Electric heating energy system real-time control method based on virtual asynchronous machine

Abstract

The invention discloses an electric heating energy system real-time control method based on a virtual asynchronous machine. The method comprises the following steps: establishing an electric energy and heat energy dynamic response coupling time scale model of a regional electric heating coupling energy system based on different time scales; the invention discloses a virtual asynchronous machine control method based on an electric energy and heat energy dynamic response coupling time scale model, and the method carries out the real-time power balance control of an electric energy side, and improves the frequency dynamic response when the power demand of a DC side changes. And on the basis of judgment on the inertia coefficient of temperature change, the temperature response of the heat energy side is adjusted, and real-time control over the electric heating energy system is achieved. According to the method, real-time power balance can be achieved through virtual asynchronous machine control using large inertia heat and fast response speed electric power, and the method has the advantages of adapting to load power changes and restraining frequency oscillation.

Description

Technical Field

[0001] The invention belongs to the field of electric thermal energy systems, and relates to a real-time control method of an electric thermal energy system based on a virtual asynchronous machine. Background Art

[0002] During the winter heating season in China, there is a concentrated heat demand and unpredictable electricity drop. Generally, the regional electric-heat energy system can make full use of the characteristics of electricity and heat, thereby improving energy efficiency. However, while meeting the heating demand, the stable operation of the regional electric-heat coupled energy system is affected by its limited power output. This problem poses a major challenge to the supply and demand balance of the regional energy system. In the past few years, power balancing strategies have been designed to adjust the electricity or heat output when there is electricity and heat coupling. The regional electric-heat coupled energy system usually operates in two modes based on priority: heat-oriented mode and electricity-oriented mode. On the one hand, the main goal of the traditional regional electric-heat coupled energy system operating in the heat-oriented mode is to meet the real-time heat demand, while power generation is regarded as a secondary goal. On the other hand, a relatively electricity-oriented mode is being considered for the combined heat and power units. In this mode, the system adjusts the power output according to the electricity demand. In combined heat and power, there are two ways to adjust the electricity demand. One method is to adjust the heat output and fuel input of the boiler while maintaining a constant heat-to-power ratio. To achieve this goal, various control technologies can be used, such as advanced boiler-turbine coordinated control. Another method is to adjust the heat-to-power ratio by manipulating the turbine bypass valve, i.e., heat source regulation. Heat source regulation allows the cogeneration to bypass the slower dynamic processes of boiler and fuel processing and quickly increase power output. Both methods involve a single control variable and cannot meet the requirements of power regulation, especially in large regional heating networks. Therefore, in order to, the present invention proposes a real-time power balance control method for a virtual asynchronous machine based on an electric thermal energy system.

[0003] The lack of power flexibility is evident in the fact that wind power generation reduction rates as high as 20% in some provinces of China are due to the lack of flexibility of CHP units. For the power-oriented mode, microgrids face challenges due to their low inertia due to the large presence of power electronic converters. Frequent load changes and intermittent distributed generation can lead to significant frequency fluctuations, which have a negative impact on the efficiency and stability of microgrids. In contrast, heat sources exhibit slow dynamic processes and huge thermal inertia compared to power systems. The main frequency control in microgrids needs to respond quickly within seconds. Therefore, the flexibility of heat sources can be fully utilized to achieve system power balance, thereby improving the frequency response of the system. Current small hybrid microgrids have significant deficiencies in frequency control. Although frequency changes can be adapted by dynamically adjusting demand, isolated microgrids with low power ratings often react insufficiently when faced with significant frequency deviations, which may lead to reduced system stability. Existing frequency deviation correction methods rely on the local measured frequency dead zone and the linear correlation of temperature with frequency, but this strategy causes delays in the deactivation and restoration process of controlled loads when the frequency drops below a threshold, affecting system reliability. At the same time, despite the expansion of energy storage forms to enhance the flexibility of the power system, the thermal inertia of indoor temperatures in multiple regions of buildings has not been fully considered in the integrated thermal power dispatch of large power supply networks. This factor is crucial to the response to power demand and supply. Ignoring it may lead to insufficient optimization and increase the vulnerability of the system under high load conditions. In addition, new battery models still face challenges in integration and application. Overall, these defects make it urgent to improve the electric thermal energy system in achieving efficient and stable frequency control. Summary of the invention

[0004] In order to solve the above problems, the technical solution adopted by the present invention is: a real-time control method of an electric thermal energy system based on a virtual asynchronous machine, comprising: Establish a coupled time scale model of the dynamic response of electric and thermal energy in a regional electric-thermal coupled energy system based on different time scales; A virtual asynchronous machine control method based on a coupled time scale model of the dynamic response of electric energy and thermal energy is used to achieve real-time power balance control on the electric energy side and improve the frequency dynamic response when the power demand on the DC side changes. Based on the real-time temperature inside the building, the inertia coefficient of temperature change is used to adjust the temperature response on the thermal energy side to achieve real-time control of the electric thermal energy system.

[0005] Furthermore, the virtual asynchronous machine control method based on the electric energy and thermal energy dynamic response coupling time scale model is as follows: Based on the three-phase current abc of the asynchronous motor, the dq axis current is obtained through park transformation; Then the electromagnetic torque is calculated by the current in the dq axis respectively and angular velocity compensation ; The mechanical torque is then calculated by calculating the virtual asynchronous machine converter output, the real-time input power on the AC side, and the virtual rotor speed compensation. ; Electromagnetic torque and mechanical torque After the subtraction operation, the virtual rotor angular velocity compensation is calculated through the adaptive inertia link. , and then calculate the synchronous speed To adjust the mechanical torque , and the rated angular velocity of the microgrid is obtained by subtracting , and then the electrical angle of the asynchronous motor is obtained through the integral link .

