Distributed control method and system for wind, light, hydrogen and storage DC micro-grid

Abstract

The invention provides a distributed control method and system for a wind, light, hydrogen and storage direct current micro-grid, and belongs to the technical field of micro-grid distributed control, and the method comprises the steps: carrying out the maximum power output control of a photovoltaic generator set in a photovoltaic power generation unit through employing an EPP-MPPT algorithm based on a Newton-Rafson method, an IMPPT algorithm is adopted to carry out maximum power output control on a wind power generator in a wind power generation unit, a distributed power supply control strategy is adopted to adjust power imbalance among a distributed power generation unit, a load demand and energy storage equipment, and the whole power balance control process is divided into an active part and a passive part. According to the invention, the problem of current overshoot or undershoot of the distributed energy storage equipment under the control of a traditional bus voltage signal when the system power is suddenly changed can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid distributed control, and in particular relates to a wind, solar, hydrogen, and DC storage microgrid distributed control method and system. Background Art

[0002] With the development of new energy, distributed power generation represented by photovoltaics and wind turbines has the advantages of low transmission cost and flexible installation location, and has been rapidly developed. Microgrids, as an effective management mode for distributed renewable energy and loads, include DC microgrids and AC microgrids. Compared with AC microgrids, DC microgrids do not have problems such as frequency and phase, and the control complexity is relatively low. In addition, DC microgrids have low losses, high efficiency, flexible operation, and simple control. They have become an important form of distributed energy development. However, due to the randomness and intermittent characteristics of distributed power generation, energy storage equipment is usually required to smooth the power fluctuations of microgrids. In recent years, hydrogen energy has become an energy carrier to promote the large-scale development of renewable energy due to its low-carbon, clean, flexible and efficient advantages. Distributed energy systems based on hydrogen fuel cell technology have become a major research hotspot. In existing distributed control based on DC microgrids, energy storage mostly adopts a hybrid energy storage system composed of supercapacitors (SC) and batteries. Although batteries can meet the needs of balancing system power changes, the cost of batteries is proportional to the capacity. In contrast, hydrogen tanks have a larger storage capacity while having less investment costs and are more flexible and efficient. However, the electricity-to-hydrogen conversion devices of electrolyzers (EC) and fuel cells (FC) respond slowly due to the influence of their internal ion flow. The high energy density and fast response characteristics of SC can be used to compensate for the shortcomings of EC and FC.

[0003] The control of DC microgrid mainly includes the power output control of distributed generation units and the control of energy storage system to maintain the stability of DC bus voltage. The energy storage system needs to deal with two different energy fluctuations: one is to manage the energy flow between the generation unit and the load demand, and the other is to balance the energy fluctuations within the microgrid system. Due to the intermittent and uncertain output power of photovoltaic units and wind turbines, it is complicated to control the hybrid energy storage DC microgrid system composed of SC, EC and FC, and it is necessary to conduct in-depth research on it.

[0004] Some scholars have explored the field of DC microgrid control. For the control of distributed generation units, there are relatively classic perturbation and observation methods (P&O) and optimal blade speed ratio control (TSR) methods, which can effectively perform maximum power point tracking (MPPT) control, but the tracking speed and accuracy need to be further improved. The control of energy storage systems is mainly divided into centralized and distributed. In the centralized control method, the controller needs to collect some necessary information, such as voltage, current, etc., and then perform centralized calculations and control the converter according to the calculation results. This method is suitable for optimizing the entire system, but fast communication is required between the central controller and the devices that implement the control, especially as the number of energy storage devices increases, the communication requirements will be difficult to meet. In distributed control, each device connected to the bus is responsible for stabilizing the bus voltage. Droop control is usually used to achieve local autonomy of each distributed unit. However, droop control has 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 changes frequently, distributed energy storage devices and generators need to respond quickly and frequently 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 view of the deficiencies in the prior art, 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, including:

[0007] The DC bus is connected 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 super capacitor, an electrolyzer and a hydrogen fuel cell;

[0008] The EPP-MPPT algorithm based on the Newton-Raphson method is used to control the maximum power output of the photovoltaic power generation group 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, and in a manner of estimating the model once every two disturbance modes, according to the changing disturbance step size, using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit, and outputting the maximum output power of the photovoltaic power generation unit;

[0009] The IMPPT algorithm is used to control the maximum power output of the wind generator in the wind power generation unit. The IMPPT algorithm includes: according to the dynamic characteristics of the wind generator, the IMPPT algorithm is used to adjust the optimal reference value of the rotor speed of the wind generator, and the maximum output power of the wind power generation unit is obtained according to the optimal reference value of the rotor speed;

[0010] In the case 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 by the DC bus, and the corresponding converter is a main voltage controller. 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, and the corresponding converter is a secondary voltage controller. When the converter current of the DC bus connected to 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 change in the disturbance 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 between the change in photovoltaic output power and the change in voltage 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 in the current step, continue to search for the voltage corresponding to the maximum power along the same direction; otherwise, search for the voltage corresponding to the maximum power along the opposite direction.

[0015] The method of using the IMPPT algorithm to adjust the optimal reference value of the rotor speed of the wind turbine according to the dynamic characteristics of the wind turbine includes:

[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] The actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2 are used as inputs, and the optimal reference value of the rotor speed at each moment is calculated according to 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, and the frequency domain DC bus voltage calculation formula under the control of the main voltage controller is as follows:

[0028]

[0029] Among them, V bus is the DC bus voltage, is the bus voltage reference value, I MVC Main voltage controller current, 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 to which the supercapacitor is connected, i.e. 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 ith 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 converter current of the DC bus connected to 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] In the second aspect, the present application proposes a wind, solar, hydrogen, and DC storage microgrid distributed control system, including:

[0040] A connection module, used to connect 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 super capacitor, an electrolyzer and a hydrogen fuel cell;

[0041] A photovoltaic unit control module is used for performing maximum power output control on a photovoltaic generator set in a photovoltaic power generation unit based on an EPP-MPPT algorithm of the Newton-Raphson method, wherein the EPP-MPPT algorithm based on the Newton-Raphson method comprises: using the Newton-Raphson method to determine a changing disturbance step size, performing an estimation model once every two disturbance modes, 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;

[0042] A wind turbine control module, used for using an IMPPT algorithm to perform maximum power output control on a wind turbine in a wind power generation unit, wherein the IMPPT algorithm comprises: using the IMPPT algorithm to adjust an optimal reference value of a rotor speed of the wind turbine according to a dynamic characteristic of the wind turbine, and obtaining a maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed;

[0043] 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 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. 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 a converter connected to a supercapacitor via the DC bus, and the corresponding converter is a main voltage controller. 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, and the corresponding converter is a secondary voltage controller. When the converter current of the DC bus connected to 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.

[0044] Beneficial effects:

[0045] This application proposes a distributed control method and system for wind, light, hydrogen, and DC storage microgrids. This application aims at the MPPT control of photovoltaic power generation units, and designs an EPP-MPPT algorithm based on the Newton-Raphson method to improve the traditional disturbance observation method. The algorithm uses an estimation mode once every two disturbance modes, and can respond quickly to changes in temperature and irradiation intensity. At the same time, the Newton-Raphson method is used to determine the variable disturbance step size, replacing the traditional fixed-step disturbance, which can achieve double improvements in MPPT tracking accuracy and speed. In the design of the MPPT controller of the wind power generation unit, an IMPPT algorithm is designed to improve the system efficiency in view of the dynamic characteristics of large wind turbines. The algorithm comprehensively considers short-term wind speed prediction, maximum wind energy capture, and the dynamic response of the wind turbine, and adjusts the optimal reference value of the rotor speed in an intelligent way, so that the movement of the rotor is shortened during the dynamic tracking process, reducing mechanical fatigue. According to the power imbalance between distributed generation units, load demand and energy storage devices, the SVC equipment is controlled by the slight voltage change generated by the main voltage controller (MVC) connected to the bus as a signal, which can effectively solve the current overshoot or undershoot problem of distributed energy storage equipment under the traditional bus voltage signal control when the system power suddenly changes. In addition, the advantages of the wind, light, hydrogen, and DC storage microgrid distributed control method and system are that the SVC equipment is controlled by the slight voltage change generated by the MVC connected to the bus as a signal, the control method is simple and efficient, no central processing unit is required, and no communication is required between converters. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic flow chart of a distributed control method for a wind, solar, hydrogen and DC storage microgrid according to an embodiment of the present 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 in 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 tracking simulation example under EPP-MPPT of a photovoltaic power generation unit according to an embodiment of the present application;

