A wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights

Abstract

The present invention discloses a wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights, which relates to the field of power systems, including establishing a wind-solar hydrogen production system according to the composition of a wind-solar coupled hydrogen production system, wherein the wind-solar hydrogen production system includes a wind power station, a photovoltaic power station, an energy storage device, and a water electrolysis hydrogen production device, and is characterized in that, in the wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights, a discrete mathematical model is established according to the characteristics of wind power and photovoltaic power generation, and a water electrolysis hydrogen production discrete model is established according to the conversion relationship between electric energy and hydrogen energy. This control method is used to achieve coordinated control of the wind-solar hydrogen production system, and wind power and photovoltaics are used as energy sources for the water electrolysis hydrogen production system to replace the traditional power grid power supply mode. The water electrolysis hydrogen production device converts wind power and photovoltaics into hydrogen energy, and at the same time uses energy storage devices to suppress wind-solar fluctuations to reduce the start-stop frequency of the water electrolysis hydrogen production equipment, thereby improving the consumption of new energy.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a wind-solar-solar hydrogen production control method based on fuzzy weight-based finite set model predictive control. Background Art

[0002] The demand for installed capacity of renewable energy power generation will continue to increase. By the end of 2021, my country's cumulative installed capacity of renewable energy power generation was 1.06 billion kilowatts, accounting for 44.8% of the total installed capacity, of which wind power installed capacity was 328 million kilowatts and solar power installed capacity was 306 million kilowatts. With the continuous development of wind power generation and photovoltaic power generation technology, the proportion of new energy power generation in my country is increasing, and the amount of new energy power generation is also increasing. Hydrogen energy, as a carrier of clean energy, is an important part of achieving "carbon peak and carbon neutrality" and a key link in energy transformation. According to my country's hydrogen energy planning and market demand, the demand for hydrogen energy will show explosive growth in the future. At present, the main source of hydrogen is mainly fossil fuel production, and a large amount of greenhouse gases will be emitted during the production process, which is not conducive to the realization of my country's "carbon peak and carbon neutrality" goals. High-purity hydrogen can be obtained by using water electrolysis to produce hydrogen, and near-zero carbon emissions in the hydrogen production process will help promote the realization of the "dual carbon" goal. Hydrogen production from water electrolysis is the main method of green hydrogen production, which is achieved through power supply from large power grids. However, the electricity bills lead to high costs for hydrogen production, which does not have a price advantage compared to hydrogen production from fossil energy. Therefore, in order to reduce the cost of hydrogen production from water electrolysis and promote the consumption of new energy, the combination of renewable energy power and water electrolysis hydrogen production technology to achieve the conversion of electricity and hydrogen energy is conducive to promoting the development of my country's hydrogen energy industry.

[0003] Wind power generation is affected by natural wind, and its output fluctuates greatly, with large daily and seasonal variation rates, showing strong seasonality and intermittency. The output of photovoltaic power generation systems is directly affected by sunlight. When the intensity of sunlight changes rapidly, the output power will fluctuate rapidly over a large range. At the same time, the output of photovoltaic power generation is random and intermittent due to the influence of weather and day and night. Due to the uncertainty of natural conditions, the output of wind power and photovoltaic power generation systems is uncertain and volatile. In view of the requirements of water electrolysis hydrogen production equipment for electricity, the output fluctuations of wind power generation systems and photovoltaic power generation systems will shorten the service life of electrolysis hydrogen production equipment, and reduce the quality and purity of hydrogen, causing safety hazards. Summary of the invention

[0004] The object of the present invention is to provide a wind-solar hydrogen production control method based on finite set model predictive control of fuzzy weights to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights comprises establishing a wind-solar hydrogen production system according to the composition of the wind-solar coupled hydrogen production system, wherein the wind-solar hydrogen production system comprises a wind power station, a photovoltaic power station, an energy storage device and a water electrolysis hydrogen production device. The method is characterized in that, in the wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights, a discrete mathematical model is established according to the characteristics of wind power and photovoltaic power generation, and a discrete model of water electrolysis hydrogen production is established according to the conversion relationship between electric energy and hydrogen energy.