[0006] Furthermore, the virtual rotor angular velocity compensation amount The expression is as follows:

[0007] Where: J represents the inertia coefficient of the temperature change of the electric thermal energy system, D represents the friction damping coefficient, T L is the rotor mechanical torque, Electromagnetic torque.

[0008] Furthermore, the expression of the temperature-varying inertia coefficient J is as follows:

[0009] in: , is the positive inertia compensation coefficient, T indoor is the indoor temperature, T norm Set the temperature for the indoor temperature, T max and T min are the maximum and minimum values ​​of the indoor temperature, J0 is the initial inertia coefficient, is the indoor temperature relative to time The rate of change.

[0010] Furthermore, the virtual asynchronous machine control method based on the coupled time scale model of the dynamic response of electric energy and thermal energy controls the real-time power balance on the electric energy side and improves the dynamic frequency response when the power demand on the AC side changes as follows: By collecting the voltage and current signals on the AC side, the input power on the AC side is calculated in real time ; The power signal is controlled by the power outer loop and the current inner loop to generate the AC electromotive force reference values ​​on the d-axis and q-axis respectively. and , AC electromotive force reference value and , and then the AC electromotive force reference value of the asynchronous motor in the abc reference system is obtained through Park transformation ; The AC electromotive force reference value of the asynchronous motor under the abc reference system is modulated by PWM to generate the switch trigger signal of the rectifier, thereby realizing the control of the virtual asynchronous machine converter, and then realizing real-time power balance control on the electric energy side, and improving the frequency dynamic response when the power demand on the AC side changes.

[0011] Furthermore, the virtual asynchronous machine small signal modeling method is used to verify the stability of the real-time control method.

[0012] Furthermore, the process of verifying the stability of the real-time control method by using the virtual asynchronous machine small signal modeling method is as follows: The small signal equation of the virtual asynchronous machine is as follows:

[0013]

[0014]

[0015] Where: , , , , , , , , , , , , , In the formula, , , , is the stator current and voltage variation under dq axis, and are the steady-state values ​​of the stator current under the d-axis and q-axis respectively. To describe the stator current variation Change from rated angular velocity The relationship matrix between them. To describe the stator current variation The relationship matrix of To describe the stator current variation The change in the rotor d-axis magnetic flux And stator current change Rotor angular velocity change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The stator current variation The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The relationship matrix of To describe the change in rotor angular velocity Change from rated angular velocity The relationship matrix between them; To describe the change in rotor angular velocity The stator current variation The relationship matrix between them; To describe the change in rotor angular velocity The change of the rotor d-axis magnetic flux The relationship matrix between them; To describe the change in rotor angular velocity The relationship matrix of matrix The stator q-axis current at the rated operating point is and stator d-axis current Composition, Matrix In is the speed at the rated operating point, and are the stator and rotor resistances, respectively, , and are the rotor leakage inductance, stator leakage inductance and magnetizing inductance respectively. Similarly, the matrix Also determined by the system structure parameters and the angular velocity at the rated operating point After calculation, the matrix In is the rotor d-axis magnetic flux at the rated operating point, . is the inertia coefficient, is the active proportional coefficient, is the damping coefficient; The inverter output voltage amplitude The small signal equation between is expressed as follows:

[0016] Where: , ,

[0017] In the formula, is the stator voltage change, is the stator voltage change relative to the inverter voltage change The relationship matrix of is the stator voltage change relative to the phase angle change Relationship Matrix , Respectively m The steady-state values ​​of the power angle and voltage of each inverter, diag Indicates that the matrix is ​​a diagonal matrix; Ignoring energy loss, according to the power balance on both sides of the AC / DC integrated circuit, we have:

[0018] , , , They are stator d-axis voltage, stator q-axis voltage, stator d-axis current, and stator q-axis current respectively; , is the DC side voltage and current; is the DC side capacitance value, is the time rate of change of the DC side voltage, is the output current on the DC side; Then, the change in DC side voltage is The small signal model of is written as:

[0019] Where: , , ,

[0020] In the formula, Indicates the change in DC side voltage. , , , Respectively represent the DC side voltage change relative to the stator current change , stator voltage change , itself and DC side output current The relationship matrix of the variation. and are the steady-state values ​​of the stator voltage on the d-axis and q-axis respectively, C is the DC side capacitance value. , are the steady-state values ​​of the DC side voltage and output current respectively. and are the steady-state values ​​of the stator current on the d-axis and q-axis respectively; The electric boiler is modeled as a fixed resistor , varies with load conditions and is described as follows:

[0021] Therefore, the small signal state space of the electric boiler is:

[0022] Where:

[0023] In the formula, To reflect the change of DC side voltage The DC output current Admittance matrix of the relation; The small signal state space equation of the virtual asynchronous machine converter is expressed as follows:

[0024] In the formula, is the state variable of the interconnect converter, is its derivative with respect to time, is the state variable on the AC side State variables of interconnected converters The relationship matrix between them; is a relation matrix reflecting the relationship between the state variables of the interconnected converters; Where:

[0025] , , ; In the formula, is the power angle change, is the change in angular velocity, is the voltage change, , are the line and load current changes on the AC side, is the selection matrix; The complete AC side small signal model is written as:

[0026] In the formula, To reflect the relationship matrix between the state variables of the AC measurement, is the interconnect converter state variable and AC side state variables The relationship matrix between them; Where:

[0027] , , , ,

[0028] In the formula, for M×M The identity matrix of size, for M×1 A column vector whose size is all 1. M is the number of inverters, is the angular velocity change The change of inverter dq axis current The relationship matrix between is the inverter voltage change The change of inverter dq axis current The relationship matrix between them. is the inverter angular velocity change The inverter voltage change The relationship matrix between them. is the inverter voltage change The relationship matrix of its own relationships. For the m The voltage steady-state operating point of the inverter. , Respectively m Active and reactive droop coefficients of each inverter, is the inverter angular velocity. is the power angle relationship matrix of multiple inverters, which is composed of the power angle relationship matrix of a single inverter Diagonal composition, Depend on and Decide, For the m The inverter power angle steady-state value. is the line incident matrix of the DC section, and the column elements corresponding to the line input or output in each row are 1 or 1, other elements are 0; , , , , ,

[0029] In the formula, It is used to reflect the relationship between the output current and power angle of the inverter under the dq axis, which is represented by the matrix Diagonal composition, From a single inverter , and and Decision, which are m The steady-state value of the output current of each inverter under the d-axis and q-axis and the sine and cosine values ​​of the power angle. It is used to reflect the angular velocity relationship. It is a diagonal matrix, and the diagonals of the matrix are ; Finally, the small signal model of the regional electric and thermal integrated system is obtained by combining the above formulas.

[0030] The small signal model is expressed as follows:

[0031] Here, The system matrix is ​​composed of a combination of the system matrix, which reflects all the relationships of the system. are all the state variables of the system; Here are: , .

[0032] The state variables of the AC side and interconnect converter state variables composition, The relationship matrix reflecting the communication side relationship , the relationship matrix reflecting the relationship between interconnected converters , reflecting the coupling relationship between the AC side and the interconnected converter side and Combination formation.

[0033] The present invention provides a real-time control method for an electric thermal energy system based on a virtual asynchronous machine, which uses heat with large inertia and electricity with fast response speed to control the virtual asynchronous machine, verifies the small signal stability of the proposed control strategy, and realizes real-time power balance. It has the following characteristics: The method provides the ability to achieve real-time power balance control. A coupled time scale model based on the dynamic response of electricity and heat at different time scales is established. The model uses the building heating system as a virtual energy storage system to adjust the heat output with large inertia according to the power change of the power grid. This process realizes real-time power balance in the regional electric-thermal coupled energy system, thereby improving the frequency stability on the AC side. This achievement first proposed a virtual asynchronous machine control strategy based on the regional electric-thermal coupled energy system, which improves the frequency dynamic response when the power demand on the AC side changes. Compared with the virtual synchronous generator control strategy, the virtual asynchronous machine strategy has the advantages of adapting to load power changes and suppressing frequency oscillations. In order to obtain better temperature response, an adaptive inertia control method based on temperature change is proposed, and a unified expression is established. When the indoor temperature deviates from the set temperature, a relatively large inertia is used, and when the indoor temperature stabilizes to the set temperature as soon as possible, a relatively small inertia is used, so that a better temperature response can be obtained.

[0034] Compared with the prior art, the advantages of the present invention are: The virtual asynchronous machine control proposed in the present invention does not need to know the grid frequency and rotor frequency, because the frequency will automatically change with the change of power. When the load power changes, the virtual asynchronous machine control can automatically optimize the frequency of the AC measurement. Different from the minute-level and hour-level power optimization scheduling methods of traditional electric-thermal coupled energy systems, the virtual asynchronous machine control proposed in the present invention using large inertia heat and fast response speed electricity can achieve real-time power balance. Compared with the virtual synchronous generator control strategy, the virtual asynchronous machine method has the advantages of adapting to load power changes and suppressing frequency oscillations. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0035] Figure 1 This is a structural diagram of the regional electric-thermal integrated energy system; Figure 2 This is a schematic diagram of the model architecture of the electric boiler heating system of the present invention; Figure 3 It is a schematic diagram of the charging and discharging model of the virtual energy storage system; Figure 4 It is a schematic diagram of the virtual asynchronous machine converter model; Figure 5 It is the asynchronous machine structure and vector schematic diagram; Figure 6is the schematic diagram of the power and current controller; Figure 7 Schematic diagram of a virtual asynchronous machine controller. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] A real-time control method for an electric thermal energy system based on a virtual asynchronous machine, comprising: S1. Establish a coupled time scale model of the dynamic response of electric and thermal energy in a regional electric-thermal coupled energy system based on different time scales; S2. A virtual asynchronous machine control method based on a coupled time scale model of the dynamic response of electric energy and thermal energy is used to achieve real-time power balance control on the electric energy side and improve the frequency dynamic response when the power demand on the AC side changes; S3. Based on the real-time temperature in the building, the inertia coefficient of temperature change is used to adjust the temperature response of the thermal energy side to achieve real-time control of the electric thermal energy system.

[0039] The steps S1 / S2 / S3 are performed sequentially; Figure 1 This is a structural diagram of the regional electric-thermal integrated energy system; The regional electric-thermal coupling energy system is composed of a microgrid, a virtual asynchronous machine converter, an electric boiler system and a building heat load. The virtual asynchronous machine converter converts the AC power of the AC microgrid into DC power to meet the needs of the electric boiler system; the electric boiler, as a DC load, converts electrical energy into thermal energy to provide the required heat for the building. Through this connection structure, the regional electrothermal coupling energy system realizes the efficient conversion and coordinated utilization of electrical energy and thermal energy, and improves the overall energy utilization efficiency.

[0040] The coupled time scale model of the dynamic response of electrical and thermal energy includes an electric boiler system model, a building thermal model, and a virtual energy storage system model; S1-1. The construction process of the electric boiler system model is as follows: Figure 2 As shown: Electric power consumed by thermal storage electric boiler P eb As shown below:

[0041] Where: P i1 The electric power of the direct heating part of the thermal storage electric boiler; P i2 The electric power of the heat storage part of the heat storage electric boiler; It is a sign of abandoning wind and solar power. b =1 indicates that wind and solar power are abandoned. b =0 means no wind or solar power curtailment.

