A virtual inertia hybrid control system, method, and storage medium

Abstract

The present invention discloses a virtual inertia hybrid control system, method and storage medium, which relates to the field of new energy system control, and solves the technical problem in the prior art that the energy density of electrochemical energy storage is low and it is difficult to meet the demand for long-term steady-state load support; the present invention models multiple types of voltages in the electro-chemical reaction process of a DC power station by using an analytical model method to obtain the working voltage of a single electrolytic cell; the current and open-circuit voltage of the battery are obtained by performing secondary modeling on the working voltage through a Thevenin equivalent circuit; the power of the wind turbine is obtained through a wind power generation unit; the MPPT control strategy of a photovoltaic power generation unit is determined, and a DC grid anti-interference model is constructed based on an AC power grid to balance the fluctuating voltage; the control strategy is determined by simulating the virtual inertia of a hydrogen storage unit; the variable droop coefficient is obtained, and the virtual inertia control strategy of an electrical energy storage unit is determined by using the variable droop coefficient control; the present invention improves the stability of a hydrogen-electric combined energy storage DC power station.

Description

Technical Field

[0001] The present invention belongs to the field of new energy system control, relates to virtual inertia hybrid control technology, and specifically is a virtual inertia hybrid control system, method, and storage medium based on hydrogen-electric integration. Background Art

[0002] With the increase in the proportion of distributed new energy power generation such as photovoltaic power generation, the power system is undergoing a structural transformation, and the volatility and controllability of these energy sources are relatively weak. Therefore, the power system not only needs to maintain power balance but also has higher regulation capabilities to cope with the power instability problems caused by new energy. Microgrid technology is a key way to improve the efficiency of distributed power sources and has important economic and social value; hydrogen energy storage, as a green and environmentally friendly energy storage method, has the advantages of high energy density and long storage time and is an important way to absorb renewable energy.

[0003] In the prior art, due to its low energy density, limited cycle life, and high large-scale deployment cost, electrochemical energy storage is difficult to meet the requirements of long-term steady-state load support; at the same time, hydrogen energy storage has insufficient dynamic response capabilities and lacks inertia in the system, making it have great limitations in coping with the fluctuations of renewable energy output and load mutations.

[0004] The present invention provides a virtual inertia hybrid control system, method, and storage medium to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a virtual inertia hybrid control system, method, and storage medium to solve the technical problem that in the prior art, electrochemical energy storage is difficult to meet the requirements of long-term steady-state load support due to its low energy density, limited cycle life, and high large-scale deployment cost.

[0006] To achieve the above object, the first aspect of the present invention provides a virtual inertia hybrid control system, including: a model construction module and a virtual inertia control module;

[0007] Model construction module: Use the analytical model method to model various types of voltages in the electrochemical reaction process of the DC power station respectively to obtain the working voltage of a single electrolytic cell; perform secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery; wherein, the DC power station includes: a grid-connected unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen energy storage unit, a DC load unit, and an electrical energy storage unit;

[0008] Virtual inertia control module: Obtain the power of the wind turbine through the wind power generation unit; Determine the MPPT control strategy of the photovoltaic power generation unit, and construct a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage; Determine the control strategy through the virtual inertia of the hydrogen storage unit; Obtain the variable droop coefficient, and use the variable droop coefficient control to determine the virtual inertia control strategy of the electrical energy storage unit.

[0009] Preferably, the multi-type voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration overvoltage, and the operating voltage of a single electrolytic cell;

[0010] The model of the Nernst voltage is: In the formula, is the electrolysis voltage applied to the electrode, ΔS is the entropy change in the hydrogen fuel cell reaction, and ΔS < 0, n is the number of electrons transferred in the reaction, F is the Faraday constant, are the partial pressures of hydrogen and oxygen at the reaction interface respectively, is the water activity between the electrode and the membrane, T fc is the fuel cell reaction temperature, R is the ideal molar gas constant, T ref is the reference temperature under standard conditions;

[0011] The model of the activation overvoltage is: In the formula, i fc is the PEMFC current density, i fcO，A is the anodic half-reaction exchange current density, i fcO，C is the cathodic half-reaction exchange current density; a A and a C are the charge transfer coefficients of the anode and cathode respectively;

[0012] The model of the ohmic overvoltage is: In the formula, I mem is the thickness of the proton exchange membrane, σ mem is the proton exchange membrane conductivity;

[0013] The model of the concentration overvoltage is: In the formula, i fc,lim is the maximum current density; B is the diffusion coefficient, reflecting the rate of ion diffusion;

[0014] The model of the operating voltage of a single electrolytic cell is: E el，cell = E rev + E act + E ohm + E diff .

[0015] It should be noted that the specific values of the charge transfer coefficients of the anode and cathode depend on the magnitude of the activation energy barrier in the electrochemical reaction.

[0016] Preferably, obtaining the current and open-circuit voltage of the battery by performing secondary modeling on the operating voltage through the Thevenin equivalent circuit includes:

[0017] S100: Obtain the battery terminal voltage and label it as U bat , polarization voltage U bat,p , ohmic resistance R of the battery bat,ohm , label the electrochemical polarization resistance of the battery as R bat，p , and label the electrochemical polarization capacitance of the battery as C bat，p ;

[0018] S200: Add parallel resistors and capacitors based on the Rint model;

[0019] S300: Calculate the current of the single-cell battery through the formula ; In the formula, t represents the operating time of the battery; the derivative of the polarization voltage with respect to the operating time t represents the rate of change of the polarization voltage with time, reflecting the change amount of the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change of the battery polarization voltage with time;

[0020] Calculate the open-circuit voltage of the single-cell battery through the formula U bat，oc = I bat ·R bat，ohm + U bat，p + U bat . It should be noted that the direction flowing out from the positive electrode of the battery is defined as the positive direction of the current.

