Direct-current bus voltage control strategy of off-grid wind-solar hydrogen storage system based on power side and load side

Abstract

The invention discloses a direct current bus voltage control strategy based on a power side and a load side of an off-grid wind-solar hydrogen storage system. The direct current bus voltage control strategy comprises the following steps: establishing an off-grid wind-solar hydrogen storage system space state model; constructing DC / DC converter models including a fuel energy storage unit converter model and a material battery unit DC / DC converter model; adding an extended state observer on a power source side, and setting a self-adaptive backstepping control strategy; and a self-adaptive fuzzy control power spring device is added to the load side to compensate the bus voltage. The method considers the randomness and volatility of wind and light output and the influence of the dynamic change of load power on the bus voltage, can effectively reduce the fluctuation range of the bus voltage when the system is disturbed, improves the stability and reliability of the system, and optimizes the energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of DC microgrid bus voltage control, and specifically relates to a control strategy for the DC bus voltage of an off-grid wind-solar-hydrogen storage system based on the power side and the load side. Background Art

[0002] When a DC microgrid is in an island operation state, the fluctuation control of the bus voltage usually depends on an energy storage system with a double closed-loop control. Among them, a PI regulator is responsible for processing the voltage deviation, and integral control eliminates the steady-state error. These two parts are combined to generate a control signal, and the control instruction is sent to the converter to adjust the operating state of its switching devices. However, while the traditional double-loop control method improves the system's response ability to disturbances, it is difficult to reduce the fluctuation of the DC bus voltage, and it is not very adaptable to the rapid changes of loads and power sources. To enhance the dynamic response ability of the system, multiple studies have introduced feedforward control. For example, in the literature: Hou Nie, Song Wensheng, Wu Mingyi. Load current feedforward control method for bidirectional full-bridge DC-DC converter [J]. Proceedings of the CSEE, 2016, 36(09): 2478-2485. DOI: 10.13334 / j.0258-8013.pcsee.2016.09.021., it is introduced that the core of feedforward control is to work according to the compensation principle based on the change of the disturbance or the given value. Its working principle is to measure the disturbance amount entering the system, including external disturbances and changes in the set value, and control and change the disturbance amount according to its signal, so that the controlled variable is maintained at the set value. Different from feedback control, feedforward control does not depend on the actual output of the system, but controls according to the magnitude of the disturbance action after the disturbance occurs and before the controlled variable changes, so as to compensate for the influence of the disturbance on the controlled variable. However, it requires accurate disturbance prediction information, has high requirements for the accuracy and response speed of sensors, and has limitations in the aspects of micro-power sources, load expansion, and plug-and-play in DC microgrids. Therefore, existing studies apply a variety of control methods and their combinations to stabilize the bus voltage of DC microgrids.

[0003] In the traditional voltage-current double closed-loop control strategy, first, the voltage of the DC bus and the current value of the fuel cell unit are obtained through the measurement link. Then, the voltage outer-loop control module calculates the required current reference value according to these measurement values. Next, this reference value is sent to the current inner-loop control module. Finally, the current inner-loop control module generates a control signal to adjust the operating state of the DC / DC converter to ensure that the output voltage and current can track their respective reference values. The traditional double closed-loop control has a slow dynamic response speed and is difficult to adapt to the rapid changes of loads and power source fluctuations. In particular, the current inner-loop control is not sensitive enough to system parameter changes and is difficult to adapt to strong nonlinear and uncertain factors in the power grid. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a DC bus voltage control strategy for an off-grid wind-solar-hydrogen storage system based on the power side and the load side. This method takes into account the randomness and volatility of wind and solar power generation, as well as the impact of the dynamic change of load power on the bus voltage, can effectively reduce the fluctuation range of the bus voltage when the system is disturbed, improve the stability and reliability of the system, and at the same time optimize the energy utilization efficiency.

[0005] The technical solutions adopted by the present invention are as follows:

[0006] DC bus voltage control strategy for an off-grid wind-solar-hydrogen storage system based on the power side and the load side,

[0007] Establish a mathematical model of the off-grid wind-solar-hydrogen storage system;

[0008] Construct a DC / DC converter model, including a fuel cell unit converter model and a fuel cell unit DC / DC converter model;

[0009] Attach an extended state observer to the power source side and set an adaptive backstepping control strategy;

[0010] Add a power spring device with adaptive fuzzy control to the load side to compensate the bus voltage.

[0011] In the off-grid wind-solar-hydrogen storage system, the wind power generation module and the photovoltaic power generation module are used as the primary power sources of the system, which are respectively used to convert renewable energy into electrical energy and inject it into the AC bus through an inverter; due to the volatility of wind and solar power generation, the present invention adds a hybrid energy storage system to smooth the power fluctuation of the power source and ensure continuous power supply; the hybrid energy storage system mainly consists of two parts: a hydrogen energy storage part and an electrochemical energy storage part, where the electrochemical energy storage part includes lithium batteries; the hydrogen energy storage part consists of an electrolyzer, a hydrogen storage tank, and a fuel cell. Figure 4 It is a topological structure diagram of an off-grid wind-solar-hydrogen storage DC microgrid system.

[0012] The mathematical model of the off-grid wind-solar-hydrogen storage system specifically includes:

[0013] (a). Mathematical modeling of the wind power generation module:

[0014] The output power of the direct-drive permanent magnet synchronous wind generator is related to the parameters of the wind turbine, the air density, and the current wind speed, etc., and can be expressed as:

[0015]

[0016] Among them, P m is the output power of the wind generator, C P (λ,β) is the wind energy utilization coefficient, ρ is the air density, R is the radius of the wind turbine, and v is the wind speed.

[0017] (b). Mathematical Modeling of Photovoltaic Power Generation Module:

[0018] The output current of the photovoltaic module is:

[0019]

[0020] Among them, I ph is the photocurrent, I 0 is the saturation current of the photovoltaic module, q is the electron charge, V is the output voltage of the photovoltaic module, R s is the series resistance, n is the ideality factor of the diode, N s is the number of series blocks of the photovoltaic module, K is the Boltzmann constant, T is the operating temperature, I sh is the current passing through the parallel resistance.

[0021] (c). Mathematical Modeling of Electrolytic Hydrogen Production Module:

[0022] Regarding the electrolytic hydrogen production system as a DC load, the higher the input current and voltage, the higher the hydrogen production efficiency. The U-I characteristic equation of the electrolyzer is:

[0023] U el = U eloc + U act + U ohm (13);

[0024] In the formula: U el is the electrolyzer terminal voltage; U eloc is the open-circuit voltage of the electrolyzer, U act is the activation polarization voltage, which is the voltage generated during the electrochemical reaction in the electrolyzer; U ohm is the ohmic polarization voltage, which is the voltage generated by the internal resistance of the electrolyzer. The open-circuit voltage U eloc The expression is:

[0025]

[0026] Among them: I elec is the inductor current; U rev is the reversible voltage of the electrolyzer under normal operating conditions; r 1 , r 2 are the electrolyzer internal resistance parameters affected by temperature; T elec is the operating temperature of the electrolyzer; A elec is the electrolyzer area; t 1 represents the electrolyzer startup time; t 2 represents the time required for the electrolyzer operating temperature to reach T elec ; t 3 represents the time required for the electrolyzer operating temperature to reach ;

[0027] Activation polar voltage U act The expression is:

[0028]

[0029] Where: R a is the gas constant; T elec is the electrolytic cell temperature; F is the Faraday constant (F = 96500 C / mol); α is the transfer coefficient, i is the current density, and i 0 is the exchange current density.