[0042] Electric power of electric boiler P i1 and P i2 The expression is as follows:

[0043]

[0044] Where: I i1 This is the current of the direct heating part of the thermal storage electric boiler. I i2 is the current and voltage of the heat storage part of the heat storage electric boiler U It is 220V.

[0045] Output heating power of thermal storage electric boiler for:

[0046] The heat storage electric boiler and its heat storage device have a high heat release power, which is linearly related to the power consumption, namely:

[0047]

[0048] Where: Q 01 ( t ) is the thermal output power of the thermal storage electric boiler; is a constant representing the electric-heat conversion efficiency of the electric boiler; Q i2 ( t ) is the heat storage power of the heat storage device of the heat storage electric boiler.

[0049] The heat that the radiator receives from the heat network pipes Q sink As follows:

[0050] Heat gain in buildings Q HEAT for:

[0051] Where: density and specific heat C w The water flow rate can be obtained by looking up the water temperature table. V w It can be measured using a flow meter. is the water temperature difference between the inlet and outlet of the radiator, S sink is the total heat dissipation area of ​​the radiator, is the logarithmic mean temperature difference.

[0052] S1-2. The construction process of the building thermal model is as follows: Ignoring factors such as the heat dissipated by indoor activities of people in the building, heat transfer between adjacent rooms and other influencing factors, by applying the building heat balance equation, a quantitative mathematical relationship can be established between indoor and outdoor temperature, outdoor temperature and heating demand. Its expression is as follows:

[0053]

[0054]

[0055] In the formula, Representation variables Relative time The amount of change. Q HEAT is the heat output from the radiator, T HEAT is the radiator temperature, T indoor is the indoor temperature, T outdoor is the outdoor temperature, M dot is the air mass flow rate through the radiator, Q loss is the heat loss, M air For indoor air quality, c is the specific heat capacity of air at constant pressure, R eq is the loss temperature equivalent resistance.

[0056] S1-3. Construction of virtual energy storage system model, such as Figure 3 shown.

[0057] Considering the main heat exchange factors, the specific building heat balance equation is:

[0058] Where: is the total heat absorbed indoors, For heat transfer through the exterior wall, For heat transfer through the roof, is the solar radiation that passes through the windows.

[0059]

[0060] Where: is the heat lost through heat transfer from the exterior wall, The heat added by heat transfer through the exterior wall is Orientation i Total wall area (m2), For buildings i Heat transfer coefficient of the wall.

[0061]

[0062] Where: is the heat lost through roof heat transfer, The heat added by heat transfer through the roof, Orientation j Total roof area (m2), building j Heat transfer coefficient of the roof.

[0063]

[0064] Where: is the total heat transfer through the window, is the glass transmission coefficient, is the solar intensity coefficient, For buildings l The heat transfer coefficient of the window, for i The total amount of solar radiation when the wall is facing the sun (kW / m2).

[0065] When the indoor temperature rises ( dT indoor >0), the heat energy of the indoor air will also increase , which shows that dt There is energy storage during this period.

[0066] On the contrary, when the indoor temperature decreases ( dT indoor <0), the heat energy of indoor air is reduced , which means that in dt Energy is released during this period. Therefore, the thermal storage capacity of the building and the indoor temperature are adjusted according to the dynamic electricity demand.

[0067]

[0068] in, Indicated in t The power flow of charging and discharging the virtual energy storage system at each moment, where a positive value indicates discharging and a negative value indicates charging; Indicates the heat load demand when the virtual energy storage system performs power balance adjustment; Represents the heat load demand when the virtual energy storage system (VESS) is not used for power balancing.

[0069] Based on the physical model of the asynchronous motor, the mathematical model of the asynchronous motor is derived, and then the control model of the virtual asynchronous machine is derived. Before studying the mathematical model of the asynchronous motor, in order to exclude the influence of mechanical manufacturing installation, environmental factors, etc., it is necessary to make certain idealized assumptions about the asynchronous motor. The assumptions are as follows: Assumption 1: Ignore spatial harmonics and cogging effects, the three-phase windings are symmetrical, the electrical angles differ by 120° in space, and the generated magnetic force is distributed around the air gap according to the sinusoidal law; Assumption 2: Ignore magnetic circuit saturation, and the self-inductance and mutual inductance of each winding are linear; Assumption 3: Ignore core loss; Assumption 4: The influence of temperature and frequency on winding resistance is not considered; Assumption 5: When the motor rotor is transferred to the stator side, the number of turns per phase is equal before and after the transfer.

[0070] The virtual asynchronous machine control method of the regional electrothermal coupling system in step S2 derives a calculation formula from the basic structure and principle of the asynchronous machine, thereby obtaining a virtual asynchronous machine converter model, such as Figure 4 The specific process is as follows: S2-1. Basic characteristics of asynchronous motors.

[0071] The slip rate of an asynchronous motor is defined as:

[0072] Where: , and They are synchronous frequency, rotor frequency and slip frequency respectively.

[0073] Using space vector notation, stator voltage of an asynchronous machine with a short-circuited rotor The and rotor voltage equations are shown below:

[0074]

[0075] Where: p represents the differential operator, r and s represent the rotor and stator respectively, and the stator inductance , rotor inductance , where L lr , L ls and L m They are rotor leakage inductance, stator leakage inductance and magnetizing inductance respectively. represents the stator resistance, represents the rotor resistance, represents the stator current, represents the rotor current, is the system angular velocity, j is the complex imaginary part, and =-1.

[0076] Air Gap Power P ag The calculation formula is:

[0077] In the formula, represents the absolute value of the rotor current, For slip.

