity to drive equipment during peak production periods, while also requiring heat sources such as steam. Residential users, on the other hand, experience significant peaks in electricity and heat usage during the morning and evening hours. These dispersed user demands are aggregated to determine the real-time power and heat demands of the entire electrically coupled microgrid, forming the foundation for the subsequent construction of balancing functions.

[0146] In this embodiment, the power balance function can be determined by first classifying all devices with power labels, determining the power generation device set and the energy storage device set, extracting the real-time power generation power in the real-time device adjustable matrix of each device in the power generation device set, extracting the real-time charge and discharge power in the real-time device adjustable matrix of each device in the energy storage device set, obtaining the power exchange between the microgrid and the main grid, and determining the power balance function based on all the extracted real-time power generation power, real-time charge and discharge power, power exchange between the microgrid and the main grid, real-time power demand and power loss. The calculation formula of the power balance function can be expressed as: real-time power generation power of all devices in the power generation device set + real-time charge and discharge power of all devices in the energy storage device set + power exchange between the microgrid and the main grid = real-time power demand + power loss.

[0147] In this embodiment, the determination of the thermal balance function can first classify all devices with thermal labels, determine the heating device set and the heat storage device set, extract the real-time heating capacity in the real-time device adjustable matrix of each device in the heating device set, extract the storage and release heat power in the real-time device adjustable matrix of each device in the energy storage device set, and determine the thermal balance function based on all the extracted real-time heating capacity, storage and release heat power, real-time thermal demand and thermal energy loss. The calculation formula of the thermal balance function can be expressed as: real-time heating capacity of all devices in the heating device set + storage and release heat power of all devices in the heat storage device set = real-time thermal demand + thermal energy loss.

[0148] In this embodiment, the power balance function and the thermal balance function are integrated to form a balance function for the electrically coupled thermal microgrid. This balance function comprehensively considers the balance between power and thermal energy within the microgrid, ensuring that the energy within the microgrid is rationally distributed and efficiently utilized while meeting users' real-time power and thermal needs.

[0149] The beneficial effects of the above technical solution are as follows: based on the real-time electric and heating demand and the real-time device adjustable matrix of all devices, the balance function is determined, which can accurately match the electric and heating supply and demand of the microgrid, achieve dynamic energy balance, improve the comprehensive energy utilization rate, provide a quantitative basis for real-time regulation of the microgrid, improve heating efficiency and power supply efficiency, and enhance system stability and adaptability.

[0150] Example 8:

[0151] The embodiment of the present invention provides a method for coordinated control of microgrid loads based on electrothermal coupling, which obtains macro-control instructions, determines and executes real-time control instructions of the electrothermal coupling microgrid based on a minimum objective function, a balance function, and the macro-control instructions, including:

[0152] Build an optimization model based on the minimum objective function and the balance function, and determine the optimal operation strategy based on the output of the optimization model;

[0153] Adjusting the macro-control instructions based on the optimal operation strategy to determine the real-time control instructions of the electrothermal coupled microgrid, wherein the real-time control instructions include multiple real-time sub-control instructions;

[0154] Each real-time sub-control instruction in the real-time control instruction is sent to the corresponding device and executed.

[0155] In this embodiment, the minimum objective function reflects the microgrid's operational goals, while the balance function ensures a balanced supply and demand of electricity and heat within the microgrid. Combining these two functions creates an optimization model, an intelligent decision-making engine that considers two key aspects of microgrid operation: economic efficiency and stability.

[0156] In this embodiment, by solving the optimization model and utilizing various optimization algorithms (such as linear programming and genetic algorithms), a set of operating parameter combinations is found that achieves the optimal value of the minimum objective function while satisfying the constraints of the balance function. This set of operating parameters is the optimal operating strategy. For example, under the premise of balancing the supply and demand of electricity and heat, the power generation capacity of each power generation device and the charge and discharge status of the energy storage device are determined to minimize operating costs.

[0157] In this embodiment, macro-control instructions are usually issued by the upper-level power grid or energy management department, and may include power limits for microgrids, renewable energy consumption ratio requirements, etc. These instructions guide the operation of microgrids from a more macro perspective.

[0158] In this embodiment, the macro-control instructions are adjusted based on the optimal operating strategy obtained previously. Because the optimal operating strategy is calculated based on the microgrid's real-time operating conditions and objectives, it may not be completely consistent with the macro-control instructions. Through adjustment, the macro-control instructions are made more consistent with the actual operating requirements of the microgrid, while also better achieving the microgrid's objectives.