[0051] Figure 6 A 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 simulation example of distributed control of a wind, solar, hydrogen, and DC storage microgrid according to an embodiment of the present application;

[0053] Figure 8 A principle block diagram 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 of the present application is further described in detail below in conjunction with the drawings and examples.

[0055] This application proposes a distributed control method and system for wind, light, hydrogen, and DC storage microgrids. First, the MPPT control algorithms of photovoltaic units and wind turbines are improved respectively, and an "Estimation-Perturbation-Perturbation (EPP, Estimation Perturbation Perturbation)" MPPT algorithm based on the Newton-Raphson method and an intelligent MPPT (IMPPT) algorithm are proposed respectively, which improves the traditional P&O and TSR methods. Then, according to the power flow imbalance between distributed power generation units, load demand and energy storage equipment, the converter connected to the supercapacitor is set as the main voltage controller (MVC) to respond to high-frequency fluctuations in the system, and the controller connected to the electrolyzer and fuel cell is set as the secondary voltage controller (SVC) to respond to low-frequency fluctuations in the system. The SVC device is controlled by the small voltage change generated by the MVC connected to the bus as a signal, and the entire control process is divided into an active part and a passive part, which can effectively solve the problem of current overshoot or undershoot of distributed energy storage equipment under the control of traditional bus voltage signals when the system power suddenly changes. The control method is simple and efficient, no central processing unit is required, and no communication is required between converters.

[0056] Embodiment 1:

[0057] This embodiment proposes a distributed control method for a wind, solar, hydrogen, and DC storage microgrid, such as Figure 1 , Figure 2 As 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, respectively, wherein the distributed power generation unit includes a photovoltaic power generation unit and a wind power generation unit; the energy storage device includes: a super capacitor, an electrolyzer and a hydrogen fuel cell;

[0059] In this embodiment, the DC bus is connected to the distributed generation unit, the energy storage device and the 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 generation unit includes a photovoltaic power generation unit and a wind power generation unit, which are connected to the DC bus through an input converter and operate in MPPT mode.

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

[0061] In this embodiment, in the design of the MPPT controller of the photovoltaic power generation unit, an EPP-MPPT algorithm based on the Newton-Raphson method is proposed, which improves the traditional perturbation 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 irradiation intensity, and uses the Newton-Raphson method to determine the variable perturbation step size, replacing the traditional fixed step size perturbation, which can double the tracking accuracy and speed of the MPPT.

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

[0063] In this embodiment, the Newton-Raphson method is used for calculation, which provides a prediction method to calculate the effective step size. The EPP-MPPT algorithm under the variable step size makes it possible to track the maximum power point faster and with higher accuracy. First, the Newton-Raphson method is used to converge the solution, and 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 P stands for the same meaning. 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 get:

[0069]

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

[0071]

[0072] Where ΔV is the perturbation step size, i.e., the step size of the change calculated in step S2.1. From 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 increase in the output voltage of the photovoltaic power generation unit leads to an increase in the photovoltaic output power, then in the current step, the corresponding voltage at the maximum power continues to be searched in the same direction; otherwise, the corresponding voltage at the maximum power is searched 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 As shown, including:

[0080] Step S100: start 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 then go to step S113; if the value is 1, enter the disturbance mode, and then go to step S123; if the value is 2, enter the disturbance mode, and then 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 it true? If so, 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 cycle;

[0088] Step S126: Determine the calculation formula V pv (n-1)>V pv (n-2) Is it true? If so, 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 cycle;

[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 cycle;

[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 cycle;

[0092] It can be seen that in this example, the step size ΔV of the change is calculated according to formula (6). According to the above process, it is determined whether the step size ΔV of the change 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 a wind turbine in a wind power generation unit, wherein the IMPPT algorithm comprises: according to the dynamic characteristics of the wind turbine, using the IMPPT algorithm to adjust an optimal reference value of a rotor speed of the wind turbine, and obtaining a maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed;

[0094] In this embodiment, the IMPPT algorithm is used to control the maximum power output of the wind turbine. The IMPPT algorithm includes: according to the dynamic characteristics of large wind turbines, the IMPPT algorithm adjusts the optimal reference value of the rotor speed in an intelligent manner, 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 As 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, according to 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 of the rotor speed at each moment is calculated;

[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 according to the dynamic characteristics of large wind turbines. The algorithm is inspired by the speed calculation formula v=v0+at in physics. Different from the existing method of simply increasing the acceleration a, IMPPT adjusts the initial speed v0 in an intelligent way, thereby shortening the maximum power tracking process and improving the overall efficiency of the system.

[0100] Among them, the optimization function of the IMPPT algorithm is deduced 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] Where, 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 fast 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 a Gaussian distribution, 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 it is assumed that the amplitude of the disturbance has a bound, 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 angular velocity of the turbine 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 force 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] As the accuracy of short-term prediction of wind power generation has been continuously improved in recent years, the IMPPT algorithm proposed in this application takes full advantage of this. The turbine characteristic curve is expressed 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 from tens of seconds to several minutes, and the predicted wind speed will be continuously updated through historical data.

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

[0142]

[0143] The constraints are:

[0144]

[0145] 0≤ω(t)≤ω max ,t0≤t≤t2. (24)

[0146] Where 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 inertia moment, 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] Thereby, the optimal reference value of the rotor speed is obtained, so that the movement of the rotor is shortened during dynamic tracking, mechanical fatigue is reduced, and intelligent regulation is achieved.

[0151] Step S4: 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 among the distributed power generation units, load demand and energy storage equipment. The distributed power supply control strategy comprises: dividing the entire 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 supercapacitor via the DC bus, and the corresponding converter is a main voltage controller, and the main voltage control is used to compensate for high-frequency fluctuations; the passive part comprises: controlling the input current of the electrolyzer or the output current of the fuel cell via measuring the DC bus voltage, and the corresponding converter is a secondary voltage controller, until the converter current 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, and the distributed power supply control strategy includes: according to the power imbalance between distributed power generation units, load demands and energy storage devices, 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, control the input current of the electrolyzer with large capacity and slow response speed or the output current of the fuel cell, and the corresponding converter is the secondary voltage controller (SVC), until the converter current connected to the DC bus of the supercapacitor is zero, that is, when the system power is balanced, the process stops. 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 part with the passive part, the system power balance control can be achieved while effectively solving the problems of output voltage fluctuation and power overshoot or undershoot of distributed power sources under traditional bus voltage signal control. The secondary voltage controller is controlled by the slight 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 set as the main voltage controller (MVC) to respond to high-frequency fluctuations in the system, and the distributed controller connected to the electrolyzer (EC) or hydrogen fuel cell (FC) is set as the secondary voltage controller (SVC) to respond to low-frequency fluctuations in the system. The secondary voltage controller device is controlled by the slight voltage change generated by the main voltage controller connected to the bus as a signal. According to the power imbalance between the distributed power supply, load demand and energy storage equipment, the DC bus voltage is changed by detecting the converter current connected to the SC by the DC bus, and then the distributed controller connected to the EC or FC detects the change in the DC bus voltage and changes the current between the DC bus and the EC or FC through the converter until the converter current connected to the DC bus by the SC is zero, that is, when the system power is balanced, the process stops. The entire control process is divided into two parts, namely the active part and the passive part. Among them, the active part is to use the fast response speed of the SC to control the DC bus voltage to balance the power flow, while the passive part is to measure the DC bus voltage and control the current of the EC or FC with large capacity and slow response speed. 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 voltage controller and the secondary voltage controller are described in detail below:

[0155] (1) Main voltage controller

[0156] MVC is responsible for the active part of the power balancing process and needs to respond quickly to power changes in the system to control the bus voltage. SC is used as the energy storage device corresponding to MVC because of its high power density and fast charging and discharging characteristics. Therefore, under the proposed DC bus control, an MVC containing SC is connected to the DC bus, and 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 determined by MVC 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 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 The increase (decrease) of After that, the other secondary voltage controllers start to absorb (release) power from the DC bus, which in turn reduces (increases) I MVC This control will lead to 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 expression in the frequency domain 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 function of the converter in the frequency domain, respectively. These values ​​are similar to those in MVC.

[0164] From formula (30), we can know 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 work after the MVC responds to bus voltage changes, so they do not directly control the DC bus voltage. They are passive parts in the entire bus voltage control process. EC and FC with relatively slow response speed are selected as energy storage devices. Their main function is to compensate for low-frequency fluctuations and continuous excess or insufficient power in the system. EC and FC are devices for unidirectional energy transmission, where EC absorbs electrical energy from the bus and FC supplies power to the DC bus. To protect the device from the influence of reverse voltage application, the output of the converter needs to be appropriately 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 functions of MVC and SVCs, in the control of the entire hydrogen storage DC microgrid, the photovoltaic unit and wind turbine generator use the EPP-MPPT algorithm and IMPPT algorithm to achieve maximum power output respectively, and the MVC uses I MVC To control V bus , SVCs pass V bus To control I SVC , thereby achieving DC bus voltage regulation and power balance control of the microgrid system. The following is a detailed derivation of V bus and I MVC ,I SVC Calculation formula:

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

[0168] Assuming that the converter responds fast enough to external disturbances, the transfer function in the converter can be neglected. 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 the MVC and SVCs, that is:

[0169]

[0170] Where: I pv is the current from the photovoltaic power generation unit to the DC bus through the DC / DC converter under the EPP-MPPT algorithm, I wt is the current flowing from the wind turbine to the DC bus through the AC / DC converter under the IMMP algorithm, I load is the load current. For convenience, ΔI is used to replace the sum of the currents of MVC and SVCs.

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

[0172]

[0173] The SVC detects the DC bus voltage to determine the charging or discharging current of the energy storage device. The 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 ith SVC

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

[0180]

[0181] At this time, the specific and 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 the MVC. Therefore, SVCs respond to the late or low-frequency portion of the power fluctuation. Assuming a step change in ΔI, it 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, equation (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 change in the target bus voltage when the power flow is unbalanced. In actual situations, the gain of the system is determined based on the response speed of the device and the allowable fluctuation range of the DC bus voltage. The advantage of this distributed control is that the SVC equipment is controlled by using the small voltage changes generated by the MVC connected to the bus as a signal. The control method is simple, no central processing unit is required, and no communication between converters is required.