[0078] The output of the asynchronous motor converts mechanical power ( P mech ) and torque ( T e ) is estimated as follows:

[0079]

[0080] Assuming the grid frequency decreases, the power / torque waveform corresponding to the grid frequency moves to the left. In this case, the rotor speed will also converge to a new unknown equilibrium point. That is, the frequency of the induction motor will automatically change with the change of power. There is no need to know the grid frequency and the rotor frequency because they change and adjust at the same time.

[0081] S2-2. Physical model of asynchronous motor Figure 5 As shown, the matrix form of the voltage equation of the asynchronous motor is as follows (and using the differential operator p Substitute the differential symbol):

[0082] Where: is the stator resistance, , , Represent the three-phase stator phase voltages, , , is the instantaneous value of the three-phase stator current, , , is the instantaneous value of the three-phase stator flux. is the rotor resistance, , , The instantaneous value of the three-phase rotor current, , , is the instantaneous value of the three-phase rotor flux, , , Represent the three-phase rotor phase voltages respectively.

[0083] The flux of the three-phase stator and rotor windings is equal to the sum of their self-inductance flux and the mutual inductance flux of other windings. The flux equation of each phase winding can be expressed as:

[0084] Where: , , is the three-phase stator flux, , , is the three-phase rotor flux, , , , , , is the self-inductance of each winding, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , is the mutual inductance of the corresponding windings, , , , , , is the current flowing through each winding.

[0085] L ms It is used to indicate the maximum mutual inductance flux linked to one phase of the stator winding, which is called stator mutual inductance; L mr It is used to indicate the maximum mutual inductance flux linked to one phase of the rotor winding, which is called rotor mutual inductance.

[0086] Since the number of turns of the stator and rotor windings are equal after the motor conversion, the mutual inductance flux between the windings passes through the air gap, the magnetic resistance is equal, and the stator mutual inductance is equal to the rotor mutual inductance, that is:

[0087] For each phase winding, the sum of the motor mutual inductance and the leakage flux constitutes the inter-link flux. Therefore, the self-inductance of each stator phase is as follows:

[0088] Where: L ls is the leakage inductance of the stator.

[0089] The self-inductance of each phase of the rotor is as follows:

[0090] Where: L lr is the leakage inductance of the rotor.

[0091] The mutual inductance between motor windings can be divided into two categories: one is the mutual inductance between motor stators and stators, and between rotors and rotors; the other is the mutual inductance between stators and rotors.

[0092] The first type of mutual inductance is constant because the position of each phase winding in space is fixed, so the mutual inductance is constant. Assuming that the phase difference of the three-phase winding axis in space is 120°, and assuming that the air gap flux of the motor is distributed according to a sine function, the expression of the first type of mutual inductance can be obtained.

[0093]

[0094]

[0095] In the formula, is a cosine function, which is used to compensate for the angle difference between phases Since the relative position between any phase of the motor stator and any phase of the motor rotor is not fixed, the second kind of mutual inductance is not a constant, but a spatial angular displacement variable. The function is as follows:

[0096]

[0097]

[0098] The equivalent model of the asynchronous motor on the dq axis is as follows:

[0099]

[0100]

[0101]

[0102] Where: and They are synchronous frequency and slip frequency respectively; , , , , and They are dq axis voltage, current and stator flux respectively.

[0103] The control expressions of the rotor and stator flux links can be described as:

[0104]

[0105]

[0106]

[0107] Where: stator inductance And the rotor inductance .

[0108] Virtual torque of virtual synchronous machine It is expressed as follows:

[0109] Furthermore, it is assumed that in steady state, the virtual rotor flux is aligned with the d-axis, i.e. The virtual torque can be restated as

[0110] The formula and equation simplify to:

[0111]

[0112] In the formula, is the slip frequency, is the change in angular velocity.

[0113] According to the above formula, we can get

[0114]

[0115] Substituting in, we get:

[0116]

[0117] Where: .

[0118] The mechanical power of an asynchronous motor is expressed as follows:

[0119] Where J represents the inertia coefficient and temperature change of the electrothermal energy system, D represents the friction damping coefficient, is the virtual rotor electrical angular velocity, T m is the mechanical torque, and:

[0120] That is, the mechanical power of the asynchronous motor is as follows:

[0121] T L is the rotor mechanical torque, which is calculated as:

[0122] in, is the exchange power between the DC side and the AC side, It is the virtual regulator part. Rated set point of AC microgrid frequency, is the active power coefficient.

[0123] If we Set as virtual rotor frequency With initial value The deviation, the formula becomes:

[0124] It has the characteristics of a first-order low-pass filter in the frequency domain, namely:

[0125] Synchronous speed and angle reference can be , and the angular velocity difference It is concluded as follows:

[0126]

[0127] When the indoor temperature deviates from the set temperature, According to the adaptive inertia coefficient formula, a relatively large inertia is used, which can weaken the impact of temperature changes on user comfort; the set temperature is 20℃ On the other hand, when the indoor temperature stabilizes to the set temperature as soon as possible, , using a relatively small inertia according to the adaptive inertia coefficient formula.

[0128] To this end, the temperature-dependent adaptive inertia coefficient The following conclusions were drawn:

[0129] in: , is the positive inertia compensation coefficient, is a symbolic function. T indoor is the indoor temperature, T norm Set the temperature for the room temperature, T max and T min are the maximum and minimum values ​​of the indoor temperature respectively, and J0 is the initial inertia coefficient.

[0130] In summary, the schematic diagrams of the power and current controller and the virtual asynchronous machine controller are as follows: Figure 6 and Figure 7 As shown in Figure 1, a virtual asynchronous machine control structure for a regional electric-heat coupled energy system is given, where the electric-heat network is not directly connected but is linked together through multiple coupling devices. In this case, the heating network is simulated as a system of partial differential-algebraic hybrid equations. Its step size is usually between a few seconds and a few minutes and is recorded as a large step size component set.

