Virtual inertia hybrid control system and method and storage medium

Abstract

The invention discloses a virtual inertia hybrid control system and method and a storage medium, relates to the field of new energy system control, and solves the technical problem that electrochemical energy storage in the prior art is low in energy density and difficult to meet long-term steady-state load support requirements. The method comprises the following steps: modeling multiple types of voltages in the electrochemical reaction process of the direct current station by adopting an analytical model method to obtain the working voltage of a single electrolytic cell; carrying out secondary modeling on the working voltage 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 the wind power generation unit; an MPPT control strategy of a photovoltaic power generation unit is determined, and a DC power grid anti-interference model is constructed based on an AC power grid to balance fluctuation voltage; determining a control strategy by simulating the virtual inertia of the hydrogen storage unit; obtaining a variable droop coefficient, and controlling and determining a virtual inertia control strategy of the electric storage unit by using the variable droop coefficient; the stability of the hydrogen-electricity combined energy storage direct current station is improved.

Description

Technical Field

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

[0002] With the increase in the proportion of distributed renewable energy generation such as photovoltaics, the power system is undergoing a structural transformation. These energy sources are less volatile and controllable. Therefore, the power system not only needs to maintain power balance, but also has a higher regulation capability to cope with the power instability caused by renewable 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] Electrochemical energy storage in existing technologies is difficult to meet the needs of long-term steady-state load support due to its low energy density, limited cycle life and high cost of large-scale deployment; at the same time, hydrogen energy storage has insufficient dynamic response capabilities and the system lacks inertia, which makes it have great limitations in dealing with fluctuations in renewable energy output and sudden load changes.

[0004] The present invention provides a virtual inertial 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; to this end, the present invention proposes a virtual inertial hybrid control system, method and storage medium, which are used to solve the technical problem that electrochemical energy storage in the prior art is difficult to meet the long-term steady-state load support requirements due to its low energy density, limited cycle life and high large-scale deployment cost.

[0006] To achieve the above object, a first aspect of the present invention provides a virtual inertial hybrid control system, comprising: a model building module and a virtual inertial control module; Model building module: The analytical model method is used to model the various types of voltages in the electrochemical reaction process of the DC power station, and the working voltage of the single electrolyzer is obtained; the working voltage is secondary modeled through the Thevenin equivalent circuit to obtain the current and open circuit voltage of the battery; wherein the DC power station includes: grid-connected unit, wind power generation unit, photovoltaic power generation unit, hydrogen storage unit, DC load unit and power storage unit; 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 build a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage; determine the control strategy by simulating 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 electricity storage unit.

[0007] Preferably, the multiple types of voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration difference overvoltage and working voltage of a single electrolyzer; 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, is the water activity between the electrode and the membrane, is the fuel cell reaction temperature, R is the ideal molar gas constant, is the reference temperature under standard conditions; The model of the activation overvoltage is: ; In the formula, is the PEMFC current density, is the half-reaction exchange current density of the anode, is the cathode half-reaction exchange current density; and are the charge transfer coefficients of the anode and cathode, respectively; The model of the ohmic overvoltage is: ; In the formula, is the thickness of the proton exchange membrane, is the conductivity of the proton exchange membrane; The model of the concentration overvoltage is: ; In the formula, is the maximum current density; B is the diffusion coefficient, which reflects the rate of ion diffusion; The model of the operating voltage of the monomer electrolyzer is: ; Among them, the multiple types of voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration difference overvoltage and the working voltage of a single electrolyzer.

[0008] It should be noted that the specific values ​​of the charge transfer coefficients of the anode and cathode depend on the size of the activation energy barrier in the electrochemical reaction.