[0021] In the present invention, by using the Thevenin equivalent circuit model to model the single-cell lithium titanate battery and adding parallel resistors and capacitors based on the Rint model, it is more conducive to reflecting the resistance-capacitance characteristics of the battery.

[0022] Preferably, obtaining the power of the wind turbine by the wind power generation unit includes:

[0023] Obtain the pitch angle of the wind turbine and label it as β, label the radius of the wind wheel as R

[0024] , label the rotational speed of the wind turbine as ω f , and label the wind speed upstream of the wind wheel as V m ; w ;

[0025] Calculate the tip speed through the formula ;

[0026] Calculate the wind energy utilization coefficient through the formula

[0027] ;

[0028] By formula The power of the wind turbine is calculated; where ρ is the density of air.

[0029] It should be noted that, based on the model and theoretical basis of the energy storage system, the construction of the power generation module is further proposed to describe the energy capture mechanism of the wind turbine and its power output characteristics.

[0030] Preferably, the determining of the MPPT control strategy of the photovoltaic power generation unit includes:

[0031] Obtain the reverse saturation current and label it as I sat , the basic charge is marked as q, the diode characteristic factor is marked as A, the Boltzmann constant is marked as k, and the absolute temperature of the battery is marked as T pv , the parallel resistor is marked as R sh , the series resistor is marked as R pv,s , the output voltage of the solar cell is marked as U pv ; Photocurrent is marked as I L ;

[0032] Through simultaneous equations Calculate the current I flowing through the diode dio , battery leakage current I sh And the output current I of the solar cell pv ;

[0033] The current flowing through the diode, the battery leakage current and the output current of the solar cell calculated by the formula are used as the MPPT control strategy for photovoltaic power generation.

[0034] Preferably, the method of constructing a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage includes:

[0035] The moment of inertia and damping coefficient of the synchronous machine are marked as J and D respectively, and the mechanical power generated by the prime mover and the electromagnetic power injected into the grid are marked as P respectively. m , P e , the rated angular speed of the generator is marked as ω n ;

[0036] Adjust the fan speed ω m , through the formula Calculate the rotor motion formula of the synchronous generator;

[0037] Get the reference value and measured value of the distributed power input bus current and mark them as I o,ref and I o ; The virtual capacitance and virtual damping coefficient are marked as C vir and D vir, denote the actual voltage of the DC bus as U dc , denote the rated voltage of the DC bus as U dc,n ; among them, U dc - U dc,n represents the gap between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation;

[0038] When U dc - U dc,n is not 0, the anti-interference model of the DC power grid is:

[0039] Judge whether U dc - U dc,n is less than 0; if yes, increase the output current through Ohm's law to offset the voltage drop; if not, reduce the current output to offset the voltage rise.

[0040] The present invention simulates the inertia of the synchronous generator through the virtual inertia coefficient C vir The virtual inertia coefficient is related to the inertia characteristics of the system and determines the response speed of the system to voltage fluctuations; combined with the principle of the synchronous generator, the swing characteristics of the rotor during the disturbance are reflected by calculating the rotor motion formula of the synchronous generator; by constructing an expression similar to the rotor motion formula of the synchronous generator, the DC bus voltage has a certain anti-interference ability.

[0041] Preferably, the determination of the control strategy by simulating the virtual inertia of the hydrogen storage unit includes:

[0042] Judge whether the system is in the off-grid mode;

[0043] If yes, the selector connects to port 1, and the control strategy is set as: the hydrogen storage unit participates in forming the grid, obtains the reference current through virtual inertia control and the voltage outer loop, and then generates the control signal through the current inner loop; by selecting appropriate virtual capacitance, virtual damping coefficient and parameters, the slow characteristics of the hydrogen storage unit are simulated, and the bus voltage is maintained at the rated value through the secondary voltage regulation link;

[0044] If not, the selector connects to port 2, and the control strategy is set as: the hydrogen storage unit works as a grid-following unit, the system receives the power reference instruction issued by the control center, and smooths the instruction through the low-pass filtering link; the power reference instruction of the hydrogen storage unit is generated after filtering, and then generates the control signal through the current inner loop.

[0045] It should be noted that while generating the control signal in the current inner loop, the voltage outer loop is only used to maintain the stability of the terminal voltage of the hydrogen storage device itself, rather than regulating the DC bus voltage; in the grid-connected mode, the hydrogen storage unit mainly cooperates with the power grid to ensure the smooth execution of the power instruction, so as to balance the system economy and equipment safety.

[0046] In the present invention, due to the consistency of the virtual inertia control strategies of the hydrogen storage unit and the grid-connected unit, both of them improve the inertia of the bus voltage by controlling the current input to the DC bus; and the command is smoothed through a low-pass filtering link, avoiding damage to the PEM electrolyzer or equipment caused by slow response speed.