[0131] The solution steps of the numerical differentiation method can be summarized as follows: ①Select the iteration step size h ; ②Select an appropriate numerical solution method; ③ Establish a recursive formula for numerical solution, and gradually deduce the solution at subsequent moments from the current sampling value and the estimated value at the previous moment. In order to reduce the estimation error and make the model calculation converge, the key is to select a small step size or use a high-precision algorithm.

[0132] Considering the characteristics of distribution parameters, the power network is simulated as a system of partial differential algebraic equations, which requires a simulation step size of microseconds. Therefore, it can be set separately as a small step size component.

[0133] The virtual asynchronous machine control method based on the electric energy and thermal energy dynamic response coupling time scale model is as follows: Based on the three-phase current abc of the asynchronous motor, the dq axis current is obtained through park transformation; Then the electromagnetic torque is calculated by the current in the dq axis respectively and angular velocity compensation ; The mechanical torque is then calculated by calculating the virtual asynchronous machine converter output, the real-time input power on the AC side, and the virtual rotor speed compensation. ; Electromagnetic torque and mechanical torque After the subtraction operation, the virtual rotor angular velocity compensation is calculated through the adaptive inertia link. , and then calculate the synchronous speed To adjust the mechanical torque , and the rated angular velocity of the microgrid is obtained by subtracting , and then the electrical angle of the asynchronous motor is obtained through the integral link .

[0134] The virtual asynchronous machine control method based on the coupled time scale model of the dynamic response of electric energy and thermal energy controls the real-time power balance on the electric energy side and improves the dynamic frequency response when the power demand on the AC side changes as follows: By collecting the voltage and current signals on the AC side, the input power on the AC side is calculated in real time ; The power signal is controlled by the power outer loop and the current inner loop to generate the AC electromotive force reference value on the dq axis. and , AC electromotive force reference value and , and then the AC electromotive force reference value of the asynchronous motor in the abc reference system is obtained through the inverse Park transformation ; The AC electromotive force reference value of the asynchronous motor under the abc reference frame is modulated by PWM to generate the switch trigger signal of the rectifier, realizing the control of the virtual asynchronous machine converter, and then realizing the real-time power balance control on the power side, improving the frequency dynamic response when the power demand on the AC side changes. In addition, the electrical angle used in Park transformation Output by virtual asynchronous machine control method, such as Figure 7 Through this control structure, the virtual asynchronous machine can simulate the dynamic behavior of the traditional asynchronous generator and enhance the stability of the system.

[0135] Furthermore, the method includes a process of verifying the stability of the method by using a virtual asynchronous machine small signal modeling method.

[0136] The process of the virtual asynchronous machine small signal modeling method is as follows: The small signal equation of the virtual asynchronous machine (VAM) is as follows:

[0137]

[0138]

[0139] Where: , , , , , , , , , , , , , In the formula, , , , is the stator current and voltage variation under dq axis, and are the steady-state values ​​of the stator current under the d-axis and q-axis respectively. To describe the stator current variation Change from rated angular velocity The relationship matrix between them. To describe the stator current variation The relationship matrix of To describe the stator current variation The change of the rotor d-axis magnetic flux And stator current change Rotor angular velocity change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The stator current variation The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The relationship matrix of To describe the change in rotor angular velocity Change from rated angular velocity The relationship matrix between them; To describe the change in rotor angular velocity The stator current variation The relationship matrix between them; To describe the change in rotor angular velocity The change of the rotor d-axis magnetic flux The relationship matrix between them; To describe the change in rotor angular velocity The relationship matrix.

[0140] matrix The stator q-axis current at the rated operating point is and stator d-axis current Composition, Matrix In is the speed at the rated operating point, and are the stator and rotor resistances, respectively, , and are the rotor leakage inductance, stator leakage inductance and magnetizing inductance respectively. Similarly, the matrix Also determined by the system structure parameters and the angular velocity at the rated operating point After calculation, the matrix In is the rotor d-axis magnetic flux at the rated operating point, . is the inertia coefficient, is the active proportional coefficient, is the damping coefficient.

[0141] The inverter output voltage amplitude The small signal equation between is expressed as follows:

[0142] Where: , ,

[0143] In the formula, is the stator voltage change, is the stator voltage change relative to the inverter voltage change The relationship matrix of is the stator voltage change relative to the phase angle change Relationship Matrix , Respectively m The steady-state values ​​of the power angle and voltage of each inverter, diag Indicates that the matrix is ​​diagonal.

[0144] Ignoring energy loss, according to the power balance on both sides of the AC / DC integrated circuit, we have:

[0145] , , , They are stator d-axis voltage, stator q-axis voltage, stator d-axis current, and stator q-axis current respectively; , is the DC side voltage and current; is the DC side capacitance value, is the time rate of change of the DC side voltage, is the output current on the DC side.

[0146] Then, the change in DC side voltage is The small signal model of is written as:

[0147] Where: , , ,

[0148] In the formula, Indicates the change in DC side voltage. , , , Respectively represent the DC side voltage change relative to the stator current change , stator voltage change , itself and DC side output current The relationship matrix of the variation. and are the steady-state values ​​of the stator voltage on the d-axis and q-axis respectively, C is the DC side capacitance value. , are the steady-state values ​​of the DC side voltage and output current respectively. and are the steady-state values ​​of the stator current on the d-axis and q-axis respectively.

[0149] The electric boiler is modeled as a fixed resistor , varies with load conditions and is described as follows:

[0150] Therefore, the small signal state space of the electric boiler is:

[0151] Where:

[0152] In the formula, To reflect the change of DC side voltage The DC output current The admittance matrix of the relation.

