A wind, light, hydrogen, and storage direct-current micro-grid distributed control method and system

Abstract

The application provides a wind, light, hydrogen and storage direct-current micro-grid distributed control method and system, and belongs to the technical field of micro-grid distributed control, wherein the method comprises the following steps: adopting an EPP-MPPT algorithm based on a Newton-Raphson method to perform maximum power output control on a photovoltaic generator set in a photovoltaic power generation unit, adopting an IMPPT algorithm to perform maximum power output control on a wind power generator in a wind power generation unit, adopting a distributed power supply control strategy to adjust power imbalance among distributed power generation units, load demand and energy storage equipment, and dividing the whole power balance control process into an active part and a passive part. The application can effectively solve the current overshoot or undershoot problem of the traditional bus voltage signal control under the system power mutation of the distributed energy storage equipment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of micro-grid distributed control, and particularly relates to a wind, light, hydrogen and storage direct-current micro-grid distributed control method and system. BACKGROUND

[0002] With the development of new energy, distributed generation represented by photovoltaic and wind turbines has the advantages of low transmission cost, flexible installation location, etc., and has developed rapidly. Micro-grid, as an effective management mode of distributed renewable energy and load, includes direct-current micro-grid and alternating-current micro-grid. Compared with alternating-current micro-grid, direct-current micro-grid has no frequency and phase problems, and has relatively low control complexity. Moreover, direct-current micro-grid has low loss, high efficiency, flexible operation and simple control, and has become an important form of distributed energy development. However, due to the randomness and intermittency of distributed generation, energy storage devices are usually required to suppress the power fluctuation of micro-grid. In recent years, hydrogen energy has become an energy carrier for promoting large-scale development of renewable energy due to its low-carbon, clean, flexible and efficient advantages, and distributed energy systems based on hydrogen fuel cell technology have become a research hotspot. The existing distributed control based on direct-current micro-grid mostly uses a hybrid energy storage system composed of super-capacitors (SC) and batteries. Although batteries can meet the demand of balancing system power changes, the cost of batteries is proportional to the capacity. In comparison, hydrogen tanks bring greater storage capacity while having lower investment cost and being more flexible and efficient. However, the electro-hydrogen conversion device of electrolysis cell (EC) and fuel cell (FC) responds slowly due to the influence of internal ion flow. The high energy density and fast response characteristics of SC can make up for the shortcomings of EC and FC.

[0003] The control of direct-current micro-grid mainly includes power output control of distributed generation units and control of energy storage systems to maintain the stability of direct-current bus voltage. The energy storage system needs to handle two different energy fluctuations: one is to manage the energy flow between generation units and load demand, and the other is to balance the internal energy fluctuation of micro-grid system. Due to the intermittency and uncertainty of output power of photovoltaic units and wind turbines, it is necessary to carry out in-depth research on the control of hybrid energy storage direct-current micro-grid system composed of SC, EC and FC.

[0004] Some researchers have explored the control of DC microgrids. For the control of distributed generation units, the classic perturbation and observation (P&O) method and tip speed ratio control (TSR) methods are effective for maximum power point tracking (MPPT) control, but tracking speed and accuracy need further improvement. Energy storage system control is primarily categorized into centralized and distributed approaches. In centralized control, the controller collects necessary information, such as voltage and current, performs centralized calculations, and controls the converters based on the results. This approach is suitable for optimizing the entire system, but requires fast communication between the central controller and the devices implementing the control. This communication requirement becomes increasingly difficult to meet as the number of energy storage devices increases. In distributed control, each device connected to the bus is responsible for stabilizing the bus voltage. Droop control is typically used to achieve local autonomy for each distributed unit. However, droop control presents a trade-off between current distribution accuracy and bus voltage regulation deviation. In addition, some scholars use the DC bus as a communication tool between distributed controllers to perform simple and effective control of distributed devices based on bus signals. However, when the DC bus voltage undergoes frequent mutations, distributed energy storage devices and generators need to frequently and quickly respond to system fluctuations, which places high requirements on the response speed of each distributed energy storage device. In addition, sudden changes in the DC bus voltage can cause noise-related problems, such as current overshoot or undershoot problems in distributed energy storage devices. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, this application proposes a distributed control method and system for a wind, solar, hydrogen and DC storage microgrid.

[0006] In the first aspect, the present application proposes a distributed control method for a wind, solar, hydrogen, and DC storage microgrid, comprising:

[0007] Connecting the DC bus to a distributed power generation unit, an energy storage device, and a load through a converter, wherein the distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; and the energy storage device includes a supercapacitor, an electrolyzer, and a hydrogen fuel cell;

[0008] An EPP-MPPT algorithm based on the Newton-Raphson method is used to control the maximum power output of a photovoltaic generator set in a photovoltaic power generation unit. The EPP-MPPT algorithm based on the Newton-Raphson method includes: using the Newton-Raphson method to determine a changing disturbance step size, performing an estimation model once every two disturbance patterns, and using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit according to the changing disturbance step size, and outputting the maximum output power of the photovoltaic power generation unit;

[0009] An IMPPT algorithm is used to control the maximum power output of a wind turbine in a wind power generation unit. The IMPPT algorithm includes: adjusting an optimal reference value of a rotor speed of the wind turbine using the IMPPT algorithm based on the dynamic characteristics of the wind turbine, and obtaining the maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed;

[0010] Under the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit, a distributed power supply control strategy is used to adjust the power imbalance between the distributed power generation units, load demands and energy storage devices. The distributed power supply control strategy includes: dividing the entire power balance control process into an active part and a passive part, the active part includes: changing the DC bus voltage by detecting the current of the converter connected to the supercapacitor, the corresponding converter is a main voltage controller, and the main voltage control is used to compensate for high-frequency fluctuations; the passive part includes: controlling the input current of the electrolyzer or the output current of the fuel cell by measuring the DC bus voltage, the corresponding converter is a secondary voltage controller, until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced, the control process is stopped, and the secondary voltage controller is used to compensate for low-frequency fluctuations and continuous excess or insufficient power.

[0011] The Newton-Raphson method is used to determine the perturbation step size, and the calculation formula is as follows:

[0012]

[0013] Among them, ΔV is the perturbation step size, P pv(n) is the output power of the photovoltaic power generation unit under the nth disturbance, P pv(n-1) is the output power of the photovoltaic power generation unit under the n-1th disturbance, grad n is the ratio of the photovoltaic output power change to the voltage change under the nth disturbance, grad n-1 It is the ratio of the photovoltaic output power change to the voltage change under the n-1th disturbance.

[0014] The maximum power point of the photovoltaic power generation unit is tracked using the EPP-MPPT algorithm according to the changing disturbance step size, including: if the output voltage of the photovoltaic power generation unit increases, resulting in an increase in the photovoltaic output power, then continue to search for the voltage corresponding to the maximum power in the same direction in the current step; otherwise, search for the voltage corresponding to the maximum power in the opposite direction.