[0047] Preferably, the obtaining of the variable droop coefficient includes:

[0048] Obtain the initial droop coefficient and label it as K o , and label the exponential voltage adjustment coefficient as K e ; extract the voltage change rate δ through a high-pass filter, and label the voltage change rate threshold as δ o ;

[0049] Through the system of equations Calculate to obtain the variable droop coefficient that is exponential.

[0050] It should be noted that the setting of the voltage change rate threshold is adjusted and determined according to the impact of the voltage change rate on the safety and stability of the system, simulation experiments, control strategies, and system performance requirements.

[0051] In the present invention, the electrochemical energy storage with a large magnification and small capacity is used as fast energy storage, which plays a key role in the voltage suppression control. By using the variable droop coefficient control, the short-term additional power generation capacity of the battery is fully utilized to provide virtual inertia for the DC microgrid.

[0052] To achieve the above object, a second aspect of the present invention provides a virtual inertia hybrid control method, including:

[0053] Adopt the analytical model method to respectively model multiple types of voltages in the electrochemical reaction process of the DC power station to obtain the working voltage of a single electrolyzer; wherein, the DC power station includes: a grid-connected unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen storage unit, a DC load unit, and an energy storage unit;

[0054] Perform secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery;

[0055] Obtain the power of the wind turbine through the wind power generation unit;

[0056] Determine the MPPT control strategies of the photovoltaic power generation unit and the wind power generation unit, and determine the control strategy by simulating the virtual inertia of the hydrogen storage unit;

[0057] Utilize the variable droop coefficient control to determine the virtual inertia control strategy of the energy storage unit.

[0058] To achieve the above object, a third aspect of the present invention provides a virtual inertia hybrid control storage medium, on which a computer-readable storage medium is stored. When the computer-readable storage medium is executed by a processor, a virtual inertia hybrid control system as described in the first aspect above is implemented.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. The present invention uses a control strategy based on virtual synchronous generator (VSG) and variable droop coefficient virtual inertia hybrid for the DC power station system, which ensures the voltage stability of the system on a short time scale, improves the reliability of grid-connected operation on a long time scale, improves the DC bus voltage stability of the power station, and enhances the inertia response of the system; the virtual inertia control strategy based on VSG and variable droop coefficient is applicable to grid-connected units, hydrogen storage units and electrical energy storage units respectively, enhances the inertia response of the system, and improves the stability of the system; in the island state, the virtual inertia hybrid control technology can effectively cope with the fluctuations of wind and solar power generation, improve the DC bus voltage stability of the power station, and verify the feasibility of the hybrid control strategy; the hydrogen-electric energy storage DC power station has the characteristics of high energy density, environmental friendliness and strong flexibility compared with the pure electrical energy storage power station, and is effectively combined with renewable energy to provide long-term stable energy storage and release.

[0061] 2. The hydrogen energy storage unit in the present invention provides high-density long-term energy support, and the battery energy storage unit realizes the power balance of the system and the stability of the bus voltage on a short time scale through its fast dynamic response ability; in addition, the virtual inertia control technology significantly improves the dynamic response ability and anti-interference performance of the DC system by simulating the inertia characteristics of traditional synchronous generators; the present invention significantly improves the dynamic response ability and stability of the hydrogen-electric combined energy storage DC power station, effectively solves the problems of insufficient inertia and low energy efficiency, and provides important technical support for the access and efficient utilization of high-proportion renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is the topology diagram of the DC power station of the present invention;

[0064] Figure 2 It is the schematic diagram of the virtual inertia control of the grid-connected unit of the present invention;

[0065] Figure 3 Schematic diagram of virtual inertia control for the hydrogen storage unit of the present invention;

[0066] Figure 4 Schematic diagram of virtual inertia control for the electrical energy storage unit of the present invention;

[0067] Figure 5 Schematic diagram of power variation with different current feed - forward coefficients of the present invention;

[0068] Figure 6 Schematic diagram of voltage variation with different current feed - forward coefficients of the present invention;

[0069] Figure 7 Voltage variation diagram with different virtual capacitances of the present invention;

[0070] Figure 8 Voltage variation diagram with different virtual damping coefficients of the present invention;

[0071] Figure 9 Output power diagram of each unit of the DC power station of the present invention;

[0072] Figure 10 Voltage - current state change diagram of the DC power station of the present invention;

[0073] Figure 11 System simulation waveform diagram of the DC power station of the present invention;

[0074] Figure 12 Schematic diagram of the process of virtual inertia hybrid control of the present invention. Detailed implementation manners

[0075] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0076] Please refer to Figure 1 , an embodiment of the first aspect of the present invention provides a virtual inertia hybrid control system, including: a model construction module and a virtual inertia control module;

[0077] Model construction module: Using the analytical model method to respectively model multiple types of voltages in the electro - chemical reaction process of the DC power station to obtain the working voltage of a single electrolytic cell; performing secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open - circuit voltage of the battery; wherein, the DC power station includes: a grid - connection unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen storage unit, a DC load unit, and an electrical energy storage unit;

[0078] Virtual inertia control module: Obtain the power of the wind turbine through the wind power generation unit; Determine the MPPT control strategy of the photovoltaic power generation unit, and construct a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage; Determine the control strategy through the virtual inertia of the simulated hydrogen storage unit; Obtain the variable droop coefficient, and use the variable droop coefficient control to determine the virtual inertia control strategy of the electrical energy storage unit.