[0007] A wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights, characterized in that it comprises the following steps:

[0008] S1. Consider the reliability of the wind-solar hydrogen production system, improve the consumption of wind power and photovoltaic power, and build a discrete prediction model for the wind-solar hydrogen production system;

[0009] S2, collect the actual input voltage, current and output voltage parameters of each part of the system at the current moment, and predict the active power under different switch states;

[0010] S3. According to the fluctuation of wind power and photovoltaic output, the weight coefficients of each component in the wind-solar hydrogen production system are obtained by using fuzzy weight control;

[0011] S4. Establish a cost function based on fuzzy weights to evaluate the quality of power prediction values;

[0012] S5. Output the optimal switching state to the corresponding rectifier and converter according to the optimal switching state corresponding to the optimal power prediction value evaluated by the system cost function.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. This control method is used to achieve coordinated control of wind and solar hydrogen production systems. Wind power and photovoltaics are used as energy sources for the water electrolysis hydrogen production system to replace the traditional power grid power supply method. Wind power and photovoltaics are converted into hydrogen energy through water electrolysis hydrogen production equipment. At the same time, energy storage equipment is used to suppress wind and solar fluctuations to reduce the start and stop frequency of water electrolysis hydrogen production equipment and improve the consumption of new energy.

[0015] 2. To ensure the safe and reliable operation of the system, minimize the harmful effects of the uncertainty of wind and solar power output on the system, control the working state of the controller, and combine energy storage equipment to suppress the uncertainty of wind and solar power output, improve the stability of the system, and ensure the stable power supply of the wind and solar hydrogen production system, which is conducive to the stable operation of the water electrolysis hydrogen production equipment, improves the quality and purity of hydrogen, and at the same time improves the ability of the wind and solar coupled water electrolysis hydrogen production system to absorb new energy on site. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1This is a schematic diagram of the wind-solar hydrogen production system of the present invention;

[0017] Figure 2 It is a schematic diagram of the main circuit topology of the rectifier of the present invention;

[0018] Figure 3 It is a schematic diagram of a simplified topology of a main circuit of a rectifier of the present invention;

[0019] Figure 4 It is a Clarke transformation coordinate system relationship diagram of the present invention;

[0020] Figure 5 It is a schematic diagram of the distribution of the voltage vector on the AC side of the rectifier of the present invention in a two-phase stationary αβ coordinate system;

[0021] Figure 6 A schematic diagram of the converter topology structure of the present invention;

[0022] Figure 7 It is a schematic diagram of the topological structure of the battery bidirectional DC / DC converter of the present invention;

[0023] Figure 8 It is a schematic diagram of fuzzy weight control of the present invention;

[0024] Fig. 9 It is the fuzzy regular surface graph of the present invention;

[0025] Fig.10 The invention provides a wind-solar hydrogen production system that uses a finite set model predictive control based on fuzzy weights. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figures 1 to 10 The wind-solar hydrogen production system mainly includes: wind power station, photovoltaic power station, energy storage equipment and water electrolysis hydrogen production equipment, such as Figure 1It is composed of a wind-solar hydrogen production system. In the process of water electrolysis hydrogen production, wind power and photovoltaics are used as the source of electricity supply. Due to the uncertainty of wind power and photovoltaic output, the continuity and stability of electricity supply cannot be guaranteed, resulting in damage to the water electrolysis hydrogen production equipment during the electrolysis process. At the same time, it will reduce the purity of hydrogen and cause safety accidents. Energy storage equipment can suppress the fluctuation of wind and solar output and ensure the stable operation of the system. In the wind-solar hydrogen production control method based on fuzzy weighted finite set model predictive control, a discrete mathematical model is established according to the characteristics of wind power and photovoltaic power generation. Since the water electrolysis hydrogen production equipment converts electrical energy into hydrogen energy, the water electrolysis hydrogen production equipment acts as a load to consume electrical energy. According to the conversion relationship between electrical energy and hydrogen energy, a water electrolysis hydrogen production discrete model is established; the working state of the wind-solar hydrogen production system is predicted by the discrete model of the system, and the control state of the optimal working state of the wind-solar hydrogen production system is determined in combination with the cost function under fuzzy weight control.

[0028] 1. Wind and solar hydrogen production system model

[0029] Considering that the wind-solar hydrogen production control method is a key link to achieve the coordinated work of wind power, photovoltaics, energy storage equipment and water electrolysis hydrogen production equipment, improve the consumption of new energy, and ensure the normal operation of water electrolysis hydrogen production equipment. The wind-solar hydrogen production system consists of wind turbines, photovoltaic power stations, energy storage equipment and water electrolysis hydrogen production equipment. In order to achieve coordinated control of various parts of the wind-solar hydrogen production system, it is necessary to establish corresponding models for the important components of the system.