[0015] The IMPPT algorithm is used to adjust the optimal reference value of the rotor speed of the wind turbine according to the dynamic characteristics of the wind turbine, including:

[0016] Get the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2;

[0017] Taking the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2 as input, the optimal reference value of the rotor speed at each moment is calculated based on the power coefficient of the wind turbine generator in the wind power generation unit and the optimization function of the IMPPT algorithm;

[0018] The optimal reference value is output at time t0, and is not output at time t1 and time t2.

[0019] The optimization function of the IMPPT algorithm is calculated as follows:

[0020]

[0021] Constraints:

[0022] C p (λ,β)=0.5176(116 / λ-0.4β-5)e -21 / λ +0.0068λ

[0023]

[0024] 0≤ω(t)≤ω max ,t0≤t≤t2.

[0025]

[0026] Among them, ω ref is the rotor speed reference value, v1 is the predicted wind speed at time t1, v2 is the predicted wind speed at time t2, β is the pitch angle of the blade, ρ is the air density, R is the turbine radius, C p is the power coefficient, ω is the rotor speed, v is the actual wind speed input at time t0, ω0 is the rotor speed at time t0, ω max is the maximum rotor speed, J is the turbine inertia moment, λ=ωR / ν is the tip speed ratio (TSR), T is one cycle, ω ref1 and ωref2 are the reference values ​​of rotor speed under different wind speeds, f(ω) represents C p (λ,β).

[0027] The DC bus voltage is changed by detecting the current of the converter connected to the supercapacitor. The frequency domain DC bus voltage under the control of the main voltage controller is calculated as follows:

[0028]

[0029] Among them, V bus is the DC bus voltage, is the bus voltage reference value, I MVC Current of the main voltage controller, K IV Proportional gain of the main controller MVC.

[0030] The current calculation formula of the main voltage controller is as follows:

[0031]

[0032] Among them, I MVC is the input or output current of the converter connected to the supercapacitor, that is, the current of the main voltage controller, is the time constant, ΔI is the input and output current difference of the DC bus, is the voltage reference value of the kth sub-voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the kth voltage controller, s is the complex frequency, is the DC bus center voltage.

[0033] The passive part includes: controlling the input current of the electrolyzer or the output current of the fuel cell by measuring the DC bus voltage. The corresponding converter is a secondary voltage controller, and the current calculation formula of the secondary voltage controller is as follows:

[0034]

[0035] in, is the input or output current of the converter of the electrolyzer or hydrogen fuel cell, that is, the current of the i-th voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the ith sub-voltage controller, is the DC bus center voltage, is the bus voltage reference value, is the proportional gain of the kth secondary voltage controller, which is the same as the time constant of the main voltage controller, s is the complex frequency, is the time constant, which is the same as the time constant of the main voltage controller.

[0036] The control process is stopped until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced. The expression is as follows:

[0037]

[0038] Among them, I MVC is the current of the main voltage controller, is the time constant, K IV is the proportional gain of the main controller, is the proportional gain of the kth voltage controller, s is the complex frequency, is the DC bus center voltage, is the voltage reference value of the kth secondary voltage controller.

[0039] Secondly, this application proposes a wind, solar, hydrogen, and DC storage microgrid distributed control system, including:

[0040] A connection module is used to connect the DC bus to the distributed power generation unit, the energy storage device and the load through the converter. The distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; the energy storage device includes: a supercapacitor, an electrolyzer and a hydrogen fuel cell;

[0041] A photovoltaic unit control module is used to perform maximum power output control on the photovoltaic generator set in the photovoltaic power generation unit using an EPP-MPPT algorithm based on the Newton-Raphson method. The EPP-MPPT algorithm based on the Newton-Raphson method includes: using the Newton-Raphson method to determine a varying disturbance step size, performing an estimation model once every two disturbance patterns, and using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit based on the varying disturbance step size, thereby outputting the maximum output power of the photovoltaic power generation unit;

[0042] a wind turbine control module, configured to use an IMPPT algorithm to perform maximum power output control on a wind turbine in a wind power generation unit, wherein the IMPPT algorithm comprises: adjusting an optimal reference value of a rotor speed of the wind turbine using the IMPPT algorithm based on dynamic characteristics of the wind turbine, and obtaining a maximum output power of the wind power generation unit based on the optimal reference value of the rotor speed;

[0043] The distributed power supply control module is used for adjusting power imbalance among the distributed power generation unit, load demand and energy storage device under the condition of maximum output power of the photovoltaic power generation unit and maximum output power of the wind power generation unit, and the distributed power supply control strategy comprises: dividing the whole power balance control process into an active part and a passive part, the active part comprises: changing the DC bus voltage by detecting the current of the converter connected to the super capacitor, and the corresponding converter is a main voltage controller, and the main voltage control is used for compensating high-frequency fluctuation; the passive part comprises: controlling the input current of the electrolytic cell or the output current of the fuel cell by measuring the DC bus voltage, and the corresponding converter is a secondary voltage controller, and when the converter current of the DC bus connected to the super capacitor is zero, that is, when the system is in power balance, the control process is stopped, and the secondary voltage controller is used for compensating low-frequency fluctuation and continuous excess or insufficient power.

[0044] Beneficial effects:

[0045] The application provides a wind, light, hydrogen and storage DC micro-grid distributed control method and system. The EPP-MPPT algorithm based on the Newton-Raphson method is designed for MPPT control of a photovoltaic power generation unit to improve the traditional perturbation and observation method. The algorithm uses an estimation mode once every two times of perturbation mode, can quickly respond to changes in temperature and irradiance, and adopts the Newton-Raphson method to determine a variable perturbation step, thereby replacing the traditional fixed-step perturbation, and achieving double improvement in tracking accuracy and speed of MPPT. In the design of an MPPT controller of a wind power generation unit, an IMPPT algorithm is designed to improve system efficiency according to the dynamic characteristics of a large wind turbine. The algorithm comprehensively considers short-term wind speed prediction, maximum wind energy capture and dynamic response of a wind turbine, and adjusts the optimal reference value of the rotor speed in an intelligent manner, so that the movement of the rotor is shortened in the dynamic tracking process, and mechanical fatigue is reduced. According to power imbalance among the distributed power generation unit, load demand and energy storage device, a small voltage change generated by a main voltage controller (MVC) connected to a bus is used as a signal to control an SVC device, so that the current overshoot or undershoot of the distributed energy storage device under the control of the traditional bus voltage signal in the system power mutation can be effectively solved. In addition, the wind, light, hydrogen and storage DC micro-grid distributed control method and system have the advantages that the small voltage change generated by the MVC connected to the bus is used as the signal to control the SVC device, the control mode is simple and efficient, a central processing unit is not needed, and communication is not needed between converters. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A wind, light, hydrogen and storage DC micro-grid distributed control method according to an embodiment of the application;