[0079] Please refer to Figure 2 , and the model for constructing the Nernst voltage is: In the formula, is the electrolysis voltage applied to the electrode, ΔS is the entropy change in the hydrogen fuel cell reaction, and ΔS < 0, n is the number of electrons transferred in the reaction, F is the Faraday constant, are the partial pressures of hydrogen and oxygen at the reaction interface respectively, is the water activity between the electrode and the membrane, T fc is the fuel cell reaction temperature, R is the ideal molar gas constant, T ref is the reference temperature under standard conditions;

[0080] The model for constructing the activation overvoltage is: In the formula, i fc is the PEMFC current density, i fcO，A is the exchange current density of the anodic half-reaction, i fcO,C is the exchange current density of the cathodic half-reaction; a A and a C are the charge transfer coefficients of the anode and cathode respectively;

[0081] The model for constructing the ohmic overvoltage is: In the formula, I mem is the thickness of the proton exchange membrane, σ mem is the proton exchange membrane conductivity;

[0082] The model for constructing the concentration overvoltage is: In the formula, i fc,lim is the maximum current density; B is the diffusion coefficient, reflecting the rate of ion diffusion;

[0083] The model for constructing the working voltage of a single electrolytic cell is: E el，cell = E rev + E act + E ohm + E diff ;

[0084] S100: Obtain the battery terminal voltage and label it as U bat , the polarization voltage U bat,p , the ohmic resistance R of the battery bat,ohm , label the electrochemical polarization resistance of the battery as R bat,p, and marking the electrochemical polarization capacitance of the battery as C bat，p ;

[0085] S200: Adding a parallel resistor and capacitor based on the Rint model;

[0086] S300: Through the formula Calculate the current of the single cell; where t represents the operating time of the battery; the derivative of the polarization voltage with respect to the operating time t represents the rate of change of the polarization voltage with time, reflecting the change amount of the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change of the battery polarization voltage with time;

[0087] Through the formula U bat，oc = I bat ·R bat，ohm + U bat，p + U bat Calculate the open-circuit voltage of the single cell;

[0088] Obtain the pitch angle of the wind turbine and mark it as β, mark the radius of the wind wheel as R f , mark the rotational speed of the fan as ω m , and mark the wind speed upstream of the wind wheel as V w ;

[0089] Through the formula Calculate the tip speed;

[0090] Through the formula Calculate the wind energy utilization coefficient;

[0091] Through the formula Calculate the power of the wind turbine; where ρ is the density of air;

[0092] Obtain the reverse saturation current and mark it as I sat , mark the elementary charge amount as q, the diode characteristic factor as A, the Boltzmann constant as k, and the absolute temperature of the battery as T pv , mark the parallel resistor as R sh , mark the series resistor as R pv，s , mark the output voltage of the solar cell as U pv ; mark the photocurrent as I L ;

[0093] By solving the simultaneous equations Calculate the current I flowing through the diode dio , the battery leakage current I sh and the output current I of the solar cell pv ;

[0094] Take the current flowing through the diode, the battery leakage current, and the output current of the solar cell obtained by formula calculation as the MPPT control strategy for photovoltaic power generation;

[0095] Mark the moment of inertia and damping coefficient of the synchronous machine as J and D respectively, and mark the mechanical power generated by the prime mover and the electromagnetic power injected into the grid as P m 、P e , and mark the rated angular velocity of the generator as ω n ;

[0096] Retrieve the fan speed ω m , and obtain the rotor motion formula of the synchronous generator through the formula ;

[0097] Obtain the reference value and measured value of the input bus current of the distributed power source and mark them as I o,ref and I o ; Mark the virtual capacitance and virtual damping coefficient as C vir and D vir respectively, and record the actual voltage of the DC bus as U dc , and record the rated voltage of the DC bus as U dc，n ; Among them, U dc -U dc，n represents the gap between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation;

[0098] When U dc -U dc，n is not 0, the anti-interference model of the DC power grid is:

[0099] Judge whether U dc -U dc，n is less than 0; if so, increase the output current through Ohm's law to offset the voltage drop; if not, reduce the current output to offset the voltage rise.

[0100] For example, take the electrolytic cell as an example to analyze its mathematical model, obtain the number of electrons transferred in the reaction and mark it as n, the Faraday constant is 96485 C / mol, and the electrolytic voltage applied to the electrode is marked as The ideal molar gas constant is 8.314 pa·m 3 / mol ·k; Mark the partial pressures of hydrogen and oxygen at the reaction interface as respectively. The water activity between the electrode and the membrane is 1. Mark the charge transfer coefficients of the anode and cathode as 2 and 0.5 respectively. Mark the PEMFC current density, the maximum current density, and the half-reaction exchange current densities of the anode and cathode as i fc 、i fc,lim 、ifcO,A 、i fcO,C Mark the fuel cell reaction temperature as T fc and mark the entropy change in the hydrogen fuel cell reaction as ΔS, and the value range of ΔS is less than 0;

[0101] The Nernst voltage is calculated through the formula ;

[0102] The activation overvoltage is calculated through the formula ;

[0103] The ohmic overvoltage is calculated through the formula ;

[0104] The concentration overvoltage is calculated through the formula ;

[0105] The working voltage of the single electrolytic cell is calculated through the formula E el，cell = E rev + E act + E ohm + E diff ;