[0009] Preferably, the method of performing secondary modeling on the operating voltage through the Thevenin equivalent circuit to obtain the current and open circuit voltage of the battery includes: S100: Get the battery terminal voltage and mark it as , polarization voltage , the ohmic resistance of the battery , the electrochemical polarization resistance of the battery is marked as , and the cell electrochemical polarization capacitance is labeled ; S200: Add parallel resistance and capacitance based on the Rint model; S300: By formula The current of the single cell is calculated; 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 at which the polarization voltage changes with time, reflecting the amount of change in the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change in the battery polarization voltage with time; By formula Calculate the open circuit voltage of the single cell.

[0010] It should be noted that the direction of current flowing out from the positive electrode of the battery is defined as the positive direction of current.

[0011] The present invention models a single lithium titanate battery by adopting the Thevenin equivalent circuit model, and adds parallel resistance and capacitance based on the Rint model, which is more conducive to reflecting the resistance-capacitance characteristics of the battery.

[0012] Preferably, obtaining the power of the wind turbine through the wind power generation unit includes: Get the pitch angle of the wind turbine and mark it as β, and mark the radius of the wind rotor as , the fan speed is marked as , and the wind speed upstream of the rotor is marked as ; By formula Calculate the blade tip speed; By formula Calculate the wind energy utilization coefficient; By formula Calculate the power of the wind turbine; where, is the density of air.

[0013] 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.

[0014] Preferably, the determining of the MPPT control strategy of the photovoltaic power generation unit includes: Obtain the reverse saturation current and mark it as , 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 , the parallel resistor is marked as , the series resistor is marked as , the output voltage of the solar cell is marked as ; Photocurrent is marked as ; Through simultaneous equations Calculate the current flowing through the diode , battery leakage current And the output current of the solar cell ; 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.

[0015] Preferably, the method of constructing a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage includes: 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 , the rated angular speed of the generator is marked as ; Adjust the fan speed , through the formula Calculate the rotor motion formula of the synchronous generator; Get the reference value and measured value of the distributed power input bus current and mark them as and ; The virtual capacitance and virtual damping coefficient are marked as and , the actual voltage of the DC bus is recorded as , the rated voltage of the DC bus is ;in, Indicates the difference between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation; when When it is not 0, the DC grid anti-interference model is: ; judge Is it less than 0? If yes, the output current is increased according to Ohm's law to offset the voltage drop; if no, the current output is reduced to offset the voltage rise.

[0016] The present invention uses a virtual inertia coefficient The inertia of the synchronous generator is simulated. 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 rotor motion formula of the synchronous generator is calculated to reflect the swing characteristics of the rotor during the disturbance. By constructing an expression similar to the rotor motion formula of the synchronous generator, the DC bus voltage has a certain anti-interference ability.

[0017] Preferably, the control strategy is determined by simulating the virtual inertia of the hydrogen storage unit, comprising: Determine whether the system is in off-grid mode; If yes, the selector is connected to port 1, and the control strategy is set as follows: the hydrogen storage unit participates in the network construction, obtains the reference current through the virtual inertia control and the voltage outer loop, and then generates the control signal through the current inner loop; the slow speed characteristics of the hydrogen storage unit are simulated by selecting appropriate virtual capacitors, virtual damping coefficients and parameters, and the bus voltage is kept at the rated value through the secondary voltage regulation link; If not, the selector is connected to port 2 and the control strategy is set as follows: the hydrogen storage unit works as a grid-connected unit, the system receives the power reference instruction issued by the control center, and smoothes the instruction through a low-pass filter; the power reference instruction of the hydrogen storage unit is generated after filtering, and then a control signal is generated through the current inner loop.

[0018] It should be noted that while the inner current loop generates control signals, the outer voltage loop is only used to maintain the stability of the terminal voltage of the hydrogen storage device itself, rather than to adjust 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 power commands, thereby taking into account both system economy and equipment safety.

[0019] In the present invention, since the virtual inertia control strategies of the hydrogen storage unit and the grid-connected unit are consistent, the inertia of the bus voltage is increased by controlling the current of the input DC bus; the instructions are smoothed by a low-pass filter link, thereby avoiding damage to the PEM electrolyzer or equipment due to slow response speed.