[0030] Ohmic polar voltage U ohm The expression is:

[0031]

[0032] Where: i is the current density, t m is the thickness of the proton exchange membrane, and σ m is the membrane resistivity.

[0033] Hydrogen production flow rate W elec The expression is:

[0034]

[0035] Where: N c is the number of single electrolysis units in the electrolytic cell; η F is the Faraday efficiency affected by the current density; f 1 , f 2 is a parameter for measuring the Faraday efficiency.

[0036] Reversible voltage U of the electrolytic cell under normal operating conditions rev The expression is:

[0037]

[0038] Where: △G elec is the Gibbs free energy change of the electrochemical reaction occurring in the electrolytic cell under normal operating conditions; z is the number of electrons transferred in each chemical reaction during the electrolytic hydrogen production process; F represents the Faraday constant.

[0039] (d). Mathematical modeling of fuel cell unit:

[0040] The mathematical model of the proton exchange membrane hydrogen fuel cell is:

[0041]

[0042] Where: n d is the ion traction coefficient; is the molar mass of water, and N cis the number of electrolyzer units, A elec is the area of the electrolyzer, t m is the thickness of the proton exchange membrane, D w is the diffusion rate of water in the membrane, C wa and C wc are the water concentrations on the anode and cathode membrane surfaces respectively.

[0043] (e). Mathematical modeling of lithium batteries:

[0044] As an electrochemical energy storage, during discharge of a lithium battery, i * > 0;

[0045]

[0046] During charging (i * < 0);

[0047]

[0048] In the formula: i t is the actual charging amount of the battery, i * is the filtering current, i is the battery current, E 0 is the constant voltage, K * is the polarization constant, Q is the battery capacity, A is the exponential voltage, B is the exponential region time reverse ratio; f 1 (i t , i, i * ) represents the state function of the lithium battery during discharge; f 2 (i t , i, i * ) represents the state function of the lithium battery during charging.

[0049] In the off-grid wind-solar-hydrogen storage DC microgrid system described above, the electrochemical energy storage part has two functions: First, during the low electricity consumption period, the wind-solar power generation supplies power to users and electrolyzes water to produce hydrogen, and the excess electricity is stored in the electrochemical energy storage part; Second, during the high electricity consumption period, when the wind-solar power output cannot meet the load demand, power is transmitted from the electrochemical energy storage part to the electrical load for use; Therefore, the electrochemical energy storage part is equipped with a DC / DC bidirectional converter to meet its charging and discharging requirements; The circuit diagram of the electrochemical energy storage part is as Figure 1 shown.

[0050] Figure 1 In the circuit, C bus , C are the input and output filter capacitors respectively, the switch tube 1 is the main switch tube, and the switch tube 2 plays a freewheeling role. When the switch tube 1 is turned on, the inductor L stores energy, and when the switch tube 1 is turned off, the inductor L and the power supply jointly supply energy to the lithium battery.

[0051] When the charging operation is carried out, the lithium battery side DC / DC bidirectional converter in the electrochemical energy storage part is configured in the Buck converter mode; its mathematical expression is:

[0052]

[0053] In formula (4): i ch is the current flowing through the battery during charging, V ref is the reference value of the DC bus voltage, i in is the grid-side input current; d is the conduction time ratio of the switch tube, that is, the duty cycle; i s is the current of the input converter, v o and i ch are the voltage and current of the output converter respectively; k vp , k vi represent the proportional gain and integral gain in the voltage loop PI controller respectively; k ip , k ii represent the proportional gain and integral gain in the current loop PI controller respectively; i L represents the current flowing through the inductor L; V bus represents the bus voltage; R out represents the resistance value of the resistor R; C bus represents the input filter capacitor of the converter; i ref represents the current reference value after passing through the PI controller; i ch (τ) represents the current function related to the charging time τ.

[0054] When the charging operation is carried out, if the duty cycle of switch tube 1 is set to d, correspondingly, the duty cycle of switch tube 2 is 1 - d. At this time, the DC / DC bidirectional converter is configured in the Boost converter mode. Its mathematical expression is:

[0055]

[0056] The structure diagram of the fuel cell unit DC / DC converter is as shown in Figure 2 . Since the connected bus voltage level is 10 kV, the fuel cell unit DC / DC converter is configured as a Boost step-up converter. When the switch tube is triggered to be in the on state, the fuel cell charges the inductor L 1 , and the capacitor C 1 discharges. When the switch tube is turned off under reverse voltage, the fuel cell and the inductor L 1 supply energy to the bus simultaneously and charge the capacitor C 1 . The inductor L 1 and the capacitor C 1 play the roles of boosting voltage and stabilizing voltage output respectively.

[0057]

[0058] In Equation (6): V out is the output voltage of the fuel cell; I 1 is the current of the boost circuit; V 1 is the output voltage of the boost circuit; d is the duty cycle; t on is the conduction time of the switching transistor; t off is the turn-off time of the switching transistor.

[0059] The concept of an nth-order time-varying nonlinear system is used in the design of the extended state observer;

[0060] The expression of the nth-order time-varying nonlinear system is shown in Equation (1):

[0061] The expression of the nth-order time-nonlinear system is shown in Equation (1):

[0062] x (n) (t) = f(x(t), …, x (n-1) (t) + w(t) + bu(t)) (1);

[0063] In Equation (1): x(t), …, x (n-1) (t) represents the system state variables that can be directly measured; w(t) represents the external disturbances suffered by the system, and these external disturbances have uncertainty characteristics; u(t) represents the control input of the system; b represents the gain coefficient of the control input;

[0064] Let x 1 (t) = x(t), x 2 (t) = x′(t), …, x n (t) = x (n-1) (t), where: x 1 (t), x 2 (t) …, x n (t) represent the estimated values of the 1st, 2nd,..., nth system state variables respectively.

[0065] Then the state expression of Equation (1) is shown in Equation (2):

[0066]

[0067] In Equation (2): respectively represent the derivatives of the estimated values of the 1st, 2nd,..., nth system state variables; x i respectively represent the actual values of the 1st, 2nd,..., nth system state variables; f(x 1 , x 2 , …, x n ) represents the unknown dynamics and external disturbances of the system; y represents the output of the system.