[0047] Figure 2 A flow chart of a distributed control method for a wind, solar, hydrogen, and DC storage microgrid according to an embodiment of the present application;

[0048] Figure 3 An example flow chart of an EPP-MPPT algorithm based on the Newton-Raphson method according to an embodiment of the present application;

[0049] Figure 4 Schematic diagram of the IMPPT algorithm of the embodiment of the present application;

[0050] Figure 5 Schematic diagram of a maximum power point tracking simulation example under EPP-MPPT of a photovoltaic power generation unit according to an embodiment of the present application;

[0051] Figure 6 Schematic diagram of a maximum power point tracking simulation example under IMPPT of a wind power generation unit according to an embodiment of the present application;

[0052] Figure 7 A schematic diagram of a distributed control simulation example of a wind, solar, hydrogen, and DC storage microgrid according to an embodiment of the present application;

[0053] Figure 8 A block diagram of the principles of a wind, solar, hydrogen, and DC storage microgrid distributed control system in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The specific implementation methods of the present application are further described in detail below with reference to the accompanying drawings and examples.

[0055] This application proposes a distributed control method and system for a wind, solar, hydrogen, and DC storage microgrid. First, the MPPT control algorithms for photovoltaic units and wind turbines are improved. A Newton-Raphson-based "Estimation-Perturbation-Perturbation" (EPP) MPPT algorithm and an intelligent MPPT (IMPPT) algorithm are proposed, respectively, to improve upon traditional P&O and TSR methods. Then, based on the power flow imbalance between distributed generation units, load demand, and energy storage devices, the converter connected to the supercapacitor is configured as a primary voltage controller (MVC) to respond to high-frequency fluctuations in the system, while the controllers connected to the electrolyzer and fuel cell are configured as secondary voltage controllers (SVCs) to respond to low-frequency fluctuations in the system. The SVC devices are controlled using tiny voltage changes generated by the MVC connected to the busbar as signals. The entire control process is divided into active and passive parts. This effectively addresses the current overshoot or undershoot issues of distributed energy storage devices under traditional busbar voltage signal control when the system power suddenly changes. The control method is simple and efficient, requiring no central processing unit (CPU), and no communication between converters.

[0056] Example 1:

[0057] This embodiment proposes a distributed control method for a wind, solar, hydrogen, and DC storage microgrid. Figure 1 、 Figure 2 Shown, including:

[0058] Step S1: Connecting the DC bus to the distributed power generation unit, the energy storage device and the load through the converter, wherein the distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; the energy storage device includes a supercapacitor, an electrolyzer and a hydrogen fuel cell;

[0059] In this embodiment, the DC bus connects distributed generation units, energy storage devices, and loads via a converter. This control system employs hybrid energy storage, with the energy storage devices comprising supercapacitors, electrolyzers, and hydrogen fuel cells. The distributed generation units, including photovoltaic and wind power units, are connected to the DC bus via input converters and operate in MPPT mode.

[0060] Step S2: Using an EPP-MPPT algorithm based on the Newton-Raphson method to control the maximum power output of the photovoltaic generator set in the photovoltaic power generation unit, the EPP-MPPT algorithm based on the Newton-Raphson method includes: using the Newton-Raphson method to determine the changing disturbance step size, performing an estimation model once every two disturbance patterns, and using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit according to the changing disturbance step size, and outputting the maximum output power of the photovoltaic power generation unit;

[0061] In this embodiment, an EPP-MPPT algorithm based on the Newton-Raphson method is proposed in the design of an MPPT controller for a photovoltaic power generation unit, which improves the traditional perturbation and observation method. The EPP-MPPT algorithm based on the Newton-Raphson method is used to control the maximum power output of the photovoltaic power generation unit. The EPP-MPPT algorithm based on the Newton-Raphson method includes: the algorithm uses an estimation mode once every two perturbation modes, which can respond quickly to changes in temperature and irradiance intensity. At the same time, the Newton-Raphson method is used to determine the variable perturbation step size, replacing the traditional fixed-step perturbation, which can achieve a double improvement in MPPT tracking accuracy and speed.

[0062] Step S2.1: Determine the perturbation step size using the Newton-Raphson method;

[0063] In this embodiment, the Newton-Raphson method is used for calculation. This method provides a prediction method to calculate the effective step size. By using the EPP-MPPT algorithm under a variable step size, the maximum power point can be tracked faster and with higher accuracy. First, the Newton-Raphson method is used to converge the solution. The calculation formula is as follows:

[0064]

[0065]

[0066] Among them, X n is the disturbance variable, X n+1 is the updated disturbance variable, F(X n ) is the ratio of the power change to the voltage change under two adjacent disturbances, and grad n The meaning of P is the same. pv(n) is the output power of the photovoltaic power generation unit under the nth disturbance, P pv(n-1) is the output power of the photovoltaic power generation unit under the n-1th disturbance, V pv(n) is the voltage of the photovoltaic power generation unit under the nth disturbance, V pv(n-1) is the voltage of the photovoltaic power generation unit under the n-1th disturbance, F'(X n ) is the derivative of the function, and the calculation formula is as follows:

[0067]

[0068] We can further obtain:

[0069]

[0070] The voltage reference value can be further obtained:

[0071]

[0072] Where ΔV is the perturbation step size, i.e., the change step size calculated in step S2.1. Based on equations (1), (2), (4), and (5), we can derive:

[0073]

[0074] Among them, ΔV is the perturbation step size, P pv(n) is the output power of the photovoltaic power generation unit under the nth disturbance,

[0075] P pv(n-1) is the output power of the photovoltaic power generation unit under the n-1th disturbance, grad n is the ratio of the photovoltaic output power change to the voltage change under the nth disturbance, grad n-1 It is the ratio between the change in photovoltaic output power and the change in voltage under the n-1th disturbance.

[0076] The perturbation step size at each iteration is calculated using Equation (6). Using this variable step size, the maximum power point tracker will converge to the maximum power value faster and with smaller power fluctuations.

[0077] Step S2.2: According to the changed disturbance step size, the maximum power point of the photovoltaic power generation unit is tracked using the EPP-MPPT algorithm to output the maximum output power of the photovoltaic power generation unit.

[0078] In this embodiment, the EPP-MPPT algorithm is used to obtain the maximum power point: if the output voltage of the photovoltaic power generation unit increases, resulting in an increase in the photovoltaic output power, then in the current step, the search for the voltage corresponding to the maximum power continues in the same direction; otherwise, the search for the voltage corresponding to the maximum power is carried out in the opposite direction.