[0020] Preferably, obtaining the variable droop coefficient includes: Get the initial droop coefficient and label it as , the exponential voltage regulation factor is marked as ; Extract the voltage change rate through high-pass filtering , and the voltage change rate threshold is marked as ; Through the system of equations The calculated variable droop coefficient is an exponential one.

[0021] It should be noted that the voltage change rate threshold setting is adjusted and determined according to the impact of the voltage change rate on system safety and stability, simulation experiments, control strategies and system performance requirements.

[0022] The present invention uses electrochemical energy storage with large rate and small capacity as fast energy storage, which plays a key role in voltage control. By variable droop coefficient control, the short-term increase capacity of the battery is fully utilized to provide virtual inertia for the DC microgrid. To achieve the above object, a second aspect of the present invention provides a virtual inertia hybrid control method, comprising: 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; The operating voltage is modeled secondary through the Thevenin equivalent circuit to obtain the battery current and open circuit voltage; Obtaining power from the wind turbine through a wind power generation unit; Determine the MPPT control strategy 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; The virtual inertia control strategy of the power storage unit is determined by using variable droop coefficient control.

[0023] To achieve the above objectives, the third aspect of the present invention provides a virtual inertial hybrid control storage medium, which stores a computer-readable storage medium. When the computer-readable storage medium is executed by a processor, a virtual inertial hybrid control system as described in the first aspect is implemented.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses a virtual inertia hybrid control strategy based on a virtual synchronous generator (VSG) and a variable droop coefficient for a DC power station system, thereby ensuring the voltage stability of the system in a short time scale, while improving the reliability of long-time grid-connected operation, improving the bus voltage stability of the DC power station, and enhancing the inertial response of the system; the virtual inertia control strategy based on VSG and a variable droop coefficient is respectively applicable to the grid-connected unit, the hydrogen storage unit, and the electric storage unit, enhancing the inertial response of the system and improving the stability of the system; in an island state, the virtual inertia hybrid control technology can effectively cope with fluctuations in wind and solar power generation, improve the bus voltage stability of the DC power station, and verify the feasibility of the hybrid control strategy; compared with a pure electric storage power station, a hydrogen-electric energy storage DC power station has the characteristics of high energy density, environmental friendliness, and strong flexibility, and is effectively combined with renewable energy to provide long-term stable energy storage and release.

[0025] 2. The hydrogen energy storage unit in the present invention provides high-density long-term energy support, and the battery energy storage unit achieves system power balance and bus voltage stability in a short time scale through its rapid dynamic response capability; in addition, the virtual inertia control technology significantly improves the dynamic response capability 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 capability and stability of the hydrogen-electricity 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 a high proportion of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 This is a topological diagram of a DC power station of the present invention; Figure 2 It is a schematic diagram of virtual inertia control of a grid-connected unit according to the present invention; Figure 3 It is a schematic diagram of virtual inertia control of the hydrogen storage unit of the present invention; Figure 4 It is a schematic diagram of virtual inertia control of the electric storage unit of the present invention; Figure 5 It is a schematic diagram of power variation of different current feed-forward coefficients of the present invention; Figure 6 It is a schematic diagram of voltage variation with different current feed-forward coefficients of the present invention; Figure 7 It is a voltage variation diagram under different virtual capacitors of the present invention; Figure 8 It is a voltage variation diagram under different virtual damping coefficients of the present invention; Fig. 9 It is the output power diagram of each unit of the DC power station of the present invention; Fig.10 This is a voltage and current state change diagram of the DC power station of the present invention; Fig.11 This is a simulation waveform diagram of the DC power station system of the present invention; Fig.12 It is a schematic diagram of the flow of virtual inertia hybrid control of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. 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.