[0195] Simulation results:

[0196] like Figure 5 As shown in the figure, the maximum power point tracking simulation example of the photovoltaic power generation unit under EPP-MPPT shows that the EPP-MPPT algorithm is faster and more efficient than the traditional perturbation 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 7As shown in the figure, it is a simulation example of distributed control of a wind, light, hydrogen, and DC storage microgrid proposed in this embodiment. It can be seen that when the system power suddenly changes, the bus voltage changes, and the supercapacitor included in the MVC responds to the high-frequency fluctuations of the system in time, and then the output current becomes 0. The electrolyzer or hydrogen fuel cell included in the SVC responds slowly to balance the low-frequency power fluctuations of the system. The bus voltage fluctuation range is within an acceptable range during the entire operation, and effective control of the distributed power source is achieved at the same time.

[0197] This embodiment proposes a distributed control method for a wind, light, hydrogen, and DC storage microgrid. The DC bus connects the distributed power generation unit, the energy storage device, and the 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 source adopts a photovoltaic unit and a wind turbine, which are connected to the DC bus through an input converter, and both operate in MPPT mode. For the MPPT control of the photovoltaic power generation unit, 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 every two perturbation modes, and can respond quickly to changes in temperature and irradiation intensity. At the same time, the Newton-Raphson method is used to determine the variable perturbation step size, which replaces the traditional fixed-step disturbance, and can achieve double improvements in the tracking accuracy and speed of MPPT. In the design of MPPT controller for wind power generation unit, an IMPPT algorithm is designed to improve system efficiency according to the dynamic characteristics of large wind turbines. The algorithm comprehensively considers short-term wind speed prediction, maximum wind energy capture and dynamic response of wind turbines, and adjusts the optimal reference value of rotor speed in an intelligent way, so that the movement of rotor is shortened during dynamic tracking, reducing mechanical fatigue. According to the power imbalance between distributed generation, load demand and storage device, the SVC device is controlled by the small voltage change generated by the MVC connected to the bus as a signal. The whole control process is divided into active part and passive part, in which MVC controls the SVC device through 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, SVCs are connected to electrolyzers or hydrogen fuel cells with large capacity and slow response speed to respond to the late or low-frequency part of power fluctuations. By combining the active part and the passive part, the current overshoot or undershoot problem of distributed energy storage devices under the control of traditional bus voltage signals when the system power changes suddenly can be effectively solved. In addition, the advantage of this distributed control method for wind, light, hydrogen, and DC storage microgrids is that the SVC equipment is controlled by the small voltage changes generated by the MVC connected to the bus as a signal. The control method is simple and efficient, no central processing unit is required, and no communication between converters is required.

[0198] Embodiment 2:

[0199] This embodiment proposes a wind, solar, hydrogen, and DC storage microgrid distributed control system, such as Figure 8 As shown, it includes: 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 respectively connected to the photovoltaic unit control module and the wind turbine control module, and the photovoltaic unit control module and the wind turbine control module are respectively connected to the power supply control module;

[0200] A connection module, used to connect 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 super capacitor, an electrolyzer and a hydrogen fuel cell;

[0201] A photovoltaic unit control module is used to use an EPP-MPPT algorithm based on the Newton-Raphson method to perform maximum power output control on a photovoltaic power generation unit in a photovoltaic power generation unit, wherein the EPP-MPPT algorithm based on the Newton-Raphson method comprises: using the Newton-Raphson method to determine a changing disturbance step size, performing an estimation model once every two disturbance modes, 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;