[0068] In formula (2), the nonlinear part and the disturbance of the system are denoted as a(t), then a(t) = f(x(t), …, x (n-1) (t)) + w(t), and thus formula (2) can be expressed as formula (3):

[0069]

[0070] The present invention follows the design concept of integrating dynamic compensation parameters into a nonlinear extended state observer, and constructs a corresponding extended state observer for the system described by formula (2);

[0071]

[0072] In formula (7): respectively represent the derivatives of the first, second, …, nth system state variables processed by the extended state observer; represents the derivative of the n + 1th system state variable processed by the extended state observer; k 1 , k 2 , …, k n respectively represent the gain coefficients of the first, second, …, nth order control inputs; k n+1 represents the gain coefficient of the n + 1th order control input; g(z 1 -x(t)) represents a nonlinear function related to the error; g'(z 1 -x(t)) represents the derivative of the nonlinear function related to the error; x(t) represents the state variable of the system.

[0073] The design of the extended state observer completes the observation of the nonlinear part of the system shown in formula (2).

[0074] The extended state observer can monitor and estimate uncertain factors such as external disturbances in real time, quickly track each state variable, so that the system analysis can be approximately linearized. Based on the traditional observer, the extended state observer (ESO) regards the disturbance in the system as an additional state variable for observation. For example, for a system with an n-dimensional state space, the extended state observer (ESO) will expand it to n + 1 dimensions, where the (n + 1)th state variable is used to describe the disturbance. By introducing the disturbance state variable, the extended state observer (ESO) can more comprehensively describe the dynamic behavior of the system. This state expansion enables the extended state observer (ESO) to not only estimate the internal state of the system, but also estimate external disturbances in real time.

[0075] Next, an adaptive backstepping control strategy will be designed for the system shown in formula (2):

[0076] Backstepping control is a control design method based on Lyapunov stability theory. Its core idea is to decompose a complex nonlinear system into several subsystems, and then start from the innermost subsystem and gradually design the control law outward. The design of each step depends on the result of the previous step. By introducing virtual control variables and Lyapunov functions, the stability of each subsystem is ensured. Adaptive control is used to handle uncertain parameters and disturbances in the system. It adjusts the control law to adapt to the changes of the system by online estimating unknown parameters or disturbances. Adaptive control usually combines parameter update laws to achieve compensation for uncertainties, and can effectively improve the control performance and stability of the system.

[0077] First, the nonlinear system is modeled as a series of subsystems, and each subsystem can be expressed in the form of a state equation. Starting from the innermost subsystem, a virtual control variable is designed to make the subsystem asymptotically stable. During the design process, a Lyapunov function needs to be constructed and its derivative is ensured to be negative definite. Substitute the designed virtual control variable into the next subsystem and repeat the above steps until the control law of the entire system is designed. For the uncertain parameters or disturbances in the system, an adaptive parameter update law is designed for online estimation. The update law is usually based on error feedback to make the estimated value approach the true value. Finally, combine the control law obtained by backstepping control with the adaptive parameter update law to form the final adaptive backstepping control law. The control law is:

[0078]

[0079] In Equation (8): bu represents the system control law; e 2 represents the second system error; e represents the first system error; f 2 (x 1 , x 2 ) represents the nonlinear part in the system; represents the estimation error; represents the virtual control variable.

[0080] The parameter update rate is:

[0081]

[0082] In Equation (9): represents the derivative of the estimation error; γ represents the parameter adjustment rate.

[0083] Add a power spring device with adaptive fuzzy control on the load side:

[0084] First, model the power spring device and analyze its dynamic characteristics. Consider the possible parameter changes and external disturbances during system operation, such as load changes, voltage fluctuations, etc. According to the control objectives of the power spring device, design a fuzzy controller. Define fuzzy variables such as input error and error change rate, and formulate corresponding fuzzy rules. The fuzzy rules describe how to adjust the control quantity to achieve the desired control effect under different error conditions. Introduce an adaptive adjustment mechanism to monitor the system state and parameter changes in real time. By online estimating the system parameters or disturbances, adjust the parameters of the fuzzy controller, such as the weights of the fuzzy rules, the scaling factors of the fuzzy universe of discourse, etc. For example: According to the magnitude of the input error, adjust the fuzzy universe of discourse in real time to improve the control accuracy. Apply the adaptive fuzzy control strategy to the power spring device, and achieve stable control of the grid voltage by adjusting the power output of the power spring device. In actual operation, the adaptive fuzzy controller can dynamically adjust the control strategy according to the changes of the system, improving the regulation ability and adaptability of the power spring device. The equivalent circuit diagram of the power spring is as Figure 3 shown, and the relationship between the power spring and the controllable load voltage is as shown in Equation (10):

[0085]

[0086] where, u 1 is the node voltage on the input side; u EL is the load terminal voltage on the output side; C 1 , C L are the input - side and output - side capacitors respectively; D is the duty cycle of the controller; i 1 represents the input current; i o represents the output current; i EL represents the current flowing into the controllable load; L represents the filter inductor. The control block diagram of the power spring based on adaptive fuzzy control is as Figure 3 shown.

[0087] To verify the effectiveness of the control strategy proposed in the present invention, a stand - alone wind - solar - hydrogen storage system model is built on the MATLAB / Simulink platform, verifying that the proposed control strategy based on the extended state observer and adaptive backstepping control can make the bus voltage recover to stability faster than the traditional PI control; in the face of load disturbances, the power spring device with adaptive fuzzy control can compensate the bus voltage by controlling the controllable load voltage.

[0088] The technical effects of the DC bus voltage control strategy for the stand - alone wind - solar - hydrogen storage system based on the power side and the load side of the present invention are as follows:

[0089] 1) The mathematical model of the off-grid wind-solar-hydrogen storage system established by the present invention can comprehensively describe the dynamic characteristics of each component in the system and their interaction relationships by establishing an accurate mathematical model. For example, the operating characteristics of equipment such as wind turbines, photovoltaic generators, alkaline electrolyzers, fuel cells, and lithium batteries are different, and their output powers and response characteristics under different environmental conditions also vary. Through mathematical modeling, these complex characteristics can be quantified into state equations and control equations, providing a solid foundation for the subsequent design of control strategies.

[0090] 2) The DC / DC converter model established by the present invention. As a key energy conversion and regulation device in the system, the establishment of its model can accurately describe the dynamic behavior of the converter under different operating states. For example, the converter models of the electrolytic hydrogen production unit and the fuel cell unit respectively reflect the voltage and current relationships during the energy conversion process of the electrolyzer and the fuel cell and their interaction with the system bus voltage. Through these models, the response characteristics of the converter under different input powers, load changes, and control strategies can be accurately predicted and analyzed, providing a theoretical basis for optimizing the system stability and efficiency. Secondly, constructing the DC / DC converter model helps to achieve precise control and management of the system power flow. In a microgrid system, the reasonable distribution and regulation of power are the keys to ensuring the stable operation of the system. Through the converter model, effective control strategies can be designed, such as adjusting parameters such as the duty cycle and switching frequency of the converter, to achieve precise control of the output power. For example, during peak electricity consumption, the DC / DC converter of the electrochemical energy storage unit can be adjusted to quickly release the stored electrical energy to meet the load demand; while during low electricity consumption, the converter can be controlled to store the excess wind-solar power generation energy. This precise power control not only improves the energy utilization efficiency but also enhances the system's adaptability to load fluctuations and power source changes.