[0030] (1) Wind turbines:

[0031] The wind power generation system is one of the power generation units of the wind-solar hydrogen production system. The wind turbine converts wind energy into mechanical energy through the wind rotor, and uses the rotation of the wind rotor to drive the generator to generate electricity. The aerodynamic model of the wind turbine is:

[0032]

[0033] in,

[0034]

[0035] Where P M is the mechanical power of the wind turbine; air is the air density; R is the radius of the wind turbine impeller; λ is the tip speed ratio; λ i is the intermediate variable; β is the pitch angle; C p is the wind energy conversion efficiency coefficient of the blade; v w is the wind speed; ω is the wind turbine rotation acceleration.

[0036] (2) Photovoltaic array:

[0037] The photovoltaic array is composed of a series of photovoltaic cells connected in series and parallel. It uses the photovoltaic effect to directly convert solar radiation energy into electrical energy. At the same time, the output of the photovoltaic array is affected by factors such as light intensity and temperature. Under any solar radiation intensity and ambient temperature, the surface temperature of the photovoltaic array cell is:

[0038] T c =T a +t c G (3)

[0039] Where, t c is the cell surface temperature coefficient of the photovoltaic array; G is the solar radiation intensity; T a is the ambient temperature. The output equation of the photovoltaic array is:

[0040]

[0041] P pv =N s I pv ·U pv (5)

[0042] in,

[0043]

[0044] Where U oc with I sc are the open circuit voltage and short circuit current of the battery module, respectively, provided by the solar cell module manufacturer; U m with I m are the voltage and current at the maximum power point of the battery module respectively; α is the current variation coefficient under the reference radiation intensity; β is the voltage temperature variation coefficient under the reference radiation intensity; R s is the series resistance of the photovoltaic module; N p 、N s are the number of PV modules in parallel and series in the PV array; G ref is the reference value of solar radiation intensity; T ref is the reference value of the surface temperature of the photovoltaic module; dT is the difference between the actual surface temperature of the photovoltaic module and its reference temperature; U pv The output voltage of the photovoltaic module.

[0045] (3) Water electrolysis hydrogen production equipment:

[0046] The electrolysis of water to produce hydrogen is a technology that uses electricity to decompose water to produce hydrogen and oxygen. The electrolysis process is divided into two processes: hydrogen evolution reaction and oxygen evolution reaction, which react at the cathode and anode of the electrolysis equipment respectively. At present, the electrolysis equipment mainly includes alkaline electrolyzers, proton exchange membrane electrolyzers, anion exchange membrane and solid oxide electrolyzers. Among them, alkaline electrolyzers are widely used in the process of electrolysis of water to produce hydrogen. When direct current is passed into the alkaline electrolyzer, OH - Oxidation occurs at the anode and loses electrons to produce oxygen (O 2 );H + A reduction reaction occurs at the cathode to generate electrons and generate hydrogen (H 2 ).

[0047] The reaction equation for alkaline water electrolysis is:

[0048] cathode:

[0049] 4OH - →O 2 ↑+2H 2 O+4e -

[0050] anode:

[0051] H 2 O+2e - →H 2 ↑+2OH -

[0052] Overall reaction:

[0053] 2H 2 O→2H 2 ↑+O 2 ↑

[0054] According to the reaction equation of the alkaline electrolytic cell, it reflects that the alkaline electrolytic cell produces hydrogen and oxygen by electrolyzing water, and uses electrolytes to increase the conductivity of the electrolyte. Since the electrolytic cell converts electrical energy into hydrogen energy, the basic voltage equation of the alkaline electrolytic cell is:

[0055]

[0056] Where ΔG is the Gibbs free energy in the electrochemical reaction process; z is the number of electrons transferred in each reaction; F is the Faraday constant; r 1 、r 2 is the ohmic resistance parameter of alkaline electrolyte; T el is the electrolytic cell temperature; A cell Electrolyzer electrode area; I el is the DC current passing through the electrolytic cell; s n is the electrode overvoltage parameter (n=1…6).

[0057] According to the relationship between the voltage and current of the electrolyzer, its power and hydrogen production rate can be obtained as follows:

[0058] P el =N sel N pel I el ·U el (8)

[0059]

[0060] in,

[0061]

[0062] Where η F is the Faraday efficiency; N sel N is the number of electrolytic cells connected in series; pel is the number of electrolytic cells connected in parallel; a n is the Faraday efficiency coefficient (n=1…5).