[0202] A wind turbine control module, used for controlling the maximum power output of the wind turbine in the wind power generation unit by using an IMPPT algorithm, wherein the IMPPT algorithm comprises: adjusting the optimal reference value of the rotor speed of the wind turbine generator set by using 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 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. 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 a converter connected to a supercapacitor via the DC bus, and the corresponding converter is a main voltage controller. 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, and the corresponding converter is a secondary voltage controller. When the converter current of the DC bus connected to 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 the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0205] The protection scope of the present application is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A wind, solar, hydrogen, and DC storage microgrid distributed control method, characterized in that: include: The DC bus is connected 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 super capacitor, an electrolyzer and a hydrogen fuel cell; The EPP-MPPT algorithm based on the Newton-Raphson method is used to control the maximum power output of the photovoltaic power generation group 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, and in a manner of estimating the model once every two disturbance modes, according to the changing disturbance step size, using the EPP-MPPT algorithm to track the maximum power point of the photovoltaic power generation unit, and outputting the maximum output power of the photovoltaic power generation unit; The IMPPT algorithm is used to control the maximum power output of the wind generator in the wind power generation unit. The IMPPT algorithm includes: according to the dynamic characteristics of the wind generator, the IMPPT algorithm is used to adjust the optimal reference value of the rotor speed of the wind generator, and the maximum output power of the wind power generation unit is obtained according to the optimal reference value of the rotor speed; In the case 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 by the DC bus, and the corresponding converter is a main voltage controller. 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, and the corresponding converter is a secondary voltage controller. When the converter current of the DC bus connected to 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.

2. The wind, light, hydrogen, and DC storage microgrid distributed control method according to claim 1 is characterized in that: The Newton-Raphson method is used to determine the change in the disturbance 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 between the change in photovoltaic output power and the change in voltage under the n-1th disturbance.

3. The wind, solar, hydrogen, and DC storage microgrid distributed control method 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 in the current step, continue to search for the voltage corresponding to the maximum power along the same direction; otherwise, search for the voltage corresponding to the maximum power along the opposite direction.

4. The wind, solar, hydrogen, and DC storage microgrid distributed control method according to claim 1 is characterized in that: The method of using the IMPPT algorithm to adjust the optimal reference value of the rotor speed of the wind turbine according to the dynamic characteristics of the wind turbine includes: Get the actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2; The actual wind speed at time t0, the predicted wind speed at time t1, and the predicted wind speed at time t2 are used as inputs, and the optimal reference value of the rotor speed at each moment is calculated according to 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 wind, solar, hydrogen, and DC storage microgrid distributed control method 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 wind, light, hydrogen, and DC storage microgrid distributed control method 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, and the frequency domain DC bus voltage calculation formula under the control of the main voltage controller is as follows: Among them, V bus is the DC bus voltage, is the bus voltage reference value, I MVC Main voltage controller current, K IV Proportional gain of the main controller MVC.

7. The wind, solar, hydrogen, and DC storage microgrid distributed control method according to claim 6 is characterized in that: 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 to which the supercapacitor is connected, i.e. 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.

8. The wind, solar, hydrogen, and DC storage microgrid distributed control method according to claim 7 is characterized in that: 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 ith 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.

9. The wind, solar, hydrogen, and DC storage microgrid distributed control method according to claim 1 is characterized in that: The control process is stopped until the converter current of the DC bus connected to 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.

10. 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 respectively connected to the photovoltaic unit control module and the wind turbine control module, and the photovoltaic unit control module and the wind turbine control module are respectively connected to the power supply control module; A connection module, used to connect 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 super capacitor, an electrolyzer and a hydrogen fuel cell; A photovoltaic unit control module is used for performing maximum power output control on a photovoltaic generator set in a photovoltaic power generation unit based on an EPP-MPPT algorithm of the Newton-Raphson method, wherein the EPP-MPPT algorithm based on the Newton-Raphson method comprises: using the Newton-Raphson method to determine a changing disturbance step size, performing an estimation model once every two disturbance modes, 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; A wind turbine control module, used for using an IMPPT algorithm to perform maximum power output control on a wind turbine in a wind power generation unit, wherein the IMPPT algorithm comprises: using the IMPPT algorithm to adjust an optimal reference value of a rotor speed of the wind turbine according to a dynamic characteristic of the wind turbine, and obtaining a maximum output power of the wind power generation unit according to the optimal reference value of the rotor speed; 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 under the maximum output power of the photovoltaic power generation unit and the maximum output power of the wind power generation unit. 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, and the corresponding converter is a main voltage controller. 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, and the corresponding converter is a secondary voltage controller. When the converter current of the DC bus connected to 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.

Images