[0106] Retrieve the fan speed ω m and calculate the rotor motion formula of the synchronous generator through the formula ; where the swing characteristic of the rotor refers to the oscillation or swing generated by the rotor when a disturbance occurs (such as load change, short circuit, etc.), and this oscillation can be observed through the change of the rotor angle;

[0107] When there is an imbalance between the mechanical power P m and the electromagnetic power (braking power) P e , the rotor will start to swing. At this time, P m - P e will generate a non-zero braking power or driving power, resulting in an angular acceleration of the rotor, and the angle of the rotor will change with time, showing oscillation characteristics; where the damping coefficient D is due to factors such as mechanical friction and wind resistance, causing the angular velocity of the rotor to gradually slow down over time; the moment of inertia J is the inertial resistance of the rotor to angular acceleration, and the greater the inertia, the slower the rotor's response to oscillation;

[0108] Under the action of the moment of inertia J and the damping D, the oscillation of the rotor will be attenuated and finally tend to be stable;

[0109] In the AC system, the kinetic energy is released through the rotor to suppress the sudden change of frequency. Correspondingly, in the DC system, the electrical energy can be released through the capacitor to suppress the sudden change of the bus voltage. A DC grid anti-interference model is constructed according to the DC grid and AC grid correspondence table (as shown in Table 1):

[0110] Table 1 Correspondence Table between DC Power Grid and AC Power Grid

[0111]

[0112] Through a dynamic process similar to rotor inertia control, the virtual inertia coefficient C vir enables the system to delay the rate of voltage change when the voltage fluctuates, that is, the system will simulate an inertial response and provide a smooth voltage change process, thereby effectively reducing the impact of external interference on the DC bus voltage;

[0113] D vir is the virtual damping coefficient, which increases the damping of the voltage control system and helps to suppress voltage fluctuations, especially when the voltage deviation is large U dc -U dc,n represents the gap between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation, and is used to adjust the control behavior of the system; the virtual damping coefficient D vir makes the current output adjust quickly when the voltage deviation is large, thereby suppressing voltage fluctuations. The larger the voltage deviation, the greater the adjustment torque generated by the system, and the voltage stability is quickly restored. Through this mechanism, the system can quickly compensate for voltage fluctuations and avoid long-term voltage fluctuations;

[0114] In summary, when there is a voltage deviation, the virtual damping control will adjust the current according to the voltage deviation, thereby stabilizing the voltage. In addition, with virtual inertia control, the system can adjust the response speed according to the rate of voltage change to prevent excessive voltage fluctuations; that is, virtual inertia provides a short-term buffer, and virtual damping suppresses voltage fluctuations, enhancing the anti-interference ability of the system.

[0115] It should be noted that due to charge drive, conduction resistance, and changes in reactant concentration, there are activation, ohmic, and concentration overvoltages in the electrolysis and power generation processes, and the actual reaction voltage is inconsistent with the theoretical voltage; in the present invention, the Nernst voltage and each overvoltage in the electrochemical reaction process are modeled by using an analytical model method; the electrical response time of the PEM stack is about 50 ms, which is much smaller than the response time of the global system. Therefore, a static model is constructed, and the electrolyzer and fuel cell are regarded as nonlinear resistance elements without considering the dynamic process therein.

[0116] Please refer to Figures 3 - 4 to determine whether the system is in the off-grid mode;

[0117] If so, the selector connects to port one, and the control strategy is set as follows: the hydrogen storage unit participates in grid formation, obtains the reference current through virtual inertia control and voltage outer loop, and then generates the control signal through the current inner loop; by selecting appropriate virtual capacitance, virtual damping coefficient and parameters, the slow characteristics of the hydrogen storage unit can be simulated, and the bus voltage can be maintained at the rated value through the secondary voltage regulation link;

[0118] If not, the selector connects to port two, and the control strategy is set as follows: the hydrogen storage unit operates as a grid-following unit. The system receives the power reference instruction issued by the control center and smooths the instruction through a low-pass filter to avoid damage to the PEM electrolyzer or equipment due to slow response speed; the power reference instruction of the hydrogen storage unit is generated after filtering and then generates the control signal through the current inner loop;

[0119] Build a simulation model of the virtual inertia hybrid control system for a DC power station with hydrogen energy storage through simulation software;

[0120] Obtain the initial droop coefficient and mark it as K o , and mark the exponential voltage adjustment coefficient as K e ; extract the voltage change rate δ through high-pass filtering and mark the voltage change rate threshold as δ o ;

[0121] Calculate the variable droop coefficient which is exponential through the equations

[0122] For example, there is consistency in the virtual inertia control strategies of the hydrogen storage unit and the AC grid-connected unit, both of which improve the inertia of the bus voltage by controlling the current input to the DC bus. The difference is that the power change speed of the hydrogen storage unit is slower and the control object is the DC / DC converter; the improved virtual inertia control strategy of the hydrogen storage unit is as Figure 3 ; Figure 3 where C PEM,vir , D PEM,vir are the virtual capacitance and virtual damping coefficient respectively; I PEM is the output current of the hydrogen storage unit, that is, the sum of the fuel cell and electrolyzer currents. The direction of flowing into the bus is specified as positive, and I PEM > 0 indicates that the PEMFC is in the working mode and the PEMWE is closed or in the hot standby state; is the current change obtained by droop control; the current correction amount generated by secondary voltage regulation is ΔI PEM,sec , when the system operates in the off-grid mode, the selector connects to port one, the hydrogen storage unit participates in grid formation, obtains the reference current through virtual inertia control and voltage outer loop, and then generates the control signal through the current inner loop; by selecting appropriate virtual capacitance, virtual damping coefficient and PI parameters, the slow characteristics of the hydrogen storage unit can be simulated, and the bus voltage can be maintained at the rated value through the secondary voltage regulation link. ​

[0123] As a fast energy storage, the electrochemical energy storage with high magnification and small capacity plays a key role in the voltage regulation control. By varying the droop coefficient control, the short-term additional power generation capacity of the battery is fully utilized to provide virtual inertia for the DC microgrid.