[0063] (4) Energy storage equipment

[0064] Since wind power and photovoltaic power output are random, energy storage equipment is introduced into the wind-solar hydrogen production system to smooth out the volatility of wind and photovoltaic power output, and avoid frequent start and stop of water electrolysis hydrogen production equipment due to fluctuations in wind power and photovoltaic output, which may damage the water electrolysis hydrogen production equipment. Energy storage equipment is mainly batteries. When the wind and photovoltaic output is greater than the power demand of the water electrolysis hydrogen production equipment, the excess electric energy is stored through the battery; when the wind and photovoltaic output cannot guarantee the normal operation of the water electrolysis hydrogen production equipment, the battery outputs electric energy to maintain the normal operation of the water electrolysis hydrogen production equipment. The mathematical model of the battery is:

[0065]

[0066] in,

[0067]

[0068] Where, E is the electromotive force in the battery; E 0 is the initial internal electromotive force of the battery; k is the polarization voltage constant; A is the battery voltage variation coefficient; B is the battery capacity variation coefficient; Q n is the rated capacity of the battery; i(τ) is the charge and discharge current of the battery; SOC is the percentage of remaining battery power (battery state of charge); C t is the battery polarization effect coefficient; T bat is the battery temperature; N sbat and N pbat are the number of batteries connected in series and in parallel in the battery pack respectively.

[0069] (5) Hydrogen storage equipment

[0070] The water electrolysis hydrogen production equipment converts electrical energy into hydrogen energy. When the large amount of hydrogen energy produced is greater than the hydrogen energy demand, it needs to be stored through hydrogen storage equipment. Therefore, in the wind and solar hydrogen production system, it is necessary to establish a hydrogen storage model to monitor the hydrogen energy status in real time. The hydrogen storage rate is:

[0071]

[0072] According to the hydrogen storage rate, the hydrogen storage capacity can be obtained as:

[0073]

[0074] According to the gas state equation, the relationship between the hydrogen storage capacity and the pressure of the hydrogen storage equipment is:

[0075]

[0076] In the above formula, q sto (τ) is the net hydrogen storage rate of the hydrogen storage equipment; is the hydrogen rate flowing into the hydrogen storage device; is the hydrogen rate flowing out of the hydrogen storage device; Q sto (t 0 ) is t 0 Hydrogen storage capacity of hydrogen storage equipment at any moment; p sto is the hydrogen pressure inside the hydrogen storage device; R is the gas constant; T sto Hydrogen storage temperature; V sto is the hydrogen storage volume.

[0077] 2. Finite set prediction model

[0078] The establishment of a prediction model is a key step to ensure the predictive control of a finite set model, and it is also an important link in controlling the energy flow of a wind-solar hydrogen production system. In a wind-solar hydrogen production system, the water electrolysis hydrogen production equipment uses direct current to decompose water to generate hydrogen, so it is necessary to convert and control the electric energy of the wind-solar hydrogen production system. First, the alternating current generated by the wind turbine needs to be converted into direct current, and secondly, the direct current generated by the photovoltaic power generation system needs to be converted into direct current so that the electric energy generated by wind power and photovoltaic power generation is at the same voltage level. At the same time, the energy storage device is combined with the direct current conversion device to reduce the power fluctuation range, and the water electrolysis hydrogen production equipment is connected to the direct current bus using a converter to realize the power transmission between the power supply and the load.

[0079] (1) Rectifier finite set prediction model

[0080] Wind power generation is the process of converting wind energy into mechanical energy, and then converting mechanical energy into electrical energy through a generator. The electrical energy output by a wind turbine is AC power. To ensure the normal operation of the DC load, the wind power needs to be rectified and controlled. The main circuit topology of the rectifier is as follows: Figure 2In order to better reflect the control method of the rectifier, the rectifier switch tube is simplified, and the rectifier main circuit diagram topology is simplified as follows Figure 3 As shown. Define the switching function S n for:

[0081]

[0082] According to Kirchhoff's law on the AC side, the voltage balance equation can be obtained as:

[0083]

[0084] In the formula, u a 、u b 、u c are the voltages between the AC power input terminal of the rectifier and the midpoint N of the three-phase power supply, respectively:

[0085]

[0086] In the formula, u aO 、u bO 、u cO They represent the voltage between the power input terminal of the rectifier on the AC side and the negative pole O on the DC side, u ON It is the voltage between point O and the midpoint N of the power supply.