[0091] 3) An extended state observer is added on the power source side of the present invention and adaptive backstepping control is set. First of all, the extended state observer can effectively estimate the uncertainties and external disturbances in the system. In a microgrid system, due to the randomness and volatility of wind and solar power generation, as well as the dynamic changes of loads, the system often faces the influence of various uncertain factors. By observing these disturbances as additional state variables, the extended state observer can accurately estimate the magnitude and change trend of the disturbances in real time, thus providing more accurate information for the formulation of control strategies. This real-time estimation and compensation of disturbances greatly improve the robustness and stability of the system, enabling it to maintain good performance in the face of various uncertain factors. Secondly, the introduction of adaptive backstepping control provides the system with powerful nonlinear control capabilities. Backstepping control is a control method based on Lyapunov stability theory, which can decompose a complex nonlinear system into multiple subsystems and gradually design control laws to ensure the stability of each subsystem. The adaptive mechanism can online estimate the uncertain parameters and disturbances in the system and dynamically adjust the control parameters according to the estimation results. This adaptive backstepping control can not only effectively compensate the nonlinear part of the system, but also adapt to the changes of system parameters and the fluctuations of the external environment, further improving the control performance and stability of the system. For example, in the coordinated control of fuel cells and electrochemical energy storage, the adaptive backstepping control can dynamically adjust the control law according to the real-time estimated disturbances and system states to achieve precise control of fuel cells and electrochemical energy storage, ensuring that the system can operate stably under various working conditions and meet the load demand. In addition, this control strategy also has good adaptability and flexibility, can be widely applied to different types of microgrid systems, and has high practical value.

[0092] 4) In the control strategy of the off-grid wind-solar-hydrogen storage DC microgrid system, an electric spring device with adaptive fuzzy control is involved on the load side. First of all, the electric spring device itself has the ability to regulate voltage and compensate power. It can maintain the stability of the grid voltage by dynamically adjusting its own power output or absorption when the grid voltage fluctuates or the load changes suddenly. This is crucial for the stable operation of the DC microgrid system because the stability of the DC bus voltage directly affects the safety and reliable operation of the entire system. Secondly, the introduction of adaptive fuzzy control provides a more flexible and intelligent control method for the electric spring device. Fuzzy control can handle the uncertainty and fuzziness in the system, and infer appropriate control quantities through fuzzy rules based on information such as input error and error change rate. This has significant advantages in dealing with the complex dynamic changes and uncertain factors in the microgrid system. For example, in the case of sudden load increase or voltage drop, the adaptive fuzzy controller can quickly adjust the power output of the electric spring according to the real-time monitored voltage deviation and change trend to compensate for the voltage drop and restore system stability. At the same time, the adaptive mechanism enables the controller to adjust parameters such as the weight of fuzzy rules and the scaling factor of the fuzzy universe in real time according to the system operation state and parameter changes, further improving the control accuracy and adaptability. This electric spring device with adaptive fuzzy control not only enhances the system's resistance to transient impact events but also improves the overall regulation ability and flexibility of the system, which is of great significance for improving the stability and reliability of the microgrid system. Description of the Drawings

[0093] Figure 1 It is the circuit diagram of the energy storage battery.

[0094] Figure 2 It is the structure diagram of the DC / DC converter of the fuel cell unit.

[0095] Figure 3 It is the equivalent circuit diagram of the electric spring device.

[0096] Figure 4 It is the topological structure of the off-grid wind-solar-hydrogen storage system.

[0097] Figure 5 It is the structure diagram of the fuel cell-lithium battery hybrid energy storage system.

[0098] Figure 6 It is the double closed-loop control block diagram of the DC bus voltage.

[0099] Figure 7 It is the feedforward control block diagram of the DC bus voltage and current.

[0100] Figure 8 It is the adaptive fuzzy control block diagram.

[0101] Figure 9It is an electrical system diagram.

[0102] Figure 10 It is a hydrogen chain system diagram.

[0103] Figure 11(a) is the bus voltage curve under the improved control strategy when the system is operating normally.

[0104] Figure 11(b) is the bus voltage curve under the traditional control strategy.

[0105] Figure 12 It is a partial comparison diagram of the system dynamic response.

[0106] Figure 13 It is a diagram of the change in the DC bus voltage before and after the controllable load compensation.

[0107] Figure 14 It is the change curve of the DC bus voltage during load disturbance.

[0108] Figure 15 It is a schematic diagram of the fuel cell output power. Specific implementation manners

[0109] Based on the DC bus voltage control strategy of the off-grid wind-solar-hydrogen storage system on the power side and the load side, for the off-grid wind-solar-hydrogen storage DC microgrid system model, at the DC / DC converter side of the fuel cell and the electrochemical energy storage, the traditional double closed-loop control is improved, and an extended state observer (ESO) is added to virtually measure the required parameters. The adaptive backstepping method is used to compensate for the nonlinear part in the system, and the control parameters are dynamically adjusted through the adaptive mechanism to further improve the stability and robustness of the DC microgrid bus voltage control. Secondly, in order to meet the load demand, at the electrolyzer converter side, a power spring device with adaptive fuzzy control is added, which can maintain the stability of the DC side voltage by adjusting the electrolyzer terminal voltage and power.

[0110] 1. Mathematical modeling of the wind-solar-hydrogen storage system:

[0111] (a) Mathematical modeling of the wind power generation module:

[0112] The output power of the direct-drive permanent magnet synchronous wind generator is related to the wind turbine parameters, air density, and current wind speed, etc., and can be expressed as:

[0113]

[0114] Among them, P m is the output power of the wind generator, C P (λ,β) is the wind energy utilization coefficient, ρ is the air density, R is the wind turbine radius, and v is the wind speed.

[0115] (b) Mathematical Modeling of Photovoltaic Power Generation Module:

[0116] The output current of the photovoltaic module is:

[0117]

[0118] Among them, I ph is the photocurrent, I 0 is the saturation current of the photovoltaic module, q is the electron charge, V is the output voltage of the photovoltaic module, R s is the series resistance, n is the ideality factor of the diode, N s is the number of series blocks of the photovoltaic module, K is the Boltzmann constant, T is the operating temperature, I sh is the current passing through the parallel resistance.