[0159] In this embodiment, after adjustment, a real-time control instruction for the electrothermal coupled microgrid is obtained. This instruction is composed of multiple real-time sub-control instructions. Each sub-control instruction corresponds to a specific device or group of related devices within the microgrid, and specifies the specific operation that these devices need to perform, such as adjusting the power generation or changing the heating temperature.

[0160] In this embodiment, each sub-control instruction within a real-time control instruction is sent to the corresponding device. This relies on the microgrid's communication system to ensure that the instructions are accurately transmitted to each device. After receiving the instructions, the device adjusts its operating state according to the instructions, thus achieving real-time control of the microgrid and ensuring that the microgrid achieves optimal operation while meeting macro-control requirements.

[0161] The beneficial effects of the above technical solution are: obtaining macro-control instructions, determining and executing real-time control instructions of the electrothermal coupled microgrid based on the minimum objective function, balance function and macro-control instructions, which can achieve the coordination and unity of macro and micro, goals and constraints, ensure the efficient operation of the microgrid itself, improve heating efficiency and power supply efficiency, and improve energy utilization efficiency and system stability.

[0162] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0163] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A microgrid load coordinated control method based on electrothermal coupling, characterized in that: include: Step 1: Obtain multiple historical operating data and historical equipment status data of the electrothermal coupled microgrid, collect real-time operating data and real-time equipment status data of the electrothermal coupled microgrid in real time, and obtain the real-time electric and heating demand of all users; Step 2: Determine an operating parameter range matrix based on all historical operating data, and determine a preset adjustable range matrix for each device based on all historical device status data; Step 3: Determine the real-time operating parameter adjustable matrix based on the real-time operating data and the operating parameter range matrix, and determine the real-time device adjustable matrix for each device based on the real-time device status data and the preset adjustable range matrix for each device; Step 4: Determine the minimum objective function based on the real-time operating parameter adjustable matrix and the real-time device adjustable matrix of all devices, and determine the balance function based on the real-time electric and heating demand and the real-time device adjustable matrix of all devices; Step 5: Obtain macro-control instructions. Based on the minimum objective function, balance function and macro-control instructions, determine and execute the real-time control instructions of the electrothermal coupled microgrid.

2. The microgrid load coordinated control method based on electrothermal coupling according to claim 1 is characterized in that: Acquire multiple historical operation data and historical equipment status data of the electrothermal coupled microgrid, and collect real-time operation data and real-time equipment status data of the electrothermal coupled microgrid in real time, including: Obtaining historical operation data and historical equipment status data for multiple specified operation periods, wherein the historical operation data includes operation tags and historical operation sub-data of multiple operation parameters, and the historical equipment status data includes equipment tags and historical equipment status sub-data of multiple equipment; The real-time operation data includes operation tags of multiple operation parameters and real-time operation sub-data, wherein the operation tags include power parameters and thermal parameters; The real-time device status data includes device tags of multiple devices and real-time device status sub-data, wherein the device tags include power devices, thermal devices, and electric-heat conversion devices.

3. The microgrid load coordinated control method based on electrothermal coupling according to claim 1 is characterized in that: Determine the operating parameter range matrix based on all historical operating data, and determine the preset adjustable range matrix for each device based on all historical device status data, including: Extracting historical operating sub-data of each operating parameter from the historical operating data of all specified operating cycles, determining operating range data of each operating parameter, analyzing the operating range data of each operating parameter, and determining an adjustable operating parameter range of each operating parameter; determining an operating parameter feature vector based on all operating parameters, and determining an operating parameter range matrix based on the operating parameter feature vector and adjustable operating parameter ranges of all operating parameter features in the operating parameter feature vector; Extract all historical device status sub-data of each device in the historical device status data of all specified operation cycles, and determine the device range data of each device; Analyze device range data for each device to determine multiple device parameters for each device and a preset adjustable range for each device parameter; An adjustable device feature vector for each device is determined based on all device parameters of each device, and a preset adjustable range matrix for each device is determined based on the adjustable device feature vector for each device and the preset adjustable range of each adjustable device feature in the adjustable device feature vector.