[0029] See also Figure 1 , a first aspect of the present invention provides a virtual inertial hybrid control system, including: a model building module and a virtual inertial control module; Model building module: The analytical model method is used to model the various types of voltages in the electrochemical reaction process of the DC power station, and the working voltage of the single electrolyzer is obtained; the working voltage is secondary modeled through the Thevenin equivalent circuit to obtain the current and open circuit voltage of the battery; wherein the DC power station includes: grid-connected unit, wind power generation unit, photovoltaic power generation unit, hydrogen storage unit, DC load unit and power storage unit; 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 build a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage; determine the control strategy by simulating 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 electricity storage unit.

[0030] See also Figure 2 , the model of Nernst voltage is constructed as: ; 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, is the water activity between the electrode and the membrane, is the fuel cell reaction temperature, R is the ideal molar gas constant, is the reference temperature under standard conditions; The activation overvoltage model is constructed as: ; In the formula, is the PEMFC current density, is the half-reaction exchange current density of the anode, is the cathode half-reaction exchange current density; and are the charge transfer coefficients of the anode and cathode, respectively; The ohmic overvoltage is modeled as: ; In the formula, is the thickness of the proton exchange membrane, is the conductivity of the proton exchange membrane; The model of concentration difference overvoltage is constructed as follows: ; In the formula, is the maximum current density; B is the diffusion coefficient, which reflects the rate of ion diffusion; The model for constructing the working voltage of a single electrolyzer is: ; S100: Get the battery terminal voltage and mark it as , polarization voltage , the ohmic resistance of the battery , the electrochemical polarization resistance of the battery is marked as And the battery electrochemical polarization capacitance is marked as ; S200: Add parallel resistance and capacitance based on the Rint model; S300: By formula The current of the single cell is calculated; 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 at which the polarization voltage changes with time, reflecting the amount of change in the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change in the battery polarization voltage with time; By formula Calculate the open circuit voltage of the single cell; Get the pitch angle of the wind turbine and mark it as β, and mark the radius of the wind rotor as , the fan speed is marked as , and the wind speed upstream of the rotor is marked as ; By formula Calculate the blade tip speed; By formula Calculate the wind energy utilization coefficient; By formula Calculate the power of the wind turbine; where, is the density of air; Obtain the reverse saturation current and mark it as , 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 , the parallel resistor is marked as , the series resistor is marked as , the output voltage of the solar cell is marked as ; Photocurrent is marked as ; Through simultaneous equations Calculate the current flowing through the diode , battery leakage current And the output current of the solar cell ; 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; 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 , the rated angular speed of the generator is marked as ; Adjust the fan speed , through the formula Calculate the rotor motion formula of the synchronous generator; Get the reference value and measured value of the distributed power input bus current and mark them as and ; The virtual capacitance and virtual damping coefficient are marked as and , the actual voltage of the DC bus is recorded as , the rated voltage of the DC bus is ;in, Indicates the difference between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation; when When it is not 0, the DC grid anti-interference model is: ; judge Is it less than 0? If yes, the output current is increased according to Ohm's law to offset the voltage drop; if no, the current output is reduced to offset the voltage rise.

[0031] 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 96485C / mol, and the electrolysis voltage applied to the electrode is marked as ; The ideal molar gas constant is ; The partial pressures of hydrogen and oxygen at the reaction interface are marked as , the water activity between the electrode and the membrane is 1, the charge transfer coefficients of the anode and cathode are marked as 2 and 0.5, respectively, and the PEMFC current density, maximum current density, and half-reaction exchange current density of the anode and cathode are marked as , the fuel cell reaction temperature is marked as And the entropy change in the hydrogen fuel cell reaction is marked as ΔS, and the value range of ΔS is less than 0; By formula The Nernst voltage is calculated; By formula The activation overvoltage is calculated; By formula The ohmic overvoltage is calculated; By formula The concentration difference overvoltage is calculated; By formula The working voltage of the single electrolyzer is calculated; Adjust the fan speed , through the formula The rotor motion formula of the synchronous generator is calculated; the rotor swing characteristic refers to the oscillation or swinging of the rotor when a disturbance occurs (such as load change, short circuit, etc.), and this oscillation can be observed by the change of the rotor angle; When mechanical power and electromagnetic power (braking power) When an imbalance occurs between the rotor and the A non-zero braking power or drag power will be generated, causing the rotor to generate angular acceleration, and the rotor angle will change over time, showing an oscillating characteristic; the damping coefficient D is due to mechanical friction, wind resistance and other factors, which gradually slows down the angular velocity of the rotor over time; the moment of inertia J is the inertial resistance of the rotor to angular acceleration. The greater the inertia, the slower the rotor responds to oscillation. Under the action of the moment of inertia J and the damping D, the oscillation of the rotor will be attenuated and eventually stabilize; In the AC system, the rotor releases kinetic energy to suppress frequency mutations. In the corresponding DC system, capacitors can release electrical energy to suppress bus voltage mutations. According to the corresponding relationship table between the DC grid and the AC grid (such as Table 1), a DC grid anti-interference model is constructed:

[0032] Table 1 Correspondence between DC power grid and AC power grid Through a dynamic process similar to rotor inertia control, the virtual inertia coefficient This enables the system to slow down the speed of voltage change when the voltage fluctuates. That is, the system will simulate the inertial response and provide a smooth voltage change process, thereby effectively reducing the impact of external interference on the DC bus voltage. The virtual damping coefficient is used to increase the damping of the voltage control system, which helps to suppress voltage fluctuations, especially when the voltage deviation is large. Indicates the gap between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation and used to adjust the control behavior of the system; virtual damping coefficient When the voltage deviation is large, the current output will be adjusted quickly, thereby suppressing the voltage fluctuation. The larger the voltage deviation, the greater the adjustment torque the system will produce, quickly restoring the voltage stability. Through this mechanism, the system can quickly compensate for voltage fluctuations and avoid long-term voltage fluctuations; In summary, when voltage deviation exists, 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 system's anti-interference ability.

[0033] 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; the present invention models the Nernst voltage and various overvoltages in the electrochemical reaction process by adopting an analytical model method; the electrical response time of the PEM stack is about 50ms, which is much smaller than the response time of the global system. Therefore, a static model is constructed to regard the electrolyzer and fuel cell as nonlinear resistance elements, without considering the dynamic process therein.

[0034] See also Figure 3-Figure 4 , determine whether the system is in off-grid mode; If yes, the selector is connected to port 1, and the control strategy is set as follows: the hydrogen storage unit participates in the network construction, obtains the reference current through the virtual inertia control and the voltage outer loop, and then generates the control signal through the current inner loop; the slow speed characteristics of the hydrogen storage unit are simulated by selecting appropriate virtual capacitors, virtual damping coefficients and parameters, and the bus voltage is kept at the rated value through the secondary voltage regulation link; If no, the selector is connected to port 2, and the control strategy is set as follows: the hydrogen storage unit works as a grid-connected unit, the system receives the power reference command issued by the control center, and smoothes the command through a low-pass filter to avoid damage to the PEM electrolyzer or equipment due to slow response speed; the power reference command of the hydrogen storage unit is generated after filtering, and then generates a control signal through the current inner loop; Build a virtual inertial hybrid control system simulation model of a hydrogen-electric energy storage DC power station through simulation software; Get the initial droop coefficient and label it as , the exponential voltage regulation factor is marked as ; Extract the voltage change rate through high-pass filtering , and the voltage change rate threshold is marked as ; Through the system of equations The calculated variable droop coefficient is an exponential one.

[0035] For example, the virtual inertia control strategies of hydrogen storage units and AC grid-connected units are consistent, both of which increase the inertia of the bus voltage by controlling the current of the input 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 follows Figure 3 ; Figure 3 middle , are virtual capacitance and virtual damping coefficient respectively; is the output current of the hydrogen storage unit, that is, the sum of the currents of the fuel cell and the electrolyzer. The direction of flow into the busbar is defined as positive. >0 means PEMFC is in working mode and PEMWE is off or in hot standby mode; is the current change obtained by droop control; the current correction amount generated by secondary voltage regulation is When the system operates in off-grid mode, the selector is connected to port one, and the hydrogen storage unit participates in the network construction. The reference current is obtained through virtual inertia control and the voltage outer loop, and then the control signal is generated 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 kept at the rated value through the secondary voltage regulation link.