[0124] The improved control strategy is as follows Figure 5 shown:

[0125] Figure 5 In which, P pv and P wind are the photovoltaic power generation power and the wind power generation power respectively. P grid (K ff = 0), P grid (K ff = 0.3) and P grid (K ff = 2 / 3) are the active power consumed by the AC grid-connected unit when the current feed-forward coefficient is K ff = 0, K ff = 0.3 and K ff = 2 / 3 respectively. Among them, the direction flowing to the DC bus is the positive power direction; the abscissa represents time, and the ordinate represents the power change, that is, the consumed power of the AC grid-connected unit and the injected power of the power generation unit change with time;

[0126] T bat is the time constant of the first-order high-pass filter. The voltage change rate δ can be extracted through high-pass filtering; K u is the exponential variable droop coefficient;

[0127]

[0128] In the formula: K o is the initial droop coefficient and also the droop coefficient at stability; K e is the exponential voltage adjustment coefficient; δ, δ o are the voltage change rate and its threshold respectively. Among them, δ o = 0.4.

[0129] Please refer to Figures 5 - 11 , retrieve the built DC power station system, turn off the electric energy storage unit module, and verify the influence of obtaining the virtual capacitance and virtual damping coefficient by the current feed-forward coefficient on the virtual inertia control; among them, Figure 9 in which, P pv and P wind have the same meanings as the corresponding letters in Figure 5 . P fc is the hydrogen fuel cell power generation power; P grid and P bat are the active power of the AC grid-connected unit and the electric energy storage power respectively. The direction flowing to the DC bus is the positive power direction; Pel and P load are the power of the electrolytic cell and the power of the 5G base station respectively, and are always non-positive;

[0130] For example, according to the built DC power station system, turn off the energy storage unit module, turn off the energy storage unit, and keep the virtual capacitor C of the grid-connected unit g，vir = 1 / 600 F and the virtual damping coefficient D g，vir = 60, and separately test the influence of the current feedforward coefficient on the virtual inertia control of the grid-connected unit. The current feedforward coefficients K ff are taken as 0, 0.3, and 2 / 3 respectively. The simulation results of the power of each unit and the change of the DC bus voltage are as shown in Figure 6 and 7 ;

[0131] From Figure 6 and 7 , it can be seen that at the beginning of the simulation, the wind and light output rapidly rises to about 2.5 MW, and the AC power grid quickly absorbs the power to stabilize the voltage. At 0.5 s, the photovoltaic power generation drops to about 0.8 MW, causing a power deficit of about 1.2 MW, and the power of the grid-connected unit increases; when K ff = 0, that is, when there is no current feedforward link, the voltage rapidly rises at the beginning of the simulation, and the change amount exceeds 5% (75 V) of the rated voltage, and then slowly drops back. The power of the grid-connected unit has a slight overshoot; at 0.5 s, the voltage drops by about 45 V and then slowly rises. At this time, the inertial effect of the grid-connected converter is not obvious; as K ff increases, the power change of the AC power grid gradually becomes faster, the voltage fluctuation of the DC bus becomes smaller, and the corresponding power overshoot also becomes smaller. The maximum value of K ff should not exceed 2 / 3. Excessive K ff will cause the control to reverse and fail;

[0132] Turn off the energy storage unit, keep the current feedforward coefficient K ff as 2 / 3 and the virtual damping coefficient D g,vir as 60, and compare the effect differences between the virtual inertia control of the grid-connected unit and the traditional droop control. The virtual capacitor capacitances C g，vir are taken as 1 / 1500 F and 1 / 600 F respectively to test the influence of the virtual capacitor capacitance value on the virtual inertia control. The simulation result of the change of the DC bus voltage is Figure 7 ; keep the current feedforward coefficient K ff as 2 / 3 and the virtual capacitor C g，vir as 1 / 600 F, and take D g,vir as 10, 60, and 200 respectively to test the influence of the virtual damping coefficient on the virtual inertia control. The simulation result of the change of the DC bus voltage is Figure 8 ;

[0133] Use Matlab / Simulink simulation software to build a virtual inertial hybrid control system simulation model of a hydrogen-powered energy storage DC power station;

[0134] In the simulation, the wind and solar power generation power is small and the surplus is insufficient for hydrogen production. The system changes when PEMWE changes from normal hydrogen production to hot standby state, the switching of the system support hot standby mode, and the response of the system to wind and solar fluctuations in hot standby state are tested; working condition design: simulation time 4s, initial light intensity 300W / m2, wind speed 7m / s, temperature 25℃, load 0.4MW, initial battery SOC 30.01%, light intensity drops to 100W / m2 in 1s, wind speed drops to 5m / s, and PEMWE shutdown is not considered. The simulation results are as follows Figure 9 , 10 As shown;