[0119] (c) Mathematical Modeling of Electrolytic Hydrogen Production Module:

[0120] Regarding the electrolytic hydrogen production system as a DC load, the higher the input current and voltage, the higher the hydrogen production efficiency. The U-I characteristic equation of the electrolyzer is:

[0121] U el = U eloc + U act + U ohm (13);

[0122] In the formula: U el is the electrolyzer terminal voltage; U eloc is the open-circuit voltage of the electrolyzer, U act is the activation polarization voltage, which is the voltage generated during the electrochemical reaction in the electrolyzer; U ohm is the ohmic polarization voltage, which is the voltage generated by the internal resistance of the electrolyzer. The expression of the open-circuit voltage U eloc is:

[0123]

[0124] Among them: I elec is the inductor current; U rev is the reversible voltage of the electrolyzer under normal operating conditions; r 1 , r 2 are the electrolyzer internal resistance parameters affected by temperature; T elec is the operating temperature of the electrolyzer; A elec is the electrolyzer area; t 1 represents the electrolyzer startup time; t 2 represents the time required for the electrolyzer operating temperature to reach T elec ; t 3 represents the time required for the electrolyzer operating temperature to reach ;

[0125] Activation Polarization Voltage U act The expression is:

[0126]

[0127] Where: R a is the gas constant, 8.314 J / mol·K; T elec is the electrolyzer temperature; F is the Faraday constant (F = 96500 C / mol); α is the transfer coefficient (dimensionless number, take 0.25), i is the current density, and i 0 is the exchange current density.

[0128] Ohmic Polarization Voltage U ohm The expression is:

[0129]

[0130] Where: i is the current density, t m is the thickness of the proton exchange membrane, and σ m is the membrane resistivity.

[0131] Hydrogen Production Flow Rate W elec The expression is:

[0132]

[0133] Where: N c is the number of single electrolysis units in the electrolyzer; η F is the Faraday efficiency affected by the current density; f 1 , f 2 is a parameter for measuring the Faraday efficiency.

[0134] Reversible Voltage U of the Electrolyzer under Normal Operating Conditions rev The expression is:

[0135]

[0136] Where: △G elec is the change in Gibbs free energy of the electrochemical reaction that occurs in the electrolyzer under normal operating conditions (for the electrochemical reaction where the product is water, △G elec = -474.4 kJ / mol); z is the number of electrons transferred in each chemical reaction during the electrolytic hydrogen production process, generally taking a fixed value of 2; the Faraday constant is represented by F, and its value is 96500 C / mol.

[0137] (d) Mathematical Modeling of Fuel Cell Unit:

[0138] The mathematical model of the proton exchange membrane hydrogen fuel cell (PEMFC) is:

[0139]

[0140] Where: n d is the ion traction coefficient; is the molar mass of water, N c is the number of electrolyzer units, A elec is the electrolyzer area, t m is the thickness of the proton exchange membrane, D w is the diffusion rate of water in the membrane, C wa and C wc are the water concentrations on the anode and cathode membrane surfaces, respectively.

[0141] (e) Lithium battery mathematical modeling:

[0142] As an electrochemical energy storage, the lithium battery in the present invention not only plays a role in peak shaving and valley filling, but also can be used as a hybrid energy storage together with the fuel cell to compensate the DC microgrid bus voltage in the off-grid operation state.

[0143] Where, during discharging (i * > 0);

[0144]

[0145] During charging (i * < 0);

[0146]

[0147] In the formula: i t is the actual battery charging amount, i * is the filtering current, i is the battery current, E 0 is the constant voltage, K * is the polarization constant, Q is the battery capacity, A is the exponential voltage, B is the exponential region time reverse ratio; f 1 (i t , i, i * ) represents the state function of the lithium battery during discharging; f 2 (i t , i, i * ) represents the state function of the lithium battery during charging.

[0148] 2. Construct the DC / DC converter model:

[0149] (a) Energy storage unit converter model:

[0150] In an off-grid wind-solar-hydrogen storage DC microgrid system, the energy storage unit has two functions. First, during the low electricity consumption period, the wind and solar power generation supplies power to users and the electrolyzer for hydrogen production, and the excess electricity is stored in the electrochemical energy storage unit. Second, during the high electricity consumption period, when the wind and solar power output cannot meet the load demand, electricity is transmitted from the electrochemical energy storage to the electrical load for use. Therefore, the electrochemical energy storage unit is equipped with a DC / DC bidirectional converter to meet its charging and discharging requirements. The circuit diagram of the electrochemical energy storage unit is as shown in Figure 1 shown.

[0151] When the charging operation is carried out, the DC / DC converter of the energy storage battery is configured in the Buck converter mode. i ch is the current flowing through the battery during charging, V ref is the reference value of the DC bus voltage, i in is the input current on the grid side; d is the conduction time ratio of the switch tube, that is, the duty cycle; i s is the current of the input converter, v o and i ch are the voltage and current of the output converter respectively, k vp and k vi represent the proportional gain and integral gain in the voltage loop PI controller respectively, k ip and k ii represent the proportional gain and integral gain in the current loop PI controller respectively. Its mathematical expression is as shown in Equation (4).

[0152] When the charging operation is carried out, if the duty cycle of switch tube 1 is set to d, correspondingly, the duty cycle of switch tube 2 is 1 - d. At this time, the DC / DC converter is configured in the Boost converter mode. Its mathematical expression is as shown in Equation (5).

[0153] (b) Fuel cell unit DC / DC converter model:

[0154] The fuel cell unit DC / DC converter model is as shown in Figure 2 shown. Since the connected bus voltage level is 10 kV, the DC / DC converter of the hydrogen fuel cell is configured as a Boost step-up converter. When the switch tube is triggered to be in the on state, the fuel cell charges the inductor L 1 , and the capacitor C 1 discharges. When the switch tube is turned off under reverse voltage, the fuel cell and the inductor supply energy to the bus and charge the capacitor at the same time. The inductor and the capacitor play the roles of boosting voltage and stabilizing the voltage output respectively. Its mathematical expression is as shown in Equation (6).

[0155] Among them, V 0 is the output voltage of the fuel cell; I 0 is the current of the boost circuit; V out is the output voltage of the boost circuit; d is the duty cycle; ton is the conduction time of the switching transistor; t off is the turn-off time of the switching transistor.

[0156] 3. Set the adaptive backstepping control in the additional extended state observer on the power source side:

[0157] As Figure 5 shown is a hybrid energy storage system composed of a fuel cell and a lithium battery, which is connected to the DC bus through a fuel cell unit DC / DC converter.

[0158] In the conventional control scheme, the voltage-current double closed-loop control method described in Figure 6 is usually implemented.

[0159] The output voltage is:

[0160]

[0161] where G v (s) represents the closed-loop transfer function of the voltage control loop; G i (s) represents the closed-loop transfer function of the current control loop; G f (s) is the transfer function of the feedforward link; is defined as the proportionality factor between the port voltage and the DC bus voltage.