[0079] In order to more intuitively describe the specific implementation process of step S2, an example is given to illustrate the specific process in detail. Figure 3 Shown, including:

[0080] Step S100: starting the EPP-MPPT algorithm;

[0081] Step S101: Measure V pv(n) , I pv(n) , where I pv(n) is the current of the photovoltaic power generation unit under the nth disturbance;

[0082] Step S102: Calculate power P pv(n) =V pv(n) ×I pv(n) ;

[0083] Step S103: Determine the counter value. If the value is 0, enter the estimation mode and go to step S113; if the value is 1, enter the disturbance mode and go to step S123; if the value is 2, enter the disturbance mode and go to step S133;

[0084] Step S113: Set the counter value to 1 and calculate n=n+1, return to step S101 and enter the next loop;

[0085] Step S123: Set the counter value to 2 and calculate dP pv =P pv (n)-P pv (n-1);

[0086] Step S124: Determine whether the calculation formula is valid. pv (n-1)>P pv (n-2)-dP pv If so, go to step S125; if not, go to step S126;

[0087] Step S125: Determine the calculation formula V pv (n-1)>V pv (n-2) is true, if true, calculate And n=n+1, return to step S101 and enter the next loop; if not, calculate And n=n+1, return to step S101 and enter the next loop;

[0088] Step S126: Determine the calculation formula V pv (n-1)>V pv (n-2) is true, if true, calculate And n=n+1, return to step S101 and enter the next loop; if not, calculate And n=n+1, return to step S101 and enter the next loop;

[0089] Step S133: Determine whether the calculation formula is valid. pv (n-1)>P pv (n-2)-dP pv If so, go to step S134; if not, go to step S135;

[0090] Step S134: Determine the calculation formula V pv (n)>V pv (n-1) is true, if true, calculate And n=n+1, return to step S101 and enter the next loop; if not, calculate And n=n+1, return to step S101 and enter the next loop;

[0091] Step S135: Determine the calculation formula V pv (n)>V pv (n-1) is true, if true, calculate And n=n+1, return to step S101 and enter the next loop; if not, calculate And n=n+1, return to step S101 and enter the next loop;

[0092] It can be seen that in this example, the change step size ΔV is calculated according to calculation formula (6). According to the above process, it is determined whether the change step size ΔV needs to be increased or decreased under specific circumstances. The EPP-MPPT algorithm is used to track the maximum power point to obtain the maximum power output of the photovoltaic power generation unit.

[0093] Step S3: Using an IMPPT algorithm to perform maximum power output control on the wind turbine in the wind power generation unit, the IMPPT algorithm comprising: adjusting an optimal reference value of the rotor speed of the wind turbine based on the dynamic characteristics of the wind turbine, and obtaining the maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed;

[0094] In this embodiment, an IMPPT algorithm is used to control the maximum power output of a wind turbine. The IMPPT algorithm includes: targeting the dynamic characteristics of large wind turbines, the IMPPT algorithm intelligently adjusts the optimal reference value of the rotor speed, thereby shortening the tracking process of reaching the maximum power point and improving the overall efficiency of the system.

[0095] The IMPPT algorithm adjusts the optimal reference value of the rotor speed in an intelligent way, thereby shortening the tracking process to reach the maximum power point, such as Figure 4 Shown, including:

[0096] Step S3.1: Obtain the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2;

[0097] Step S3.2: Taking the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2 as input, the optimal reference value of the rotor speed at each time is calculated based on the power coefficient of the wind turbine generator in the wind power generation unit and the optimization function of the IMPPT algorithm;

[0098] Step S3.3: Output the optimal reference value at time t0, use it as prediction input at time t1 and time t2, and do not perform output;

[0099] In this embodiment, in the design of the MPPT controller of the wind power generation unit, an intelligent IMPPT algorithm is proposed to improve the system efficiency based on the dynamic characteristics of large wind turbines. The algorithm is inspired by the speed calculation formula v=v0+at in physics. Unlike the existing method of simply increasing the acceleration a, the IMPPT adjusts the initial speed v0 in an intelligent manner, thereby shortening the maximum power tracking process and improving the overall efficiency of the system.

[0100] Among them, the optimization function derivation process of the IMPPT algorithm is as follows:

[0101] Step S200: Wind power generation system modeling:

[0102] Step S200.1: Wind speed modeling:

[0103] The actual wind speed model is relatively complex and is affected by many factors such as geographical location, climate characteristics, height above the ground, surface topography, etc. Wind speed can be modeled as two components as follows:

[0104] v=v m +v d (7)

[0105] Among them, v is the actual wind speed, v m is the average wind speed of the long-term slowly varying component, v d is the disturbance of the rapidly changing component.

[0106] Usually m is assumed to be a measurement or estimate at a given location and time period, v d is designed to be Gaussian, for example, d~N(μ,Σ). In this application, the empirical design is replaced by a norm-bounded uncertainty model. No assumptions are made about the distribution of the disturbance, but the amplitude of the disturbance is assumed to be bounded, for example, D γ ={d|||d||2≤λ}. This may be more realistic at high altitudes and in the short term.

[0107] Step S200.2: Pneumatic system modeling:

[0108] In an aerodynamic system, the mechanical power drawn from a wind turbine is described by:

[0109]

[0110] C p(λ,β)=0.5176(116 / λ-0.4β-5)e -21 / λ +0.0068λ (9)

[0111]

[0112] Among them, P t is the mechanical power of the wind turbine, ρ is the air density, R is the turbine radius, C p is the power coefficient, λ = ωR / ν is the tip speed ratio (TSR), ω is the turbine angular velocity, and β is the pitch angle.

[0113] The turbine torque is then expressed as:

[0114]

[0115] In addition, T t is the turbine torque, the wind turbine is at the maximum power coefficient point C pmax (λ opt ,β opt ) can generate the maximum power when running at opt =ω opt R / ν. The maximum output power of the wind turbine is calculated as follows:

[0116]

[0117] Among them, P tmax is the maximum output power of the wind turbine, ω opt is the turbine angular velocity corresponding to the maximum power coefficient point, k opt is the maximum power factor.

[0118] Step S200.3: Blade spacing system modeling:

[0119] The blade pitch system, the physical limitations of the pitch angle operating range, and the pitch angle rate are calculated as follows:

[0120]

[0121] β min ≤β≤β max (14)

[0122]

[0123] in, is the pitch angular rate, τ β is the time constant of the blade pitch system, β * is the desired pitch angle, and For the upper and lower bounds.

[0124] Step S200.4: Turbine mechanical system modeling:

[0125] Since the rotor and generator of a wind turbine are directly connected, a single-mass model is chosen to represent the dynamics of the drive train, as shown below:

[0126] ω=(T t -T g -Fω) / J (16)

[0127]

[0128] Among them, T g is the generator torque, T rat is the rated torque of the generator, J is the moment of inertia of the turbine, F is the coefficient of viscous friction, v ci 、v rat and v ct They are cut-in speed, rated speed and cut-out speed, which divide the system into three operating areas.