[0036] As a fast energy storage, electrochemical energy storage with high rate and small capacity plays a key role in voltage control. By 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. Improved control strategies such as Figure 4 As shown: Figure 4 middle , are photovoltaic power generation and wind power generation respectively. , as well as When the current feedforward coefficient is , as well as The active power consumed by the AC grid-connected unit at the time of 0.05V; the direction flowing to the DC bus is the positive direction of power; the horizontal axis represents time, and the vertical axis represents the power change, that is, the changes of the power consumed by the AC grid-connected unit and the power injected by the power generation unit over time; is the time constant of the first-order high-pass filter. The voltage change rate can be extracted by high-pass filtering. ; is the exponential variable droop coefficient;

[0037] Where: is the initial droop coefficient, which is also the droop coefficient at stability; is the exponential voltage regulation coefficient; , are the voltage change rate and its threshold, respectively, where: =0.4.

[0038] See also Figure 5-Figure 11 , call up the built DC power station system, turn off the power storage unit module, verify the current feedforward coefficient to obtain the influence of virtual capacitance and virtual damping coefficient on virtual inertial control; among them, Fig. 9 middle, , and Figure 4 The corresponding letters in the table have the same meaning. Generate electricity for hydrogen fuel cells; They are the active power of the AC grid-connected unit and the electric storage power, respectively. The direction flowing toward the DC bus is the positive direction of power. are the electrolyzer power and 5G base station power, respectively, which are always non-positive; For example, according to the built DC power station system, turn off the power storage unit module, turn off the power storage unit, and maintain the virtual capacitor of the grid-connected unit. =1 / 600F, virtual damping coefficient =60, and test the influence of current feedforward coefficient on virtual inertia control of grid-connected unit separately. The simulation results of the power and DC bus voltage changes of each unit are as follows: Figure 5 , 6 As shown; from Figure 5 , 6 It can be seen that at the beginning of the simulation, the wind and solar power output rapidly increased to about 2.5MW, and the AC power grid quickly absorbed the power to stabilize the voltage. At 0.5s, the photovoltaic power generation decreased to about 0.8MW, resulting in a power shortage of about 1.2MW, and the power of the grid-connected unit increased; =0, that is, when there is no current feedforward link, the voltage rises rapidly at the beginning of the simulation, and the change exceeds 5% of the rated voltage (75V), and then slowly falls back, and the grid-connected unit power is slightly overregulated; at 0.5s, the voltage drops by about 45V, and then slowly rises back, at this time, the inertia effect of the grid-connected converter is not obvious; As the power of the AC grid increases, the power change becomes faster, the DC bus voltage fluctuation becomes smaller, and the corresponding power overshoot also becomes smaller. The maximum should not exceed 2 / 3. It will cause the control to reverse and become ineffective; Turn off the power storage unit and maintain the current feed-forward coefficient 2 / 3, virtual damping coefficient The virtual inertia control of the grid-connected unit and the traditional droop control are compared. The influence of virtual capacitor value on virtual inertia control is tested at 1 / 1500F and 1 / 600F. The simulation results of DC bus voltage change are as follows: Figure 7 ; Maintain current feed-forward coefficient 2 / 3, virtual capacitor is 1 / 600F, respectively The influence of virtual damping coefficient on virtual inertia control is tested with values ​​of 10, 60, and 200. The simulation results of DC bus voltage change are as follows: Figure 8 ; Use Matlab / Simulink simulation software to build a virtual inertial hybrid control system simulation model of a hydrogen-powered energy storage DC power station; 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 dropped to 100W / m2 in 1s, wind speed dropped to 5m / s, and PEMWE shutdown was not considered. The simulation results are as follows Fig. 9 , 10 As shown; 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: Fig.11 As shown; from Fig.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. Fig.11 In condition 2 (c) and (d) in the figure, at 2.5s, the photovoltaic power generation increases, and the surplus of 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 finally stabilize at 1500V.