[0135] In the process of PEMFC supporting hot standby, if the light intensity drops to 0W / m2 in 2.5s as condition 1, and rises to 400W / m2 as condition 2, the simulation results are as follows: Figure 11 As shown;

[0136] from Figure 11 It can be seen from (a) and (b) that in condition 1, the photovoltaic power generation drops to 0 at 2.5s, and the PEMFC continues to increase power to support the load power consumption. After short-term dynamic support from the power storage, the output power drops to 0, and the bus voltage recovers to 1500V after a brief drop. Figure 11 (c)(d) In condition 2, the photovoltaic power generation increases at 2.5s, and the surplus wind and solar power generation is higher than the PEMWE hot standby power. Since the hydrogen storage power changes slowly in the control, the electrolyzer will not start immediately, but the PEMFC power will gradually decrease to shutdown. After a short period of support from the power storage alone, the PEMWE will be released from the hot standby state and start to work normally, and the bus voltage will eventually stabilize at 1500V.

[0137] It should be noted that in order to clearly demonstrate the virtual inertia control effect and avoid voltage waveform overlap, the voltage in the figure is the voltage after low-pass filtering, and the low-pass filtering time constant is 0.001s.

[0138] See also Figure 12 The second aspect of the present invention provides a virtual inertia hybrid control method, comprising:

[0139] The analytical model method is used to model the multiple types of voltages in the electrochemical reaction process of the DC power station respectively to obtain the working voltage of the single electrolyzer; wherein the DC power station includes: a grid-connected unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen storage unit, a DC load unit and an electricity storage unit;

[0140] The working voltage is secondarily modeled through a Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery;

[0141] The power of the wind turbine is obtained through a wind power generation unit;

[0142] Determine the MPPT control strategies of the photovoltaic power generation unit and the wind power generation unit, and construct a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage;

[0143] Determine the control strategy through the virtual inertia of the simulated hydrogen storage unit;

[0144] Use variable droop coefficient control to determine the virtual inertia control strategy of the electrical energy storage unit.

[0145] An embodiment of the third aspect of the present invention provides a virtual inertia hybrid control storage medium, including: a computer-readable storage medium stored thereon, and when the computer-readable storage medium is executed by a processor, it implements a virtual inertia hybrid control system as described in the first aspect above.

[0146] Some of the data in the above formula are taken as their numerical values after removing the dimension, and the formula is a formula that is closest to the actual situation obtained through software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0147] The working principle of the present invention: The analytical model method is used to separately model multiple types of voltages in the electrochemical reaction process of the DC power station to obtain the working voltage of the single electrolytic cell; the working voltage is secondarily modeled through a Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery; the power of the wind turbine is obtained through a wind power generation unit; determine the MPPT control strategy of the photovoltaic power generation unit, and construct a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage; determine the control strategy through the virtual inertia of the simulated hydrogen storage unit; obtain the variable droop coefficient, and use variable droop coefficient control to determine the virtual inertia control strategy of the electrical energy storage unit.

[0148] The above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those of ordinary skill in the art should understand that the technical methods of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A virtual inertia hybrid control system, characterized in that, Including: A model construction module and a virtual inertia control module; Model construction module: Using the analytical model method to model various types of voltages in the electrochemical reaction process of the DC power station respectively to obtain the working voltage of a single electrolytic cell; performing secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery; wherein, the DC power station includes: a grid-connected unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen storage unit, a DC load unit, and an electrical energy storage unit; Virtual inertia control module: Obtaining the power of the wind turbine through the wind power generation unit; determining the MPPT control strategy of the photovoltaic power generation unit, and constructing a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage; determining the control strategy by simulating the virtual inertia of the hydrogen storage unit; obtaining a variable droop coefficient, and using the variable droop coefficient control to determine the virtual inertia control strategy of the electrical energy storage unit; The constructing a DC grid anti-interference model based on the AC grid to balance the fluctuating voltage includes: Mark the moment of inertia and damping coefficient of the synchronous machine as J and D respectively, and mark the mechanical power generated by the prime mover and the electromagnetic power injected into the grid as P m , P e , and mark the rated angular velocity of the generator as ω n ; Retrieve the fan speed ω m , through the formula calculate the rotor motion formula of the synchronous generator; Obtain the reference value and the measured value of the distributed power input bus current and mark them as I o,ref and I o ; Mark the virtual capacitance and the virtual damping coefficient as C vir and D vir , denote the actual voltage of the DC bus as U dc , and denote the rated voltage of the DC bus as U dc，n ; Among them, U dc -U dc，n represents the gap between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation; When U dc -U dc，n is not zero, the DC power grid anti-interference model is as follows: Determine U dc -U dc，n Whether it is less than 0; if yes, increase the output current through Ohm's law to offset the voltage drop; if no, reduce the current output to offset the voltage rise; The determining the control strategy by simulating the virtual inertia of the hydrogen storage unit includes: Judging whether the system is in the off-grid mode; If yes, the selector connects to port 1, and the control strategy is set as: the hydrogen storage unit participates in forming the grid, obtains the reference current through virtual inertia control and voltage outer loop, and then generates a control signal through the current inner loop; by selecting appropriate virtual capacitance, virtual damping coefficient and parameters to simulate the slow characteristics of the hydrogen storage unit, and making the bus voltage reach the rated value through the secondary voltage regulation link; If not, the selector connects to port 2, and the control strategy is set as: the hydrogen storage unit works as a grid-following unit, the system receives the power reference instruction issued by the control center, and smooths the instruction through a low-pass filtering link; the power reference instruction of the hydrogen storage unit is generated after filtering, and then generates a control signal through the current inner loop.