[0129] If the friction torque is ignored, equation (16) can be rewritten as:

[0130]

[0131] in, is the turbine angular velocity when the friction torque is neglected.

[0132] Step S200.5: Electrical system:

[0133] Since the electrical dynamics of the generator are faster than the mechanical dynamics of the turbine, (19) uses a first-order model to represent the electrical dynamics, namely:

[0134]

[0135] Among them, T g is the generator torque, is the rated torque of the generator, is the updated generator torque, τ g is the time constant of the power system.

[0136] Step S201: MPPT is essentially a tracking problem, and its success depends largely on the reasonable selection of reference objects. Next, the IMPPT algorithm is designed for the TSR-controlled wind turbine.

[0137] Step S201.1: In TSR control, the maximum wind speed point at different wind speeds is achieved by tracking the optimal rotor speed:

[0138]

[0139] Among them, ω ref is the rotor speed reference value, λ opt is the optimal blade speed ratio.

[0140] With the continuous improvement in the accuracy of short-term wind power generation forecasts in recent years, the IMPPT algorithm proposed in this application fully utilizes this advantage. The turbine characteristic curve is represented as a nonlinear function of wind speed v, turbine angular velocity ω and blade pitch angle β, and the duration is assumed to be two time periods. In each time period, one wind speed and two predicted wind speeds are regarded as input signals, and the optimal control instructions are calculated by the IMPPT algorithm. Here, the scale of each time period should be tens of seconds to minutes, and the predicted wind speed will be continuously updated based on historical data.

[0141] Step S201.2: Unlike traditional TSR, which simply calculates the reference speed, the proposed IMPPT algorithm comprehensively considers short-term wind speed forecasts, maximum wind energy capture, and the dynamic response of the wind turbine, and intelligently executes the optimal reference command. IMPPT then becomes the following optimization problem:

[0142]

[0143] The constraints are:

[0144]

[0145] 0≤ω(t)≤ω max ,t0≤t≤t2. (24)

[0146] Among them, v1 and v2 are the predicted wind speeds at t1 and t2 respectively, v is the actual wind speed input at t0, β is the pitch angle of the blade, ρ is the air density, R is the turbine radius, and C p is the power coefficient, ω is the rotor speed, ω0 is the rotor speed at time t0, J is the turbine moment of inertia, λ=ωR / ν is the tip speed ratio (TSR), T is one cycle, ω ref1 and ω ref2 are the reference values ​​of rotor speed under different wind speeds, ω max is the maximum rotor speed. The elevation angle β is controlled to be zero in the partial load region. Therefore, equations (21)-(24) can be rewritten as:

[0147]

[0148] The constraints are equations (23), (24) and the following equation:

[0149]

[0150] Thus, the optimal reference value of the rotor speed is obtained, so that the movement of the rotor is shortened during the dynamic tracking process, mechanical fatigue is reduced, and intelligent regulation is achieved.

[0151] Step S4: Under the conditions of the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit, a distributed power supply control strategy is used to adjust the power imbalance between the distributed power generation units, load demands and energy storage devices. The distributed power supply control strategy includes: dividing the entire power balance control process into an active part and a passive part, the active part includes: changing the DC bus voltage by detecting the current of the converter connected to the supercapacitor via the DC bus, the corresponding converter is a main voltage controller, and the main voltage control is used to compensate for high-frequency fluctuations; the passive part includes: controlling the input current of the electrolyzer or the output current of the fuel cell via measuring the DC bus voltage, the corresponding converter is a secondary voltage controller, until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced, the control process is stopped, and the secondary voltage controller is used to compensate for low-frequency fluctuations and continuous excess or insufficient power.

[0152] In this embodiment, a distributed power supply control strategy that does not rely on communication is proposed. The distributed power supply control strategy includes: according to the power imbalance between distributed power generation units, load demand and energy storage equipment, the entire power balance control process is divided into two parts, namely the active part and the passive part. Among them, the active part is to change the DC bus voltage by detecting the current of the converter connected to the supercapacitor of the DC bus, and the corresponding converter is the main voltage controller (MVC). The passive part is to measure the DC bus voltage and control the input current of the electrolyzer with large capacity and slow response speed or the output current of the fuel cell. The corresponding converter is the secondary voltage controller (SVC). The process stops until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced. The high-frequency fluctuations of the system are compensated by the main voltage controller, and the low-frequency fluctuations and continuous excess or insufficient power are compensated by the secondary voltage controller. By combining the active and passive parts, the system power balance control can be achieved while effectively solving the problems of distributed power supply output voltage fluctuation and power overshoot or undershoot under traditional bus voltage signal control. The secondary voltage controller is controlled by the small voltage change generated by the main voltage controller connected to the bus as a signal. The control method is simple and efficient, does not require a central processing unit, and no communication is required between converters.

[0153] In this embodiment, the controller connected to the supercapacitor (SC) is configured as a primary voltage controller (MVC) to respond to high-frequency fluctuations in the system, while the distributed controller connected to the electrolyzer (EC) or hydrogen fuel cell (FC) is configured as a secondary voltage controller (SVC) to respond to low-frequency fluctuations in the system. The secondary voltage controller is controlled by using small voltage changes generated by the primary voltage controller connected to the bus as a signal. Based on power imbalances between distributed power sources, load demand, and energy storage devices, the DC bus voltage is modified by detecting the current in the converter connected to the SC. The distributed controller connected to the EC or FC then detects changes in the DC bus voltage and, through the converter, modifies the current between the DC bus and the EC or FC until the current in the converter connected to the SC reaches zero, terminating the process when system power is balanced. The entire control process is divided into two parts: an active part and a passive part. The active part utilizes the SC's fast response speed to control the DC bus voltage to balance power flow, while the passive part measures the DC bus voltage and controls the current in the larger, slower-response EC or FC. The converter between the DC bus and the SC is the primary voltage controller, and the converter between the DC bus and the EC or FC is defined as the secondary voltage controller.

[0154] The principles of the primary and secondary voltage controllers are described in detail below:

[0155] (1) Main voltage controller

[0156] The MVC is responsible for the active part of the power balancing process and needs to quickly respond to power changes in the system to control the bus voltage. SCs are used as the energy storage device corresponding to MVCs due to their high power density and rapid charging and discharging characteristics. Therefore, under the proposed DC bus control, an MVC containing an SC is connected to the DC bus. The DC bus voltage in the frequency domain can be expressed as:

[0157]

[0158] Among them, V bus is the DC bus voltage, K IV (s) is the gain of the controller MVC; G MC (s), G MP (s), H M (s) are the controller, power stage, and feedback transfer function of the converter in the frequency domain; The DC bus center voltage is determined by MVC and is defined as the reference standard when power and load demand are balanced; I MVC is the input or output current of the MVC; is the target voltage.