[0039] 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.

[0040] See also Fig.12 The second aspect of the present invention provides a virtual inertia hybrid control method, comprising: 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; The operating voltage is modeled secondary through the Thevenin equivalent circuit to obtain the battery current and open circuit voltage; Obtaining power from the wind turbine through a wind power generation unit; Determine the MPPT control strategy for photovoltaic power generation units and wind power generation units, and build a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage; Determine the control strategy by simulating the virtual inertia of the hydrogen storage unit; The virtual inertia control strategy of the power storage unit is determined by using variable droop coefficient control.

[0041] A third aspect of the present invention provides a virtual inertia hybrid control storage medium, including: a computer-readable storage medium is stored thereon, and 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.

[0042] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0043] The working principle of the present invention is as follows: the multiple types of voltages in the electrochemical reaction process of the DC power station are modeled respectively by an analytical model method to obtain the working voltage of the single electrolyzer; the working voltage is secondary modeled by the Thevenin equivalent circuit to obtain the current and open-circuit voltage of the battery; the power of the wind turbine is obtained by the wind power generation unit; the MPPT control strategy of the photovoltaic power generation unit is determined, and a DC power grid anti-interference model is constructed based on the AC power grid to balance the fluctuating voltage; the control strategy is determined by simulating the virtual inertia of the hydrogen storage unit; the variable droop coefficient is obtained, and the virtual inertia control strategy of the power storage unit is determined by the variable droop coefficient control.

[0044] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A virtual inertial hybrid control system, characterized in that: include: Model building module and virtual inertial control module; Model building module: The analytical model method is used to model the various types of voltages in the electrochemical reaction process of the DC power station, and the working voltage of the single electrolyzer is obtained; the working voltage is secondary modeled through the Thevenin equivalent circuit to obtain the current and open circuit voltage of the battery; wherein the DC power station includes: grid-connected unit, wind power generation unit, photovoltaic power generation unit, hydrogen storage unit, DC load unit and power storage unit; 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 build a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage; determine the control strategy by simulating 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 electricity storage unit.

2. A virtual inertial hybrid control system according to claim 1, characterized in that: The multiple types of voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration difference overvoltage and working voltage of a single electrolyzer; 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, is the water activity between the electrode and the membrane, is the fuel cell reaction temperature, R is the ideal molar gas constant, is the reference temperature under standard conditions; The model of the activation overvoltage is: In the formula, is the PEMFC current density, is the half-reaction exchange current density of the anode, is the cathode half-reaction exchange current density; and are the charge transfer coefficients of the anode and cathode, respectively; The model of the ohmic overvoltage is: ; In the formula, is the thickness of the proton exchange membrane, is the conductivity of the proton exchange membrane; The model of the concentration overvoltage is: ; In the formula, is the maximum current density; B is the diffusion coefficient, which reflects the rate of ion diffusion; The model of the operating voltage of the monomer electrolyzer is: ; Among them, the multiple types of voltages include: Nernst voltage, activation overvoltage, ohmic overvoltage, concentration difference overvoltage and the working voltage of a single electrolyzer.

3. A virtual inertial hybrid control system according to claim 1, characterized in that: The method of performing secondary modeling on the operating voltage through the Thevenin equivalent circuit to obtain the current and open circuit voltage of the battery includes: S100: Get the battery terminal voltage and mark it as , polarization voltage , the ohmic resistance of the battery , the electrochemical polarization resistance of the battery is marked as , and the cell electrochemical polarization capacitance is labeled ; S200: Add parallel resistance and capacitance based on the Rint model; S300: By formula The current of the single cell is calculated; 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 at which the polarization voltage changes with time, reflecting the amount of change in the battery voltage per unit time; the product of the rate and the polarization capacitance represents the charge flow caused by the change in the battery polarization voltage with time; By formula Calculate the open circuit voltage of the single cell.