[0087] In the actual control process, the dead time of the switch tube needs to be considered to avoid the situation where the same bridge arm is turned on at the same time due to the dead time of the switch tube. However, the influence of the dead time of the switch tube is not considered in the modeling process. Therefore, there is:

[0088]

[0089] Since the rectifier main circuit is a three-phase symmetrical system, we have:

[0090]

[0091] In summary,

[0092] u a +u b +u c =0 (21)

[0093]

[0094]

[0095] The current flowing through the DC load is i L =u dc / R L , and i dc =Sa i a +S b i b +S c i c ,u dc is the rectifier DC side output voltage, R L is the DC side load resistance. According to Kirchhoff's current law, at the positive pole of the filter capacitor:

[0096]

[0097] According to the above content, the rectifier has the following in the three-phase stationary abc coordinate system:

[0098]

[0099] The three-phase static abc coordinate system can clearly reflect the physical meaning of the rectifier, but since the grid-side voltage and current are both time-varying AC quantities, it is difficult to design the control system. Therefore, in order to reduce the difficulty of controller system design and realize independent control of the controlled quantity, the three-phase static abc coordinate system is transformed into a two-phase static αβ coordinate system, namely Clarke transformation, as shown in Figure 4 As shown, the power does not change before and after the conversion to achieve equal power conversion.

[0100] The equal power transformation matrix and inverse transformation matrix are:

[0101]

[0102]

[0103] Therefore, the three-phase rectifier has the following equation in the two-phase stationary αβ coordinate system:

[0104]

[0105] According to the above formula, the three-phase balanced voltage equation after Clarke transformation in the two-phase stationary αβ coordinate system is:

[0106]

[0107] The forward Euler method is used to discretize the differential term di / dt, that is:

[0108]

[0109] In the formula, i(k) and i(k+h) represent the s and (k+h)T s The instantaneous current value at time T srepresents the sampling period of the system; h is the number of prediction cycles, if h = 1 is a single-step prediction, h = 2 is a two-step prediction. According to the above formula, we can get (k+h)T s The instantaneous current at time is:

[0110]

[0111] In the formula, e α (k+h-1), e β (k+h-1), i α (k+h-1), i β (k+h-1) respectively represents (k+h-1)T in the two-phase stationary αβ coordinate system s The grid voltage and instantaneous current value at the moment need to be measured by sensors. α (k+h-1),u β (k+h-1) is the voltage value on the AC side of the rectifier, which is related to the rectifier open-tube state and the DC side voltage value. Assume that the vector expression of the rectifier AC side voltage under different switching states is u n =[u αn u βn ](n=0~7), then u n and switch status S a , S b , S c The relationship between them is shown in the following table.

[0112]

[0113] According to the corresponding relationship between the switch state and the vector expression, the voltage vector distribution in the two-phase stationary αβ coordinate system can be obtained as shown in Figure 5.

[0114] According to the 8 switch states at the current (k+h-1)T s The AC side voltage value corresponding to the time can be predicted (k+h)T s The instantaneous current value at the moment is:

[0115]

[0116] Where n = 0 ~ 7, i αn (k+h), i βn (k+h) is the predicted current value under any switching state. After equal power transformation, the instantaneous active power and instantaneous reactive power in the two-phase stationary αβ coordinate system are:

[0117]

[0118] Where P n (k+h) is (k+h)T under different switch statess The instantaneous active power prediction value at time; Q n (k+h) is (k+h)T under different switch states s The instantaneous reactive power prediction value at time; e(k+h) means (k+h)T s The instantaneous value of the AC side voltage at the time, due to the system control cycle T s is much smaller than the fundamental frequency of the AC side, so at T s The voltage on the AC side remains basically unchanged during the time interval, that is, e(k+h)≈e(k+h-1); Indicates the predicted current value i n The conjugate value of (k+h).

[0119] (2) Converter finite set prediction model

[0120] Since the photovoltaic array converts solar energy into direct current, and the water electrolysis hydrogen production equipment uses direct current to decompose water to generate hydrogen, in order to ensure the normal operation of the system, the system power needs to be converted by the inverter. Therefore, the normal operation of the system is achieved by controlling the inverter. The inverter is an important link in the conversion of DC power supply, so it is necessary to analyze the inverter. The topology of the inverter is as follows: Figure 6 shown.