[0159] From formula (28), we can see that The value of the MVC attached energy storage system I MVC The integral of is proportional to the gain K. IV .K IV The larger it is, the faster the bus voltage changes. With I MVC Increase (decrease) with the increase (decrease) of After that, the other secondary voltage controllers start to absorb (release) energy from the DC bus, which in turn reduces (increases) I MVC This control will result in I MVC The integral of is ultimately a finite value, so will converge to a value that keeps the DC bus balanced with respect to both input and output.

[0160] (2) Secondary voltage controller

[0161] The SVC detects the DC bus voltage and determines the charging or discharging current of the energy storage device. Its frequency domain expression is:

[0162]

[0163] Among them, I SVC is the input or output current of the SVC, is the target current, is the voltage reference value, K VI is the gain of the controller SVC, G SC (s), G SP (s), H S (s) are the controller, power stage and feedback transfer functions of the converter in the frequency domain, respectively. These values ​​are similar to those in MVC.

[0164] From formula (30), we can see that the SVC target current and Proportional to the gain K. VI . Where K VI The value of affects the response speed of the SVC, so a different reference voltage can be selected for each SVC by limiting the fast response Make the system run flexibly.

[0165] SVCs operate in response to bus voltage changes of MVC, so they do not directly control the DC bus voltage, and they are passive parts in the whole bus voltage control process. EC and FC, which are relatively slow in response speed, are selected as energy storage devices, and their main role is to compensate for low-frequency fluctuations and continuous excess or insufficient power in the system. EC and FC are unidirectional energy transmission devices, in which EC absorbs the power on the bus, and FC supplies power to the DC bus. In order to protect the devices from the impact of reverse voltage, the output of the converter needs to be properly limited in one direction. For example, since energy cannot flow from the EC side to the bus, the corresponding SVC is set to allow input and prohibit output. Similarly, energy cannot flow from the bus side to the FC, so the corresponding SVC is set to allow output and prohibit input.

[0166] According to the role of MVC and SVCs, in the whole control of the hydrogen storage DC microgrid, the photovoltaic unit and the wind turbine generator output maximum power with EPP-MPPT algorithm and IMPPT algorithm respectively, MVC controls V MVC through I bus , and SVCs control I bus through V SVC , so as to realize the DC bus voltage regulation and power balance control of the microgrid system. The following will derive the calculation formula of V bus and I MVC , I SVC :

[0167] Step 4.1: Derive the current I MVC of MVC:

[0168] Assuming that the response of the converter to external disturbances is fast enough, the transfer function in the converter can be ignored. This assumption gives a simple mathematical evaluation of the transient and static behavior of MVC and SVCs. In this case, the input and output current difference of the DC bus is equal to the sum of the currents of MVC and SVCs, that is:

[0169]

[0170] where I pv is the current of the photovoltaic power generation unit flowing to the DC bus through the DC / DC converter under the EPP-MPPT algorithm, I wt is the current of the wind turbine generator flowing to the DC bus through the AC / DC converter under the IMPPT algorithm, I load is the load current, and for convenience, ΔI is used instead of the sum of the currents of MVC and SVCs.

[0171] Ignoring the transfer function of the converter, the frequency domain DC bus voltage under the control of MVC can be represented as:

[0172]

[0173] The SVC detects the DC bus voltage to determine the charging or discharging current of the energy storage device. Its frequency domain expression is:

[0174]

[0175] Where, the subscript k represents the variable corresponding to the kth SVC. Equation (33) can be eliminated from equation (31) ΔI and I MVC The relationship is:

[0176]

[0177] From formula (34), we can see that I MVC is ΔI after high-pass filtering, and the time constant is Visible I MVC is the high-pass filtered ΔI, so the MVC first reacts to power imbalance and responds to high-frequency power fluctuations in the system.

[0178] Step 4.2: Derive the current of the i-th SVC

[0179] For simplicity, it is assumed that all SVCs The same, that is:

[0180]

[0181] At this time, the specific and is proportional to. Therefore, we can get for:

[0182]

[0183] in:

[0184]

[0185] In formula (36), I MVC It can be eliminated by formula (34):

[0186]

[0187] in, is ΔI after low-pass filtering, and the time constant is The same time constant as that of the MVC. Therefore, SVCs respond to the late or low-frequency portion of the power fluctuation. Assuming a step change in ΔI, which is Because I MVC is ΔI after high-pass filtering. According to the final value theorem, after a sufficient time after ΔI changes, IMVC Becomes zero.

[0188]

[0189] Visible I MVC is the high-pass filtered ΔI, so the MVC first reacts to power imbalance and responds to high-frequency power fluctuations in the system.

[0190] Under this condition, formula (31) can be expressed as:

[0191]

[0192] Formula (35) can be used to simplify formula (40) to obtain the following formula:

[0193]

[0194] From formula (41), we can see that the gain of SVC is The larger the sum, the smaller the target bus voltage change during power flow imbalance. In practice, the system gain is determined by the device response speed and the permissible fluctuation range of the DC bus voltage. The advantage of this distributed control approach is that it uses the tiny voltage changes generated by the MVCs connected to the bus as signals to control the SVC devices. This simple control method eliminates the need for a central processing unit and eliminates the need for communication between converters.

[0195] Simulation results:

[0196] like Figure 5 As shown in the figure, the maximum power point tracking simulation example of photovoltaic power generation unit under EPP-MPPT shows that the EPP-MPPT algorithm is faster and more efficient than the traditional perturbation and observation method in photovoltaic maximum power point tracking. Figure 6 As shown in the figure, the maximum power tracking simulation example of the wind power unit under IMPPT; it can be seen that the rotor speed in TSR control always tracks the reference value ω ref1 , the rotor speed under IMPPT control will track different reference values ​​ω ref1 and ω ref2 At low wind speed, ω ref2 Greater than ω ref1 , when the wind speed is high, ref1 Therefore, the acceleration distance at higher wind speeds is shortened and the corresponding system efficiency is improved. In addition, during the tracking process, C p The coefficient and the corresponding power loss are reduced. Figure 7Figure 2 shows a simulation example of distributed control for a wind, solar, hydrogen, and DC storage microgrid proposed in this embodiment. It can be seen that when the system power suddenly changes, the bus voltage fluctuates. The supercapacitors in the MVC promptly respond to the system's high-frequency fluctuations, and the output current drops to zero. The electrolyzer or hydrogen fuel cell in the SVC responds more slowly, balancing the system's low-frequency power fluctuations. Throughout operation, the bus voltage fluctuations remain within an acceptable range, while effectively controlling the distributed power generation.