[0162] The dynamic response speed of the traditional double closed-loop control is slow and it is difficult to adapt to the rapidly changing load and power source fluctuations. To solve this problem, the present invention adopts a current feedforward control component. This control component is applied to the control architecture of the DC / DC converter, and its block diagram is shown in detail in Figure 7 .

[0163] Referring to Figure 7 in the current feedforward and double closed-loop control model, the DC bus voltage generated by the DC / DC converter can be expressed by formula (14):

[0164]

[0165] where G f (s) is the transfer function of the feedforward link.

[0166] However, in practical applications, the current inner loop control is not sensitive enough to system parameter changes and it is difficult to adapt to strong nonlinear and uncertain factors in the power grid. Therefore, introducing adaptive backstepping control can use this estimated information to design the control law, thereby improving the response speed of the system to the change of the bus voltage and effectively suppressing the influence of disturbances and parameter changes on the system performance. The present invention proposes a current feedforward control method based on an extended state observer and adaptive backstepping method.

[0167] The extended state observer can monitor and estimate uncertain factors such as external disturbances in real time, quickly track each state variable, so that the system analysis can be approximately linearized.

[0168] The expression of an nth-order nonlinear system is shown in Equation (1):

[0169] Among them, x(t),…,x (n-1) (t) represents the system state variables that can be directly measured; w(t) represents the external disturbances suffered by the system, and these disturbances have uncertainty characteristics; u(t) represents the control input of the system; b is the gain coefficient of the control input. Let x 1 (t) = x(t), x 2 (t) = x′(t),…,x n (t) = x (n-1) (t), then the state expression of the standard structure of Equation (1) is shown in Equation (2):

[0170] Denote the nonlinear part and the disturbance quantity of the system in the formula as a(t), then a(t) = f(x(t),…,x (n-1) (t)) + w(t).

[0171] Thus, Equation (2) can be expressed as Equation (3).

[0172] Following the design concept of integrating dynamic compensation parameters into the extended state observer, for the system described by Equation (3), an extended state observer is constructed as shown in Equation (7).

[0173] In the above formula, g(z) is a nonlinear function related to the error. By simply selecting appropriate k values and the function g(z), each state variable and the extended state a(t) in Equation (20) can be well estimated.

[0174] The core idea of the adaptive backstepping control strategy is to use the recursive design framework of the backstepping method to handle the stability and tracking problems of nonlinear systems, and at the same time introduce an adaptive mechanism to deal with the uncertainty of system parameters and external disturbances. In adaptive backstepping control, the system is decomposed into multiple subsystems, and the designer recursively designs virtual control laws and / or state transformations for each subsystem to ensure that the performance indicators of each subsystem (such as stability, tracking error) meet the predetermined requirements. At the same time, the adaptive mechanism allows the controller to dynamically adjust the control parameters to adapt to the parameter changes and environmental disturbances that may occur during the operation of the system, thereby enhancing the robustness and adaptability of the system.

[0175] The design of the extended observer has completed the observation of the nonlinear part of the system shown in Equation (2). Next, an adaptive backstepping controller will be designed for the system shown in Equation (2), which is mainly divided into the following five steps.

[0176] (1) Suppose that after adaptive backstepping control, \(y\rightarrow y_d\). Define the system error: d ,

[0177] e 1 =y - y_d d =x 1 -y_d d (25);

[0178] The dynamic equation of the system error is:

[0179]

[0180] (2) Select the Lyapunov function:

[0181]

[0182] Take the derivative of it to get:

[0183]

[0184] Regard \(x\) 2 as an input quantity.

[0185]

[0186] At this time, reaches a negative definite form. Introduce the virtual control quantity \(\alpha\) 1 , which can be expressed as:

[0187]

[0188] (3) Then define the second error \(e_1\) 2 :

[0189] e_1 2 =x_1 2 -\alpha 1 (31);

[0190] Substitute formula (31) into formula (26) to get:

[0191]

[0192] Then

[0193]

[0194] where \(e_{1e}\) 2 is the cross - coupling term and will be processed later.

[0195] The dynamic equation of the error \(e_1\) 2 is:

[0196]

[0197] The estimated error is:

[0198]

[0199] (4) Generate a new Lyapunov function

[0200]

[0201] where γ is the parameter adjustment rate, and for V 2 Taking the derivative gives:

[0202]

[0203] (5) The system control law is shown in Equation (8). The parameter update rate is shown in Equation (9).

[0204] 4. DC microgrid bus voltage stability strategy based on adaptive fuzzy control for power springs:

[0205] In the electrolytic hydrogen production unit, a power spring with adaptive fuzzy control is added. By adjusting the power consumption and terminal voltage of the electrolyzer, the bus voltage is further regulated. The equivalent circuit of the power spring is as Figure 3 shown.

[0206] where u 1 , i 1 are the input voltage and current; u EL , i EL are the terminal voltage and current of the electrolyzer; L is the filter inductor; C 1 , C L are the input and output capacitors respectively; D is the duty cycle of the controller.

[0207] The control block diagram of the power spring based on adaptive fuzzy control is as Figure 8 shown

[0208] At this time, the outputs k p , k d , k i of the PID controller are:

[0209]

[0210] In the formula, △k p , △k i , △k d are the output values adjusted by fuzzy inference; k p0 , k d0 , k i0 are the original values of the PID controller. The fuzzy control rules are shown in Table 1.

[0211] Table 1 Fuzzy control rules

[0212]

[0213] 5. Build a simulation to verify the effectiveness of the proposed control method:

[0214] Use the intelligent DC converter as the underlying main control device to regulate the operation of each subsystem. Construct a wind-solar-hydrogen charging integrated microgrid system with new energy such as wind and solar as the main power source and the ±10 kV DC bus as the main backbone grid. Figure 9 is the system electrical wiring diagram. The hydrogen system schematic diagram is as Figure 10 shown.

[0215] Build a model of an off-grid wind-solar-hydrogen DC microgrid system in the simulation software. Add an extended state observer and adaptive backstepping control to the DC / DC converter side of the power source composed of the fuel cell, electrochemical energy storage, and wind-solar output. Use the output of the ESO-AdaptiveBackstepping control as the current reference value to input the current inner loop. In the case of fluctuations in the DC bus voltage, the DC / DC converter can quickly respond to and precisely control the current by adjusting the set current value to rapidly track the new current command. The bus voltage in the Ningbo Cixi hydrogen-electricity coupled DC microgrid is 10 kV.

[0216] Figure 11(a) is the bus voltage curve under the improved control strategy when the system is operating normally, and Figure 11(b) is the bus voltage curve under the traditional control strategy.

[0217] It can be seen that in the normal operating state of the system, compared with the traditional control strategy, the voltage stabilization time of the current feedforward control strategy based on ESO-AdaptiveBackstepping is reduced by 46.8% with almost no overshoot. Figure 12 is the comparison diagram of the system dynamic responses under the two control methods. It can be seen that the voltage waveform of the improved control strategy is more stable.