4. A virtual inertial hybrid control system according to claim 1, characterized in that: The step of obtaining the power of the wind turbine through the wind power generation unit comprises: Get the pitch angle of the wind turbine and mark it as β, and mark the radius of the wind rotor as , the fan speed is marked as , and the wind speed upstream of the rotor is marked as ; By formula Calculate the blade tip speed; By formula Calculate the wind energy utilization coefficient; By formula Calculate the power of the wind turbine; where, is the density of air.

5. A virtual inertial hybrid control system according to claim 1, characterized in that: The step of determining the MPPT control strategy of the photovoltaic power generation unit includes: Obtain the reverse saturation current and mark it as , 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 , the parallel resistor is marked as , the series resistor is marked as , the output voltage of the solar cell is marked as ; Photocurrent is marked as ; Through simultaneous equations Calculate the current flowing through the diode , battery leakage current And the output current of the solar cell ; 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.

6. A virtual inertial hybrid control system according to claim 4, characterized in that: The method of constructing a DC power grid anti-interference model based on the AC power grid to balance the fluctuating voltage includes: 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 , the rated angular speed of the generator is marked as ; Adjust the fan speed , through the formula Calculate the rotor motion formula of the synchronous generator; Get the reference value and measured value of the distributed power input bus current and mark them as and ; The virtual capacitance and virtual damping coefficient are marked as and , the actual voltage of the DC bus is recorded as , the rated voltage of the DC bus is ;in, Indicates the difference between the current DC bus voltage and the reference voltage, reflecting the degree of voltage deviation; when When it is not 0, the DC grid anti-interference model is: ;judge Is it less than 0? If yes, the output current is increased according to Ohm's law to offset the voltage drop; if no, the current output is reduced to offset the voltage rise.

7. A virtual inertial hybrid control system according to claim 1, characterized in that: The control strategy is determined by simulating the virtual inertia of the hydrogen storage unit, including: Determine whether the system is in off-grid mode; If yes, the selector is connected to port 1, and the control strategy is set as follows: the hydrogen storage unit participates in the network construction, obtains the reference current through the virtual inertia control and the voltage outer loop, and then generates the control signal through the current inner loop; the slow speed characteristics of the hydrogen storage unit are simulated by selecting appropriate virtual capacitors, virtual damping coefficients and parameters, and the bus voltage is kept at the rated value through the secondary voltage regulation link; If not, the selector is connected to port 2 and the control strategy is set as follows: the hydrogen storage unit works as a grid-connected unit, the system receives the power reference instruction issued by the control center, and smoothes the instruction through a low-pass filter; the power reference instruction of the hydrogen storage unit is generated after filtering, and then a control signal is generated through the current inner loop.

8. A virtual inertial hybrid control system according to claim 1, characterized in that: The step of obtaining the variable droop coefficient comprises: Get the initial droop coefficient and label it as , the exponential voltage regulation factor is marked as ; Extract the voltage change rate through high-pass filtering , and the voltage change rate threshold is marked as ; Through the system of equations The calculated variable droop coefficient is an exponential one.

9. A virtual inertia hybrid control method, adapted to a virtual inertia hybrid control system according to any one of claims 1 to 8, characterized in that: include: 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; The operating voltage is modeled secondary through the Thevenin equivalent circuit to obtain the battery current and open circuit voltage; Obtaining power from the wind turbine through a wind power generation unit; Determine the MPPT control strategy 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; The virtual inertia control strategy of the power storage unit is determined by using variable droop coefficient control.

10. A virtual inertial hybrid control storage medium having a computer-readable storage medium stored thereon, characterized in that: When the computer-readable storage medium is executed by a processor, the virtual inertial hybrid control system according to any one of claims 1 to 8 is implemented.

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