[0121] According to the two states of the table converter, we can get:

[0122]

[0123] In the formula, i LB is the current flowing through the inductor L B Current value; V PV is the output voltage value of the photovoltaic array; V dc is the output voltage value of the converter; S Boost is the switching state of the converter, 1 represents the on state and 0 represents the off state. The continuous time model is converted into a discrete time model using the forward Euler method, and after simplification, it is:

[0124]

[0125] In the formula, i LB (k++h-1), i LB (k+h) respectively represents (k+h-1)T s and (k+h)T s The instantaneous current value at the moment; V PV (k+h-1) is (k+h-1)T s The output voltage value of the photovoltaic array at the moment; V dc (k+h-1) is (k+h-1)T sOutput voltage value of the converter at the moment; T sB is the system sampling period. sB When the voltage is small enough, the photovoltaic output voltage remains basically constant, that is, V PV (k+h)≈V PV (k+h-1), so:

[0126]

[0127] Where P PV (k+h-1),P PV (k+h) respectively represents (k+h-1)T s and (k+h)T s The instantaneous active power value of the photovoltaic array output at the moment. In order to ensure the constant output voltage during photovoltaic operation, C PV , the variable prediction in the above formula ignores the power required for capacitor charging and discharging, so the predicted value of photovoltaic output power is corrected and discretized using the forward Euler method, and the corrected value is:

[0128]

[0129] In the formula, C PV is the voltage stabilizing capacitor value at the output side of the photovoltaic array; ΔV PV is the voltage change value at adjacent sampling moments. The output of the photovoltaic array is rectified by the inverter and wind power generation and then supplied to the water electrolysis hydrogen production equipment through the DC bus. An inverter is also required between the water electrolysis hydrogen production equipment and the DC bus to ensure the normal operation of the electrolysis equipment. Its principle is similar to that of the photovoltaic array output inverter and will not be elaborated here.

[0130] (3) Finite set prediction model for bidirectional DC / DC converter

[0131] A stable power supply is required in the process of producing hydrogen by electrolysis of water, but there is uncertainty in the output of wind power and photovoltaic power. The output of wind power and photovoltaic power may fluctuate violently, causing the water electrolysis hydrogen production equipment to fail to work properly. In order to ensure the normal operation of the water electrolysis hydrogen production equipment and suppress the fluctuation of wind power and photovoltaic output, it is necessary to add energy storage to the wind and solar hydrogen production system. The energy storage device can store excess electricity when the wind and solar output is greater than the power demand of the water electrolysis hydrogen production equipment, and output electricity when the wind and solar output cannot meet the power demand of the water electrolysis hydrogen production equipment. The energy storage device has the characteristics of bidirectional flow of electricity, so a bidirectional DC / DC converter is required to ensure the stability of the system. Figure 7 It is a battery bidirectional DC / DC converter topology.

[0132] Bidirectional DC / DC converter switch tube S 1 With S 2The switching states are complementary, so the bidirectional DC / DC converter has two working states, according to Figure 7 The open state S can be defined bat for:

[0133]

[0134] The working state of the bidirectional DC / DC converter in different switching states is also different. According to different switching states, the following relationship can be obtained:

[0135]

[0136] Where, L is the energy storage inductor; i bat is the battery current; U bat is the battery voltage; U batdc is the DC bus voltage; R is the equivalent resistance of the battery. The forward Euler method is used to transform the working state of the bidirectional DC / DC converter;

[0137]

[0138] In the formula, i bat (k+h) is (k+h)T sbat The predicted value of the battery current at the moment; U bat (k+h-1) is (k+h-1)T sbat The instantaneous value of battery voltage at the moment; U batdc (k+h-1) is (k+h-1)T sbat The instantaneous value of the DC bus voltage at the moment;

[0139] i bat (k+h-1) is (k+h-1)T sbat The instantaneous value of battery current at time T sbat is the time interval between adjacent samplings. sbat When the battery is small enough, the output voltage of the battery remains basically constant, that is, U bat (k+h)≈U bat (k+h-1), so:

[0140]

[0141] Where P bat (k+h) is (k+h)T sbat The predicted value of the battery output power at the moment; P bat (k+h-1) is (k+h-1)T sbat The instantaneous value of the active power output by the battery at that moment.

[0142] (4) Finite set prediction model for wind and solar hydrogen production system

[0143] The operation of the wind and solar hydrogen production system needs to be achieved under the premise of power balance. At the same time, wind power and photovoltaic output are the prerequisites for ensuring the normal operation of the water electrolysis hydrogen production equipment. In order to achieve stable operation of the system, the power balance equation is:

[0144] P W +P PV +P bat =P e (42)

[0145] Among them, P W is the instantaneous active power of wind power generation; P PV is the instantaneous active power output by the photovoltaic array; P bat is the instantaneous active power output by the battery; P e is the instantaneous active power of the water electrolysis hydrogen production equipment.