[0218] 6. Simulation analysis of the power spring control strategy based on adaptive fuzzy control:

[0219] For the power spring based on adaptive fuzzy control proposed in the present invention considering the demand side, it will be verified in the simulation. Set a load of 700 kW to be put into the system at 1 s. Figure 13 is the change of the bus voltage after adding controllable load compensation. The power spring of the controllable load end based on adaptive fuzzy control can compensate the bus voltage. However, due to the limited capacity of controllable loads such as electrolyzers, the bus voltage cannot be maintained within the rated standard value range. To solve this problem, the present invention uses the electrolyzer in combination with an energy storage device to increase the capacity of the controllable load, so that it can cooperate to compensate for the bus voltage to break through the limit and recover to near the rated value.

[0220] 7. Simulation Analysis of Off-grid Wind-solar-hydrogen Storage DC Microgrid:

[0221] For the off-grid wind-solar-hydrogen storage DC microgrid system studied in this invention, the following four control strategies are applied in the simulation analysis to verify the effectiveness of the proposed control strategy.

[0222] Table 2 Configuration Table of Simulation Conditions

[0223]

[0224] Now set the load disturbance condition. At t = 2s, a load of about 700kW is connected to the off-grid wind-solar-hydrogen storage DC microgrid system and removed after 0.3s. Figure 14 This is the waveform diagram of the bus voltage for different control strategies under this simulation condition.

[0225] Table 3 Maximum and Minimum Values of DC Bus Voltage during Load Disturbance

[0226]

[0227] By analyzing and comparing Control Strategy ①, ③ and Control Strategy ②, ④, it can be seen that the controllable load applying the electric spring device can compensate the DC bus voltage. By comparing Control Strategy ①, ② and Control Strategy ③, ④, it can be known that the current feedforward control strategy based on ESO-Adaptive Backstepping can effectively suppress the bus voltage fluctuation range compared with the traditional PI control, enabling the system to recover to the stable state faster.

[0228] During the process of water electrolysis for hydrogen production, the electrolyzer decomposes water to produce hydrogen, and then this hydrogen is collected and stored in the hydrogen storage tank for use by the fuel cell. When the electrolyzer participates in regulating the DC bus voltage as a controllable load, its hydrogen production efficiency may fluctuate. However, this change has little impact on the operation of the fuel cell extracting hydrogen from the hydrogen storage tank in the short term. In the fourth implementation case, the change in the output power of the fuel cell is shown in Figure 15 . As shown by Figure 15 , the output power of the fuel cell finally stabilizes at about 240kW.

[0229] 8. Summary:

[0230] This invention controls both the power side and the load side of the off-grid wind-solar-hydrogen storage DC microgrid system, enabling the system to dynamically respond to load mutations and other situations, enhancing the robustness of the system in the face of uncertainties, and improving the energy utilization rate. Verified by actual calculation examples, the specific conclusions are as follows:

[0231] (1) Considering the power side, a current feedforward control strategy based on an extended state observer and adaptive backstepping control is proposed on the DC / DC converter side to observe the states of the system in real time. The results show that under this control strategy, the time required for the bus voltage of the system to reach stability in the face of disturbances is reduced by 46.8% compared with traditional PI control. For a hydrogen fuel cell, the power generation can reach 240 kW.

[0232] (2) Considering the load side, a power spring using adaptive fuzzy control is used to compensate the bus voltage, and an electrochemical energy storage is added to cooperate with the electrolyzer as a controllable load to participate in voltage regulation to break the capacity limit of the controllable load.

Claims

1. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side is characterized by: Establish a mathematical model for off-grid wind-solar hydrogen storage system; Construct a DC / DC converter model, including a fuel storage unit converter model and a battery unit DC / DC converter model; add an extended state observer to the power source side and set an adaptive backstepping control strategy; An adaptive fuzzy controlled electric spring device is added on the load side to compensate the bus voltage.

2. According to claim 1, the DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side is characterized by: In the off-grid wind-solar hydrogen storage system, the wind power generation module and the photovoltaic power generation module serve as the primary power source of the system, respectively used to convert renewable energy into electrical energy and inject it into the AC bus through the inverter; due to the volatility of wind and solar power generation, a hybrid energy storage system is added to smooth power fluctuations and ensure continuous power supply; the hybrid energy storage system consists of two parts: a hydrogen energy storage part and an electrochemical energy storage part, wherein the electrochemical energy storage part includes a lithium battery; the hydrogen energy storage part includes an electrolyzer, a hydrogen storage tank, and a fuel cell.

3. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 2 is characterized by: The mathematical model of the off-grid wind-solar hydrogen storage system includes: (a). Wind power generation module: The output power of a direct-drive permanent magnet synchronous wind turbine is related to wind turbine parameters, air density, and current wind speed, and can be expressed as: Among them, P m is the output power of the wind turbine, C P (λ,β) is the wind energy utilization coefficient, ρ is the air density, R is the radius of the wind wheel, and v is the wind speed; (b). Photovoltaic power generation module: The output current of the photovoltaic module is: Among them, I ph is the light current, I0 is the saturation current of the photovoltaic module, q is the electron charge, V is the output voltage of the photovoltaic module, R s is the series resistance n is the diode ideality factor, N s is the number of photovoltaic modules in series, K is the Boltzmann constant, T is the operating temperature, I sh is the current through the parallel resistor; (c). Electrolysis hydrogen production module: Considering the electrolytic hydrogen production system as a DC load, the higher the input current and voltage, the higher the hydrogen production efficiency; the UI characteristic equation of the electrolyzer is: IN el =U eloc +U act +U ohm (13); Where: U el is the voltage at the electrolytic cell terminal; U eloc is the open circuit voltage of the electrolytic cell, U act U is the activation polarity voltage, which is the voltage generated when the electrochemical reaction occurs in the electrolytic cell; ohm It is the ohmic polarity voltage, which is the voltage generated by the internal resistance of the electrolytic cell; the open circuit voltage U eloc The expression is: Where: I elec is the inductor current; U rev is the reversible voltage of the electrolytic cell under normal working conditions; r1 and r2 are the internal resistance parameters of the electrolytic cell affected by temperature; T elec is the working temperature of the electrolytic cell; A elec is the electrolytic cell area; t1 is the electrolytic cell start-up time; t2 is the electrolytic cell operating temperature reaching T elec Time required; t3 indicates that the working temperature of the electrolytic cell reaches Time required; Activation polarity voltage U act The expression is: Where: R a is the gas constant; T elec is the cell temperature; F is the Faraday constant (F = 96500C / mol); α is the transfer coefficient, i is the current density, and i0 is the exchange current density; Ohmic polarity voltage U ohm The expression is: Where: i is the current density, t m is the thickness of the proton exchange membrane, σ m is the film resistivity; Hydrogen production flow W elec The expression is: Where: N c is the number of single electrolytic units in the electrolytic cell; η F is the Faraday efficiency affected by current density; f1 and f2 are parameters for measuring Faraday efficiency; The reversible voltage U of the electrolytic cell under normal working conditions rev The expression is: Where: △G elec is the Gibbs free energy change of the electrochemical reaction in the electrolyzer under normal working conditions; z is the number of electrons transferred in each chemical reaction during the electrolytic hydrogen production process; F is the Faraday constant; (d) Fuel cell unit: Where: n d is the ion pulling coefficient; M H2O is the molar mass of water, N c is the number of electrolytic cell units, A elec is the electrolytic cell area, t m is the thickness of the proton exchange membrane, D w is the diffusion rate of water in the membrane, C wa and C wc are the water concentrations on the anode and cathode membrane surfaces, respectively; (e) Lithium battery: Lithium batteries are used as electrochemical energy storage, in which, during discharge, i * >0; When charging (i * <0); Where: i t is the actual charge capacity of the battery, i * is the filter current, i is the battery current, E0 is the constant voltage, K * is the polarization constant, Q is the battery capacity, A is the exponential voltage, and B is the inverse ratio of the exponential region time; f1(i t 、i、i * ) represents the state function of the lithium battery during discharge; f2(i t 、i、i * ) represents the state function of the lithium battery during charging.

4. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 3 is characterized by: In the off-grid wind-solar-hydrogen storage DC microgrid system, the electrochemical energy storage part has two functions: first, when electricity consumption is low, wind and solar power is supplied to users and electrolyzers to produce hydrogen, and the excess electricity is stored in the electrochemical energy storage part; second, when electricity consumption is peak, the wind and solar output cannot meet the load demand, and electricity is transmitted from the electrochemical energy storage part to the electricity load for use; therefore, the electrochemical energy storage part is equipped with a DC / DC bidirectional converter to meet its charging and discharging needs.

5. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 4 is characterized by: In the electrochemical energy storage part, C bus , C are the input and output filter capacitors respectively, switch tube 1 is the main switch tube, and switch tube 2 plays a freewheeling role; when switch tube 1 is turned on, inductor L stores energy, and when switch tube 1 is turned off, inductor L and the power supply jointly provide energy to the lithium battery.

6. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 5 is characterized by: When charging, the DC / DC bidirectional converter on the lithium battery side in the electrochemical energy storage part is configured as a Buck converter mode; its mathematical expression is: In formula (4): i ch is the current flowing through the battery during charging, V ref is the reference value of the DC bus voltage, i in is the input current on the grid side; d is the on-time ratio of the switch tube, i.e., the duty cycle; i s is the current of the input converter, v o and i ch are the voltage and current of the output converter respectively; k vp , k vi Respectively represent the proportional gain and integral gain in the voltage loop PI controller; k ip , k ii Respectively represent the proportional gain and integral gain of the current loop PI controller; i L Represents the current flowing through the inductor L; V bus Indicates bus voltage; R out Indicates the resistance value of resistor R; C bus Represents the converter input filter capacitor; i ref Represents the current reference value passing through the PI controller; i ch (τ) represents the current function related to the charging time τ; When charging, if the duty cycle of switch 1 is set to d, the duty cycle of switch 2 is 1-d accordingly. At this time, the DC / DC bidirectional converter is configured as a Boost converter mode; its mathematical expression is:

7. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 6 is characterized by: Since the voltage level of the connected bus is 10kV, the DC / DC converter of the fuel cell unit is configured as a Boost converter. When the switch tube is triggered to the on state, the fuel cell charges the inductor L1 and the capacitor C1 discharges. When the switch tube is turned off due to reverse pressure, the fuel cell and the inductor L1 simultaneously supply energy to the bus and charge the capacitor C1. The inductor L1 and the capacitor C1 play the role of boosting and stabilizing the voltage output respectively. In formula (6): V out is the fuel cell output voltage; I1 is the boost circuit current; V1 is the boost circuit output voltage; d is the duty cycle; t on is the conduction time of the switch tube; t off is the switch off time.

8. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 1 is characterized by: Design n-order time-varying nonlinear systems for the design of extended state observers; The expression of the n-order nonlinear system is shown in formula (1): x (n) (t)=f(x(t),…,x (n-1) (t)+w(t)+bu(t))(1); In formula (1): x(t),…,x (n-1) (t) represents the system state variable that can be directly measured; w(t) represents the external disturbance to the system, which has uncertainty characteristics; u(t) represents the control input of the system; b represents the gain coefficient of the control input; Let x1(t)=x(t),x2(t)=x′(t),…,x n (t) = x (n-1) (t), where; x1(t), x2(t)…, x n (t) represents the estimated values ​​of the 1st, 2nd, ..., nth system state variables respectively; Then the state expression of formula (1) is shown as formula (2): In formula (2): Respectively represent the derivatives of the estimated values ​​of the 1st, 2nd, ..., nth system state variables; x i They represent the actual values ​​of the 1st, 2nd, ..., nth system state variables respectively; f(x1, x2, ..., x n ) represents the unknown dynamics and external disturbances of the system; y represents the output of the system; In formula (2), the nonlinear part and the disturbance of the system are denoted as a(t), then a(t) = f(x(t),…,x (n-1) (t))+w(t), so formula (2) can be expressed as formula (3): For the system described by formula (2), the corresponding extended state observer is constructed; In formula (7): They represent the derivatives of the 1st, 2nd, ...nth system state variables processed by the extended state observer respectively; represents the derivatives of n+1 system state variables processed by the extended state observer; k1, k2, ... k n Respectively represent the gain coefficients of the 1st, 2nd, ...nth order control input; k n+1 represents the gain coefficient of the n+1th order control input; g(z1-x(t)) represents the nonlinear function related to the error; g'(z1-x(t)) represents the derivative of the nonlinear function related to the error; x(t) represents the state variable of the system.

9. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 8 is characterized by: An adaptive backstepping control strategy is designed for the system shown in equation (2): The adaptive backstepping control law is: In formula (8), bu represents the system control law; e2 represents the second system error; e represents the first system error; f2(x1,x2) represents the nonlinear part of the system; represents the estimation error; represents the virtual control quantity; The parameter update rate is: In formula (9): represents the derivative of the estimation error; γ represents the parameter adjustment rate.

10. The DC bus voltage control strategy of the off-grid wind-solar hydrogen storage system based on the power side and the load side according to claim 9 is characterized in that: Electric spring device with adaptive fuzzy control added on the load side: The adaptive fuzzy control strategy is applied to the electric spring device. By adjusting the power output of the electric spring device, the stable control of the grid voltage is achieved. The relationship between the electric spring device and the controllable load voltage is shown in formula (10): Where, u1 is the node voltage on the input side; u EL is the load terminal voltage at the output side; C1, C L are the input and output capacitors respectively; D is the duty cycle of the controller; i1 represents the input current; i o Represents the output current; i EL Represents the current flowing into the controllable load; L represents the filter inductance.

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