[0153] The small signal state space equation of the virtual asynchronous machine converter is expressed as follows:

[0154] In the formula, is the state variable of the interconnect converter, is its derivative with respect to time, is the state variable on the AC side State variables of interconnected converters The relationship matrix between them; It is a relationship matrix that reflects the relationship between the state variables of the interconnected converters.

[0155] Where:

[0156] , , ; In the formula, is the power angle change, is the change in angular velocity, is the voltage change, , are the line and load current changes on the AC side, is the selection matrix.

[0157] The complete AC side small signal model is written as:

[0158] In the formula, To reflect the relationship matrix between the state variables of the AC measurement, is the interconnect converter state variable and AC side state variables The relationship matrix between them.

[0159] Where:

[0160] , , , ,

[0161] In the formula, for M×M The identity matrix of size, for M×1 A column vector whose size is all 1. M is the number of inverters, is the angular velocity change The change of inverter dq axis current The relationship matrix between is the inverter voltage change The change of inverter dq axis current The relationship matrix between them. is the inverter angular velocity change The inverter voltage change The relationship matrix between them. is the inverter voltage change The relationship matrix of its own relationships. For the m The voltage steady-state operating point of the inverter. , Respectively m Active and reactive droop coefficients of each inverter, is the inverter angular velocity. is the power angle relationship matrix of multiple inverters, which is composed of the power angle relationship matrix of a single inverter Diagonal composition, Depend on and Decide, For the m The inverter power angle steady-state value. is the line incident matrix of the DC section, and the column elements corresponding to the line input or output in each row are 1 or 1, other elements are 0; , , , , ,

[0162] In the formula, It is used to reflect the relationship between the output current and power angle of the inverter under the dq axis, which is represented by the matrix Diagonal composition, From a single inverter , and and Decision, which are m The steady-state value of the output current of each inverter under the d-axis and q-axis and the sine and cosine values ​​of the power angle. It is used to reflect the angular velocity relationship. It is a diagonal matrix, and the diagonals of the matrix are .

[0163] Finally, the small signal model of the regional electric and thermal integrated system is obtained by combining the above formulas.

[0164] The small signal model is expressed as follows:

[0165] Here, The system matrix is ​​composed of a combination of the system matrix, which reflects all the relationships of the system. are all the state variables of the system.

[0166] Here are: , .

[0167] The state variables on the AC side and interconnect converter state variables composition, The relationship matrix reflecting the communication side relationship , the relationship matrix reflecting the relationship between interconnected converters , reflecting the coupling relationship between the AC side and the interconnected converter side and Combination formation.

[0168] 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 replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time control method for an electric thermal energy system based on a virtual asynchronous machine, characterized in that: include: Establish a coupled time scale model of the dynamic response of electric and thermal energy in a regional electric-thermal coupled energy system based on different time scales; A virtual asynchronous machine control method based on a coupled time scale model of the dynamic response of electric energy and thermal energy is used to achieve real-time power balance control on the electric energy side and improve the frequency dynamic response when the power demand on the DC side changes. The virtual asynchronous machine control method based on the electric energy and thermal energy dynamic response coupling time scale model is as follows: Based on the three-phase current abc of the asynchronous motor, the dq axis current is obtained through park transformation; Then the electromagnetic torque is calculated by the current in the dq axis respectively and angular velocity compensation ; The mechanical torque is then calculated by calculating the virtual asynchronous machine converter output, the real-time input power on the AC side, and the virtual rotor speed compensation. ; Electromagnetic torque and mechanical torque After the subtraction operation, the virtual rotor angular velocity compensation is calculated through the adaptive inertia link. , and then calculate the synchronous speed To adjust the mechanical torque , and the rated angular velocity of the microgrid is obtained by subtracting , and then the electrical angle of the asynchronous motor is obtained through the integral link ; Based on the real-time temperature inside the building, the inertia coefficient of temperature change is used to adjust the temperature response on the thermal energy side to achieve real-time control of the electric thermal energy system.

2. According to claim 1, a real-time control method for an electric thermal energy system based on a virtual asynchronous machine is characterized in that: The virtual rotor angular velocity compensation The expression is as follows: Where: J represents the inertia coefficient of the temperature change of the electric thermal energy system, D represents the friction damping coefficient, T L is the rotor mechanical torque, Electromagnetic torque.

3. A real-time control method for an electric thermal energy system based on a virtual asynchronous machine according to claim 2, characterized in that: The expression of the temperature-varying inertia coefficient J is as follows: in: , is the positive inertia compensation coefficient, Tindoor is the indoor temperature, Tnorm is the indoor temperature setting temperature, Tmax and Tmin are the maximum and minimum values ​​of the indoor temperature respectively, and J0 is the initial inertia coefficient. is the indoor temperature relative to time The rate of change.

4. The real-time control method of an electric thermal energy system based on a virtual asynchronous machine according to claim 1 is characterized in that: The virtual asynchronous machine control method based on the coupled time scale model of the dynamic response of electric energy and thermal energy controls the real-time power balance on the electric energy side and improves the dynamic frequency response when the power demand on the AC side changes as follows: By collecting the voltage and current signals on the AC side, the input power on the AC side is calculated in real time ; The power signal is controlled by the power outer loop and the current inner loop to generate the AC electromotive force reference values ​​on the d-axis and q-axis respectively. and , AC electromotive force reference value and , and then the AC electromotive force reference value of the asynchronous motor in the abc reference system is obtained through Park transformation ; The AC electromotive force reference value of the asynchronous motor under the abc reference system is modulated by PWM to generate the switch trigger signal of the rectifier, thereby realizing the control of the virtual asynchronous machine converter, and then realizing real-time power balance control on the electric energy side, and improving the frequency dynamic response when the power demand on the AC side changes.