[0146] The finite set prediction model of the flow meter can be obtained:

[0147] P n (k+h)+P PV (k+h)+P bat (k+h)=P e (k+h) (43)

[0148] in,

[0149]

[0150] Where P n (k+h) indicates the prediction of (k+h)T based on the AC voltage value in the corresponding nth switch state s The instantaneous active power at the moment; P PV (k+h) represents the photovoltaic array (k+h-1)T sB The instantaneous active power value prediction at time (k+h)T sB The instantaneous active power at the moment; P bat (k+h) represents the battery (k+h-1)T sbat The instantaneous active power value prediction of consumption at time (k+h)T sbat The instantaneous active power at the moment; P e (k+h) represents the hydrogen production equipment based on water electrolysis (k+h-1)T se The instantaneous active power value prediction of consumption at time (k+h)T se The instantaneous active power at the moment. Among them, V(k+h-1) is (k+h-1)T se The instantaneous value of DC bus voltage at time V e (k+h-1) is the converter (k+h-1)Tse The instantaneous value of the output voltage at the moment; ΔV is the voltage change value at adjacent sampling moments; P e (k++h-1) is (k+h-1)T se The instantaneous active power value of the water electrolysis hydrogen production equipment at the moment; S e is the switching state of the converter, 1 represents the on state and 0 represents the off state; T se is the sampling period of the converter; L e is the inductance of the converter; C is the capacitance of the converter input side. In the wind-solar hydrogen production system, the parameter collection interval of each link is the same, that is, T s =T sB =T sbat =T se .

[0151] 3. Fuzzy weight control

[0152] The wind and solar power output in the wind and solar hydrogen production system is random and intermittent, which causes the working status of each component in the system to be in a dynamic change process. Based on the actual situation, fuzzy control is introduced to dynamically adjust the weight coefficients of each part of the system according to the fluctuation of photovoltaic and wind power output to ensure the efficient operation of the wind and solar hydrogen production system. Figure 8 As shown, according to the wind and light fluctuations E w 、E p Through fuzzification, it is transformed into fuzzy linguistic variable E' w , E' p Then, the fuzzy weight control rule is used for fuzzy reasoning to obtain the fuzzy weight fuzzy linguistic variable Q' w , Q' PV , Q b ' at , Q e ', after defuzzification, the weight factor Q is obtained w , Q PV , Q bat , Q e , and finally input it into the finite set model predictive controller.

[0153] The fluctuation of wind and solar power output is as follows:

[0154]

[0155]

[0156] Where E w and E p Respectively represent the fluctuation of wind power and photovoltaic power, P w , P p Indicates the actual output power of wind and solar power, P wref , P prefis the reference value of wind and solar output power. According to the fluctuation of wind power and photovoltaic output, the input E of the fuzzy weight controller w and E p The fuzzy control set is:

[0157] E w ={NL NM NS ZO PS PM PL} (47)

[0158] E p ={NL NM NS ZO PS PM PL} (48)

[0159] Among them, NL is negative and large, NM is negative and medium, NS is negative and small, ZO is zero, PS is positive and small, PM is positive and medium, and PL is positive and large. Due to the different importance of each component of the photovoltaic hydrogen production system, the output Q w , Q PV , Q bat , Q e The fuzzy control sets are:

[0160] Q w ={PS PM PL} (49)

[0161] Q PV ={PS PM PL} (50)

[0162] Q bat ={NL NM NS ZO PS PM PL} (51)

[0163] Q e ={PS PM PL} (52)

[0164] Among them, Q w , Q PV , Q bat , Q e The control coefficients of wind power, photovoltaic, energy storage, and water electrolysis for hydrogen production are shown in Table 1. Fuzzy rules are an important part of fuzzy control and an important way to obtain the fuzzy weight of the system. According to the input and output fuzzy control set, the fuzzy rules specified in combination with the operation of each component of the system are shown in Table 1.

[0165] Table 1 Fuzzy control rules for wind and solar hydrogen production

[0166]

[0167] According to the fuzzy rules, using MF editor and fuzzy rule editor, under the input variable fuzzy rules, the output variable Q is obtained. w , Q PV , Q bat , Q e About Ew and E p The fuzzy regular surface is Fig. 9 shown.