2. The virtual inertia hybrid control system according to claim 1, characterized in that The various types of voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration overvoltage, and the working voltage of a single electrolytic cell; The model of the Nernst voltage is as follows: In the formula, is the electrolytic voltage applied to the electrode, ΔS is the entropy change in the hydrogen fuel cell reaction, and ΔS < 0, n is the number of electrons transferred in the reaction, F is the Faraday constant, are the partial pressures of hydrogen and oxygen at the reaction interface respectively, is the water activity between the electrode and the membrane, T fc is the fuel cell reaction temperature, R is the ideal molar gas constant, T ref is the reference temperature under standard conditions; The model of the activation overvoltage is as follows: In the formula, i fc is the current density of the PEMFC, and i fcO,A is the exchange current density of the anodic half-reaction, and i fcO,C is the exchange current density of the cathodic half-reaction; a A and a C are the charge transfer coefficients of the anode and the cathode respectively; The model of the ohmic overvoltage is as follows: In the formula, I mem is the thickness of the proton exchange membrane, and σ mem is the conductivity of the proton exchange membrane; The model of the concentration overvoltage is as follows: where i fc,lim is the maximum current density; B is the diffusion coefficient, reflecting the rate of ion diffusion; The model of the operating voltage of the monomer electrolytic cell is: E el，cell = E rev + E act + E ohm + E diff .

3. A virtual inertial hybrid control system according to claim 1, characterized in that, The performing secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery includes: S100: Obtain the battery terminal voltage and label it as U bat , the polarization voltage U bat,p , the ohmic resistance R of the battery bat,ohm , label the electrochemical polarization resistance of the battery as R bat,p , and label the electrochemical polarization capacitance of the battery as C bat,p ; S200: Adding a parallel resistor and capacitor based on the Rint model; S300: Calculate the current of the single cell through the formula where t represents the running time of the battery; the derivative of the polarization voltage with respect to the running time t represents the rate of change of the polarization voltage with time, reflecting the change amount of the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change of the battery polarization voltage with time. The open-circuit voltage of the single battery is calculated through the formula U bat,oc = I bat ·R bat,ohm + U bat,p + U bat + U 4. A virtual inertia hybrid control system according to claim 1, wherein The obtaining the power of the wind turbine through the wind power generation unit includes: Obtain the pitch angle of the wind turbine and label it as β, label the radius of the wind turbine as R f , label the rotational speed of the fan as ω m , and label the wind speed upstream of the wind turbine as V w ; The tip speed is calculated by the formula ​ Through the formula Calculating the wind energy utilization coefficient; The power of the wind turbine is calculated through the formula ; where ρ is the density of air.

5. A virtual inertia hybrid control system according to claim 1, characterized in that, The determining the MPPT control strategy of the photovoltaic power generation unit includes: Obtain the reverse saturation current and label it as I sat , the elementary charge quantity is labeled as q, the diode ideality factor is labeled as A, the Boltzmann constant is labeled as k, and the absolute temperature of the battery is labeled as T pv , the shunt resistance is labeled as R sh , the series resistance is labeled as R pv,s , the output voltage of the solar cell is labeled as U pv ; the photocurrent is labeled as I L ; By solving the simultaneous equations the current I flowing through the diode is calculated dio , the battery leakage current I sh and the output current I of the solar cell pv ; Taking the current flowing through the diode, the battery leakage current, and the output current of the solar cell calculated by the formula as the MPPT control strategy of the photovoltaic power generation.

6. A virtual inertia hybrid control system according to claim 1, characterized in that, The obtaining the variable droop coefficient includes: Obtain the initial sag coefficient and label it as K o , and label the exponential voltage adjustment coefficient as K e ; Extract the voltage change rate δ through high-pass filtering, and label the voltage change rate threshold as δ o ; By a system of equations It is calculated to be an exponential variable droop coefficient.

7. A virtual inertia hybrid control method, applicable to a virtual inertia hybrid control system according to any one of claims 1-6, characterized in that, Including: Using the analytical model method to model various types of voltages in the electrochemical reaction process of the DC power station respectively to obtain the working voltage of a single electrolytic cell; wherein, the DC power station includes: a grid-connected unit, a wind power generation unit, a photovoltaic power generation unit, a hydrogen storage unit, a DC load unit, and an electrical energy storage unit; Performing secondary modeling on the working voltage through the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery; Obtaining the power of the wind turbine through the wind power generation unit; Determining the MPPT control strategies of the photovoltaic power generation unit and the wind power generation unit, and determining the control strategy by simulating the virtual inertia of the hydrogen storage unit; Determine the virtual inertia control strategy of the electrical energy storage unit by using variable droop coefficient control.

8. A virtual inertia hybrid control storage medium, on which a computer-readable storage medium is stored, characterized in that, When the computer-readable storage medium is executed by a processor, it implements a virtual inertia hybrid control system according to any one of claims 1-6.

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