5. A verification method for a real-time control method of an electric thermal energy system based on a virtual asynchronous machine according to any one of claims 1 to 4, characterized in that: The virtual asynchronous machine small signal modeling method is used to verify the stability of the real-time control method.

6. The verification method of a real-time control method of an electric thermal energy system based on a virtual asynchronous machine according to claim 5 is characterized in that: The process of using the virtual asynchronous machine small signal modeling method to verify the stability of the real-time control method is as follows: The small signal equation of the virtual asynchronous machine is as follows: Where: ， ， ， ， ， ， ， ， ， ， ， ， ， In the formula, , , , is the stator current and voltage variation under dq axis, and are the steady-state values ​​of the stator current under the d-axis and q-axis respectively, To describe the stator current variation Change from rated angular velocity The relationship matrix between To describe the stator current variation The relationship matrix of To describe the stator current variation The change of the rotor d-axis magnetic flux And stator current change Rotor angular velocity change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the stator current variation The stator voltage change The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The stator current variation The relationship matrix between them; To describe the change in the rotor d-axis magnetic flux The relationship matrix of To describe the change in rotor angular velocity Change from rated angular velocity The relationship matrix between them; To describe the change in rotor angular velocity The stator current variation The relationship matrix between them; To describe the change in rotor angular velocity The change of the rotor d-axis magnetic flux The relationship matrix between them; To describe the change in rotor angular velocity The relationship matrix of matrix The stator q-axis current at the rated operating point is and stator d-axis current Composition, Matrix In is the speed at the rated operating point, and are the stator and rotor resistances, respectively, , and are the rotor leakage inductance, stator leakage inductance and magnetizing inductance respectively. Similarly, the matrix Also determined by the system structure parameters and the angular velocity at the rated operating point After calculation, the matrix In is the rotor d-axis magnetic flux at the rated operating point, , is the inertia coefficient, is the active proportional coefficient, is the damping coefficient; The inverter output voltage amplitude The small signal equation between is expressed as follows: Where: ， ， In the formula, is the stator voltage change, is the stator voltage change relative to the inverter voltage change The relationship matrix of is the stator voltage change relative to the phase angle change Relationship Matrix , Respectively m The steady-state values ​​of the power angle and voltage of each inverter, diag Indicates that the matrix is ​​a diagonal matrix; Ignoring energy loss, according to the power balance on both sides of the AC / DC integrated circuit, we have: , , , They are stator d-axis voltage, stator q-axis voltage, stator d-axis current, and stator q-axis current respectively; , is the DC side voltage and current; is the DC side capacitance value, is the time rate of change of the DC side voltage, is the output current on the DC side; Then, the change in DC side voltage is The small signal model of is written as: Where: ， ， ， In the formula, Indicates the change in DC side voltage. , , , Respectively represent the DC side voltage change relative to the stator current change , stator voltage change , itself and DC side output current The relationship matrix of the variation, and are the steady-state values ​​of the stator voltage on the d-axis and q-axis respectively, C is the DC side capacitance value, , are the steady-state values ​​of DC side voltage and output current respectively, and are the steady-state values ​​of the stator current on the d-axis and q-axis respectively; The electric boiler is modeled as a fixed resistor , varies with load conditions and is described as follows: Therefore, the small signal state space of the electric boiler is: Where: In the formula, To reflect the change of DC side voltage The DC output current Admittance matrix of the relation; The small signal state space equation of the virtual asynchronous machine converter is expressed as follows: In the formula, is the state variable of the interconnect converter, is its derivative with respect to time, is the state variable on the AC side State variables of interconnected converters The relationship matrix between them; is a relation matrix reflecting the relationship between the state variables of the interconnected converters; Where: ， ， ； In the formula, is the power angle change, is the change in angular velocity, is the voltage change, , are the line and load current changes on the AC side, is the selection matrix; The complete AC side small signal model is written as: In the formula, To reflect the relationship matrix between the state variables of the AC measurement, is the interconnect converter state variable and AC side state variables The relationship matrix between them; Where: ， ， ， ， In the formula, for M×M The identity matrix of size, for M×1 A column vector whose size is all 1. M is the number of inverters, is the angular velocity change The change of inverter dq axis current The relationship matrix between is the inverter voltage change The change of inverter dq axis current The relationship matrix between is the inverter angular velocity change The inverter voltage change The relationship matrix between is the inverter voltage change The relationship matrix of its own relationship, For the m The voltage steady-state operating point of the inverter is , Respectively m Active and reactive droop coefficients of each inverter, is the inverter angular velocity, is the power angle relationship matrix of multiple inverters, which is composed of the power angle relationship matrix of a single inverter Diagonal composition, Depend on and Decide, For the m The inverter power angle steady-state value, is the line incident matrix of the DC section, and the column elements corresponding to the line input or output in each row are 1 or 1, other elements are 0; ， ， ， ， ， In the formula, It is used to reflect the relationship between the output current and power angle of the inverter under the dq axis, which is represented by the matrix Diagonal composition, From a single inverter , and and Decision, which are m The steady-state value of the output current of the inverter under the d-axis and q-axis and the sine and cosine values ​​of the power angle, the matrix It is used to reflect the angular velocity relationship. It is a diagonal matrix, and the diagonals of the matrix are ; Finally, combining the above formulas, the small signal model of the regional electric and thermal integrated system is obtained: The small signal model is expressed as follows: Here, The system matrix is ​​composed of a combination of the system matrix, which reflects all the relationships of the system. are all the state variables of the system; Here are: ， ， The state variables on the AC side and interconnect converter state variables composition, The relationship matrix reflecting the communication side relationship , the relationship matrix reflecting the relationship between interconnected converters , reflecting the coupling relationship between the AC side and the interconnected converter side and Combination formation.

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