4. The microgrid load coordinated control method based on electrothermal coupling according to claim 3 is characterized in that: The real-time operating parameter adjustable matrix is ​​determined based on the real-time operating data and the operating parameter range matrix, including: Preprocess the real-time operation data and real-time equipment status data of the electrothermal coupled microgrid respectively; Based on the operating parameter feature vector, feature extraction is performed on the real-time operating sub-data of all operating parameters in the real-time operating data to determine the real-time operating parameter vector; Determining whether a characteristic value of each operating parameter feature in the real-time operating parameter vector is within an adjustable operating parameter range in the corresponding operating parameter range matrix, and performing a first adjustable mark on a feature corresponding to each operating parameter characteristic value within the adjustable operating parameter range; Determining a real-time adjustable operating parameter vector based on all operating parameter features with a first adjustable flag, and determining a real-time adjustable operating value vector based on the feature values ​​of all features in the real-time adjustable operating parameter vector; Determining, based on the real-time adjustable operating parameter vector and the adjustable operating parameter range, a real-time operating parameter adjustable range for each operating parameter feature having a first adjustable mark in the real-time adjustable operating parameter vector; A real-time operating parameter adjustable matrix is ​​determined based on the real-time adjustable operating parameter vector, the real-time adjustable operating characteristic value vector, and the real-time operating parameter adjustable ranges of all operating parameter characteristics with the first adjustable mark.

5. The microgrid load coordinated control method based on electrothermal coupling according to claim 4 is characterized in that: Determining a real-time device adjustable matrix for each device based on the real-time device status data and a preset adjustable range matrix for each device includes: Based on the adjustable device feature vector of each device, feature extraction is performed on the device status sub-data of each device in the preprocessed real-time device status data to determine the real-time device parameter adjustable vector of each device; Determining whether a characteristic value of each adjustable device feature in the real-time device parameter adjustable vector of each device is within a preset adjustable range in the corresponding preset adjustable range matrix, and assigning a second adjustable mark to the feature corresponding to each adjustable device characteristic value within the preset adjustable range; determining a real-time device adjustable vector for each device based on all adjustable device features having a second adjustable flag for each device, and determining a real-time device adjustable value vector based on the feature values ​​of all adjustable device features in the real-time device adjustable vector; Determining, based on the adjustable device feature vector of each device and a preset adjustable range matrix, a real-time feature adjustable range of each device based on each adjustable device feature with a second adjustable flag in the adjustable device feature vector; A real-time device adjustable matrix for each device is determined based on the real-time device adjustable vector for each device and the real-time feature adjustable ranges of all adjustable device features with the second adjustable flag.

6. The microgrid load coordinated control method based on electrothermal coupling according to claim 5 is characterized in that: Based on the real-time operation parameter adjustable matrix and the real-time device adjustable matrices of all devices, the minimum objective function is determined, including: Calculating the stable value of the operating parameter of each eigenvalue in the real-time adjustable operating value vector based on the real-time operating parameter adjustable matrix; Calculating a device characteristic stability value for each eigenvalue in a real-time device adjustable value vector based on a real-time device adjustable matrix for each device; A minimum objective function is determined based on the stable values ​​of the operating parameters of all eigenvalues ​​in the real-time adjustable operating value vector and the stable values ​​of the device characteristics of all eigenvalues ​​in the real-time device adjustable value vector.

7. The microgrid load coordinated control method based on electrothermal coupling according to claim 1 is characterized in that: Based on the real-time electrical and thermal demand and the real-time device adjustable matrix of all devices, the balancing function is determined, including: Based on the real-time electricity and heat demands of all users, determine the real-time electricity demand and real-time heat demand of the electrothermal coupled microgrid; determining a power balancing function based on real-time power demand and a real-time device adjustable matrix of all devices having a device tag of power; Determine a thermal balance function based on real-time thermal demand and a real-time device adjustable matrix of all devices with a thermal tag; Based on the power balance function and the thermal balance function, the balance function of the electrothermal coupled microgrid is determined.

8. The microgrid load coordinated control method based on electrothermal coupling according to claim 7 is characterized in that: Obtain macro-control instructions, determine and execute real-time control instructions of the electrothermal coupled microgrid based on the minimum objective function, balance function and macro-control instructions, including: Build an optimization model based on the minimum objective function and the balance function, and determine the optimal operation strategy based on the output of the optimization model; Adjusting the macro-control instructions based on the optimal operation strategy to determine the real-time control instructions of the electrothermal coupled microgrid, wherein the real-time control instructions include multiple real-time sub-control instructions; Each real-time sub-control instruction in the real-time control instruction is sent to the corresponding device and executed.

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