[0197] This embodiment proposes a distributed control method for a wind, solar, hydrogen, and DC storage microgrid. The DC bus connects the distributed power generation unit, energy storage device, and load through a converter. The control system adopts a hybrid energy storage form, and the energy storage device includes a supercapacitor, an electrolyzer, and a hydrogen fuel cell. The distributed power supply adopts photovoltaic units and wind turbines, which are connected to the DC bus through an input converter and operate in MPPT mode. For the MPPT control of photovoltaic power generation units, an EPP-MPPT algorithm based on the Newton-Raphson method is designed to improve the traditional perturbation observation method. The algorithm uses an estimation mode once for every two perturbation modes, and can respond quickly to changes in temperature and radiation intensity. At the same time, the Newton-Raphson method is used to determine the variable perturbation step size, replacing the traditional fixed-step perturbation, which can achieve a double improvement in the tracking accuracy and speed of MPPT. In the design of the MPPT controller for wind power generation units, an IMPPT algorithm was designed to improve system efficiency based on the dynamic characteristics of large wind turbines. This algorithm comprehensively considers short-term wind speed prediction, maximum wind energy capture, and the dynamic response of the wind turbine, and intelligently adjusts the optimal reference value of the rotor speed, shortening the rotor's motion during dynamic tracking and reducing mechanical fatigue. Based on the power imbalance between distributed generation, load demand, and storage devices, the SVC equipment is controlled by the small voltage changes generated by the MVC connected to the bus as a signal. The entire control process is divided into active and passive parts, in which the MVC uses I MVC To control the bus voltage V bus For the active part, I MVC is ΔI after high-pass filtering, and the time constant is Therefore, MVCs are connected to supercapacitors with high energy density and fast response speed, which can quickly respond to power imbalance and high-frequency power fluctuations in the system; SVCs can be connected to supercapacitors with high energy density and fast response speed, which can quickly respond to power imbalance and high-frequency power fluctuations in the system; bus To control I SVC For the passive part, is ΔI after low-pass filtering, and the time constant is Therefore, the SVCs are connected to the electrolytic cell or hydrogen fuel cell with large capacity and slow response speed, and respond to the late or low frequency part of the power fluctuation. By combining the active part and the passive part, the current overshoot or undershoot problem of the distributed energy storage device under the control of the traditional bus voltage signal in the system power mutation can be effectively solved. In addition, the advantage of this wind, light, hydrogen, and storage DC microgrid distributed control method is that the small voltage change generated by the MVC connected to the bus is used as a signal to control the SVC device, the control method is simple and efficient, and a central processing unit is not needed, and communication is not needed between the converters.

[0198] Embodiment 2:

[0199] The embodiment provides a wind, light, hydrogen, and storage DC microgrid distributed control system, as shown in the figure, which comprises a connection module, a photovoltaic unit control module, a wind turbine control module, and a distributed power supply control module. Figure 8 The connection module is connected with the photovoltaic unit control module and the wind turbine control module, and the photovoltaic unit control module and the wind turbine control module are connected with the power supply control module.

[0200] The connection module is used for connecting the DC bus to the distributed power generation unit, the energy storage device, and the load through the converter, wherein the distributed power generation unit comprises a photovoltaic power generation unit and a wind power generation unit, and the energy storage device comprises a super capacitor, an electrolytic cell, and a hydrogen fuel cell.

[0201] The photovoltaic unit control module is used for performing maximum power output control on the photovoltaic generator set in the photovoltaic power generation unit by adopting an EPP-MPPT algorithm based on the Newton-Raphson method, wherein the EPP-MPPT algorithm based on the Newton-Raphson method comprises the following steps: determining a variable perturbation step by adopting the Newton-Raphson method, performing model estimation once every two times of perturbation mode, tracking the maximum power point of the photovoltaic power generation unit by adopting the EPP-MPPT algorithm according to the variable perturbation step, and outputting the maximum output power of the photovoltaic power generation unit.

[0202] The wind turbine control module is used for performing maximum power output control on the wind turbine in the wind power generation unit by adopting an IMPPT algorithm, wherein the IMPPT algorithm comprises the following steps: adjusting the optimal reference value of the rotor speed of the wind turbine set by adopting the IMPPT algorithm according to the dynamic characteristics of the wind turbine, and obtaining the maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed.

[0203] A distributed power supply control module is used to use a distributed power supply control strategy to adjust the power imbalance between distributed power generation units, load demands and energy storage devices when the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit are achieved. The distributed power supply control strategy includes: dividing the entire power balance control process into an active part and a passive part, the active part includes: changing the DC bus voltage by detecting the current of the converter connected to the supercapacitor by the DC bus, the corresponding converter is a main voltage controller, and the main voltage control is used to compensate for high-frequency fluctuations; the passive part includes: controlling the input current of the electrolyzer or the output current of the fuel cell by measuring the DC bus voltage, the corresponding converter is a secondary voltage controller, until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced, the control process is stopped, and the secondary voltage controller is used to compensate for low-frequency fluctuations and continuous excess or insufficient power.

[0204] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0205] The scope of protection of this application is not limited to the above-described embodiments. Obviously, those skilled in the art may make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, the disclosure is intended to include such modifications and variations.

Claims

1. A distributed control method for a wind, solar, hydrogen, and DC storage microgrid, characterized in that: include: Connecting the DC bus to a distributed power generation unit, an energy storage device, and a load through a converter, wherein the distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; and the energy storage device includes a supercapacitor, an electrolyzer, and a hydrogen fuel cell; An EPP-MPPT algorithm based on the Newton-Raphson method is used to control the maximum power output of a photovoltaic generator set in a photovoltaic power generation unit. The EPP-MPPT algorithm based on the Newton-Raphson method includes: using the Newton-Raphson method to determine a changing disturbance step size, performing an estimation model once every two disturbance patterns, and using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit according to the changing disturbance step size, and outputting the maximum output power of the photovoltaic power generation unit; An IMPPT algorithm is used to control the maximum power output of a wind turbine in a wind power generation unit. The IMPPT algorithm includes: adjusting an optimal reference value of a rotor speed of the wind turbine using the IMPPT algorithm based on dynamic characteristics of the wind turbine, and obtaining the maximum output power of the wind power generation unit based on the optimal reference value of the rotor speed; Under the condition of the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit, a distributed power supply control strategy is adopted to adjust the power imbalance between the distributed power generation units, the load demand and the energy storage device. The distributed power supply control strategy includes: dividing the entire power balance control process into an active part and a passive part, the active part includes: changing the DC bus voltage by detecting the current of the converter connected to the supercapacitor, the corresponding converter is a main voltage controller, and the main voltage control is used to compensate for high-frequency fluctuations; the passive part includes: controlling the input current of the electrolyzer or the output current of the fuel cell by measuring the DC bus voltage, the corresponding converter is a secondary voltage controller, until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced, the control process is stopped, and the secondary voltage controller is used to compensate for low-frequency fluctuations and continuous excess or insufficient power; The current calculation formula of the main voltage controller is as follows: Among them, I MVC is the input or output current of the converter connected to the supercapacitor, that is, the current of the main voltage controller, is the time constant, ΔI is the input and output current difference of the DC bus, is the voltage reference value of the kth sub-voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the kth voltage controller, s is the complex frequency, is the DC bus center voltage; The current calculation formula of the secondary voltage controller is as follows: in, is the input or output current of the converter of the electrolyzer or hydrogen fuel cell, that is, the current of the i-th voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the ith sub-voltage controller, is the DC bus center voltage, is the bus voltage reference value, is the proportional gain of the kth secondary voltage controller, which is the same as the time constant of the main voltage controller, s is the complex frequency, is the time constant, which is the same as the time constant of the main voltage controller.

2. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 1 is characterized in that: The Newton-Raphson method is used to determine the perturbation step size, and the calculation formula is as follows: Among them, ΔV is the perturbation step size, P pv(n) is the output power of the photovoltaic power generation unit under the nth disturbance, P pv(n-1) is the output power of the photovoltaic power generation unit under the n-1th disturbance, grad n is the ratio of the photovoltaic output power change to the voltage change under the nth disturbance, grad n-1 It is the ratio of the photovoltaic output power change to the voltage change under the n-1th disturbance.

3. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 1 is characterized in that: The maximum power point of the photovoltaic power generation unit is tracked using the EPP-MPPT algorithm according to the changing disturbance step size, including: if the output voltage of the photovoltaic power generation unit increases, resulting in an increase in the photovoltaic output power, then continue to search for the voltage corresponding to the maximum power in the same direction in the current step; otherwise, search for the voltage corresponding to the maximum power in the opposite direction.

4. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 1 is characterized in that: The IMPPT algorithm is used to adjust the optimal reference value of the rotor speed of the wind turbine according to the dynamic characteristics of the wind turbine, including: Get the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2; Taking the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2 as input, the optimal reference value of the rotor speed at each moment is calculated based on the power coefficient of the wind turbine generator in the wind power generation unit and the optimization function of the IMPPT algorithm; The optimal reference value is output at time t0, and is not output at time t1 and time t2.

5. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 4 is characterized in that: The optimization function of the IMPPT algorithm is calculated as follows: Constraints: C p (λ,β)=0.5176(116 / λ-0.4β-5)e -21 / λ +0.0068s 0≤ω(t)≤ω max ,t0≤t≤t2. Among them, ω ref is the rotor speed reference value, v1 is the predicted wind speed at time t1, v2 is the predicted wind speed at time t2, β is the pitch angle of the blade, ρ is the air density, R is the turbine radius, C p is the power coefficient, ω is the rotor speed, v is the actual wind speed input at time t0, ω0 is the rotor speed at time t0, ω max is the maximum rotor speed, J is the turbine inertia moment, λ=ωR / ν is the tip speed ratio, T is one cycle, ω ref1 and ω ref2 are the reference values ​​of rotor speed under different wind speeds, f(ω) represents C p (λ,β).

6. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 1 is characterized in that: The DC bus voltage is changed by detecting the current of the converter connected to the supercapacitor. The frequency domain DC bus voltage under the control of the main voltage controller is calculated as follows: Among them, V bus is the DC bus voltage, is the DC bus center voltage, I MVC Current of the main voltage controller, K IV Proportional gain of the main controller MVC.

7. The distributed control method of wind, solar, hydrogen and DC storage microgrid according to claim 1 is characterized in that: The control process is stopped until the current of the converter connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced. The expression is as follows: Among them, I MVC is the current of the main voltage controller, is the time constant, K IV is the proportional gain of the main controller, is the proportional gain of the kth voltage controller, s is the complex frequency, is the DC bus center voltage, is the voltage reference value of the kth secondary voltage controller.

8. A wind, solar, hydrogen, and DC storage microgrid distributed control system, characterized in that: include: A connection module, a photovoltaic unit control module, a wind turbine control module, and a distributed power supply control module, wherein the connection module is connected to the photovoltaic unit control module and the wind turbine control module respectively, and the photovoltaic unit control module and the wind turbine control module are connected to the power supply control module respectively; A connection module is used to connect the DC bus to the distributed power generation unit, the energy storage device and the load through the converter. The distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; the energy storage device includes: a supercapacitor, an electrolyzer and a hydrogen fuel cell; A photovoltaic unit control module is used to perform maximum power output control on the photovoltaic generator set in the photovoltaic power generation unit using an EPP-MPPT algorithm based on the Newton-Raphson method. The EPP-MPPT algorithm based on the Newton-Raphson method includes: using the Newton-Raphson method to determine a varying disturbance step size, performing an estimation model once every two disturbance patterns, and using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit based on the varying disturbance step size, thereby outputting the maximum output power of the photovoltaic power generation unit; a wind turbine control module, configured to use an IMPPT algorithm to perform maximum power output control on a wind turbine in a wind power generation unit, wherein the IMPPT algorithm comprises: adjusting an optimal reference value of a rotor speed of the wind turbine using the IMPPT algorithm based on dynamic characteristics of the wind turbine, and obtaining a maximum output power of the wind power generation unit based on the optimal reference value of the rotor speed; a distributed power supply control module, configured to, when the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit are achieved, use a distributed power supply control strategy to adjust the power imbalance between the distributed power generation units, the load demand, and the energy storage device; the distributed power supply control strategy comprising: dividing the entire power balance control process into an active part and a passive part, the active part comprising: changing the DC bus voltage by detecting the current of a converter connected to the supercapacitor via the DC bus, the corresponding converter being a primary voltage controller, and the primary voltage control being used to compensate for high-frequency fluctuations; and the passive part comprising: controlling the input current of the electrolyzer or the output current of the fuel cell via measuring the DC bus voltage, the corresponding converter being a secondary voltage controller, until the current of the converter connected to the DC bus of the supercapacitor reaches zero, i.e., when the system power is balanced, the control process is stopped, and the secondary voltage controller is used to compensate for low-frequency fluctuations and continuous excess or insufficient power; The current calculation formula of the main voltage controller is as follows: Among them, I MVC is the input or output current of the converter connected to the supercapacitor, that is, the current of the main voltage controller, is the time constant, ΔI is the input and output current difference of the DC bus, is the voltage reference value of the kth sub-voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the kth voltage controller, s is the complex frequency, is the DC bus center voltage; The current calculation formula of the secondary voltage controller is as follows: in, is the input or output current of the converter of the electrolyzer or hydrogen fuel cell, that is, the current of the i-th voltage controller, K IV is the proportional gain of the main controller, is the proportional gain of the ith sub-voltage controller, is the DC bus center voltage, is the bus voltage reference value, is the proportional gain of the kth secondary voltage controller, which is the same as the time constant of the main voltage controller, s is the complex frequency, is the time constant, which is the same as the time constant of the main voltage controller.

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