[0168] 4. System cost function

[0169] Through the analysis of the wind-solar hydrogen production system, the wind power generation, photovoltaic power generation and electrolyzer power prediction models in the system are obtained, and the prediction model of the system is obtained based on the power balance. In order to obtain the best control state of the system, it is necessary to evaluate the predicted value, and combine the fuzzy weight control to select the switch state control corresponding to the optimal cost function value to achieve the power balance of the system. The cost function of the wind-solar hydrogen production system is:

[0170]

[0171] Where P * (k+h), P P * V (k+h), P bat * (k+h) and P e * (k+h) are the instantaneous active power reference values ​​of wind power generation, photovoltaic power generation battery and water electrolysis hydrogen production equipment at the corresponding time. The system cost function takes the tracking error within the control cycle into account when evaluating, so that the average value of the control variable is close to the reference value, and a more accurate tracking effect can be obtained.

[0172] 5. Implementation process of finite set model prediction based on fuzzy weights

[0173] Fig.10 The following is the predictive control structure diagram of the finite set model based on fuzzy weights for the wind-solar hydrogen production system. The system collects the actual output current and voltage of the wind-solar power generation unit and calculates the wind-solar output fluctuation E. w 、E p , using fuzzy weight control to obtain the weight coefficient Q w , Q PV , Q bat , Q e , and input the finite set model predictive control. The principle of finite set model predictive control is: after the kth sampling period, the collected wind power system voltage instantaneous value e(k+h-1), current instantaneous value i(k+h-1) and rectifier DC side voltage U dc Perform coordinate transformation and calculate the predicted value i of the k+h current under 8 switching states through the prediction model n (k+h)(n=0~7), calculate the corresponding active power prediction value P n (k+h); according to the instantaneous value of the photovoltaic array output voltage VPV (k+h-1), instantaneous current value i PV (k+h-1) and the DC output voltage V dc Predict the k+hth current prediction value i of the converter under two switching states PV (k+h), calculate the predicted active power value P corresponding to the switch state PV (k+h); using the instantaneous value of the battery voltage U acquired by the acquisition bat (k+h-1), instantaneous current value i bat (k+h-1) and the DC side output voltage U of the converter batdc Predict the k+hth current prediction value i of the converter under two switching states bat (k+h), calculate the predicted active power value P corresponding to the switch state bat (k+h); based on the instantaneous value of the DC bus voltage V acquired e (k+h-1), instantaneous current value i e (k+h-1) and the DC output voltage V edc Predict the k+hth current prediction value i of the converter under two switching states e (k+h), calculate the predicted active power value P corresponding to the switch state e (k+h). Then, the cost of the system under different switching states is evaluated according to the cost function g, and the switching state corresponding to the power prediction value with the minimum cost is selected as the optimal switching state of the system to control the stable operation of the wind-solar hydrogen production system. In order to ensure the constant voltage on the DC side, a PI regulator is introduced through the voltage outer loop to generate an active power reference value based on the reference voltage and the DC side voltage measurement value.

[0174] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0175] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A wind-solar hydrogen production control method based on finite set model predictive control with fuzzy weights, comprising establishing a wind-solar hydrogen production system according to the composition of a wind-solar coupled hydrogen production system, wherein the wind-solar hydrogen production system comprises a wind power station, a photovoltaic power station, an energy storage device and a water electrolysis hydrogen production device, characterized in that: In the wind-solar hydrogen production control method based on fuzzy weighted finite set model predictive control, a discrete mathematical model is established according to the characteristics of wind power and photovoltaic power generation, and a discrete model for hydrogen production by water electrolysis is established according to the conversion relationship between electric energy and hydrogen energy, which specifically includes the following steps: S1. Consider the reliability of the wind-solar hydrogen production system, improve the consumption of wind power and photovoltaic power, and build a discrete prediction model for the wind-solar hydrogen production system; S2, collect the actual input voltage, current and output voltage parameters of each part of the system at the current moment, and predict the active power under different switch states; S3. According to the fluctuation of wind power and photovoltaic output, the weight coefficients of each component in the wind-solar hydrogen production system are obtained by using fuzzy weight control; S4. Establish a cost function based on fuzzy weights to evaluate the quality of power prediction values; S5. Output the optimal switching state to the corresponding rectifier and converter according to the optimal switching state corresponding to the optimal power prediction value evaluated by the system cost function.

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