Light-storage-hydrogen direct current micro-grid coordination control method based on maximum efficiency point tracking

Abstract

The invention discloses a light-storage-hydrogen direct current micro-grid coordination control method based on maximum efficiency point tracking, and belongs to the field of photovoltaic hydrogen production system optimization control. The method comprises the following steps: 1, establishing a photovoltaic system power generation model, a storage battery voltage stabilization model and an AWE actual operation model; 2, based on the AWE efficiency characteristic curve, adopting a particle swarm optimization algorithm to track voltage and current values corresponding to the maximum efficiency point of the AWE at different temperatures; 3, designing a photovoltaic system control strategy, a storage battery control strategy and an AWE control strategy based on sliding mode variable structure control; and 4, designing a plurality of coordination control strategy scenes in combination with voltage and current values corresponding to the maximum efficiency point of the AWE at different temperatures, so that the light-storage-hydrogen direct current micro-grid operates under various working conditions. The high electric energy utilization rate of direct coupling and the hydrogen production continuity of indirect coupling are combined, the energy conversion efficiency of the system is improved, and the current ripple is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated control of a photovoltaic hydrogen production system in an off-grid mode in new energy hydrogen production, and more specifically, relates to a coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking. Background Technique

[0002] Renewable energy has become a key direction for China's energy transformation. Hydrogen energy in renewable energy is not only environmentally friendly but also convenient for storage and transportation, and is regarded as a crucial component. Photovoltaic hydrogen production technology, as an emerging technology that uses photovoltaic panels and electrolyzer devices to produce hydrogen, has attracted much attention, not only because it can effectively utilize solar energy resources, but also because its production process is clean and efficient, and the hydrogen produced can be an important substitute for clean energy.

[0004] In a photovoltaic hydrogen production system, the electrolyzers that have been industrialized are alkaline water electrolyzers and proton exchange membrane electrolyzers respectively, and the connection methods between photovoltaic panels and electrolyzers are divided into direct coupling and indirect coupling. Direct coupling is to directly connect photovoltaic panels to electrolyzers, and the structure is relatively simple, while indirect coupling is that photovoltaic panels supply power to electrolyzers through components such as DC / DC converters and storage batteries.

[0005] In the current photovoltaic hydrogen production system, the direct coupling method has good economic performance, and it is necessary to design the series-parallel structure of solar cells and electrolyzer units in photovoltaic modules to make the photovoltaic modules output at the maximum power. However, in actual production, the frequent switching of electrolyzers will affect their service life, so the stability of hydrogen production is very important. Adopting an indirect coupling method to access the energy storage unit can more flexibly achieve the matching of solar cells and hydrogen production loads and improve the hydrogen production efficiency. However, the current indirect coupling hydrogen production system does not consider both the power utilization efficiency and the hydrogen production stability of the photovoltaic hydrogen production system at the same time. Summary of the Invention

[0006] 1. Technical Problems to be Solved by the Invention

[0007] Aiming at the problem of how to improve the power utilization efficiency and hydrogen production stability of a photovoltaic hydrogen production system, the present invention proposes a coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking. This method can realize dynamic optimization and coordinated control of the photovoltaic hydrogen production system, improve the energy conversion efficiency of the system, and the current ripple is improved.

[0008] 2. Technical Solution

[0009] To achieve the above object, the technical solution provided by the present invention is as follows:

[0010] A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking of the present invention includes the following steps:

[0011] Step 1: Establish a power generation model for the photovoltaic system, a voltage stabilization model for the battery, and an actual operation model for AWE;

[0012] Step 2: Based on the efficiency characteristic curve of AWE, use the particle swarm optimization algorithm to track the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures;

[0013] Step 3: According to the power generation model of the photovoltaic system, the voltage stabilization model of the battery, and the actual operation model of AWE, design a control strategy for the photovoltaic system, a control strategy for the battery, and a control strategy for AWE based on sliding mode variable structure control;

[0014] Step 4: Combine the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures, and design multiple coordinated control strategy scenarios to enable the photovoltaic-battery-hydrogen DC microgrid to operate under various working conditions.

[0015] Furthermore, in Step 2, the particle swarm optimization algorithm takes the AWE efficiency as the objective function, the current value of AWE as the position of the particle, and the current value corresponding to the maximum efficiency point as the optimal position g; by dynamically adjusting the inertia factor and iteratively updating the speed and position of the particle current, track the current value I corresponding to the maximum efficiency point ref , and calculate the corresponding voltage value U through the actual operation model of AWE ref .

[0016] Furthermore, the update formulas for the particle current speed and position are:

[0017]

[0018] where v i is the particle speed; x i is the particle current position; w is the inertia factor; c1, c2 are learning factors; rand1, rand2 are random numbers in the range of [0,1], gbest i is the global extreme value, and pbest i is the individual extreme value.

[0019] Furthermore, the calculation formula for the inertia factor w is:

[0020]

[0021] where: w ini is the initial inertia weight; w end is the inertia weight when the iteration value reaches the maximum number of evolutionary generations; G k is the maximum number of iterations; g is the optimal position.

[0022] Furthermore, in step 3, the photovoltaic system control strategy includes the MPPT mode, constant voltage mode, and load power tracking mode; when the light intensity is strong and the SOC of the battery reaches the upper limit, the photovoltaic system operates in the constant voltage mode; when the light intensity is weak and the SOC of the battery reaches the lower limit, the photovoltaic system operates in the load power tracking mode; otherwise, the photovoltaic system operates in the MPPT mode.

[0023] Furthermore, in step 3, the battery control strategy is as follows: when SOC≥90%, the battery stops charging; when SOC≤25%, the battery stops discharging; when 25%<SOC<90%, the battery is in the normal charge and discharge working state; when the light intensity is strong, the power generated by the photovoltaic system supplies power to the AWE, and the excess energy is provided to the battery for energy storage; when the light intensity is weak, the photovoltaic system and the battery jointly supply power to the AWE.

[0024] Furthermore, in step 3, the AWE control strategy includes: when the light intensity is weak and the SOC of the battery≤25%, direct coupling control is adopted to directly connect the photovoltaic system to the AWE through the photovoltaic side converter; in other working conditions, maximum efficiency tracking control based on sliding mode variable structure control is adopted.

[0025] Furthermore, the maximum efficiency tracking control first tracks U through the particle swarm optimization algorithm ref , and then adjusts the switching tube state of the AWE side converter circuit through PI control and sliding mode control; the voltage outer loop of the maximum efficiency tracking control adopts PI control, and the current inner loop adopts proportional integral sliding mode variable structure control.

[0026] Furthermore, in the photovoltaic-battery-hydrogen DC microgrid, the photovoltaic system, battery, and electrolytic cell AWE are connected to the DC bus through DC / DC converters, and the upper-layer coordinated control system selects the control method of the lower-layer DC / DC converter by judging the maximum power output value P of the photovoltaic pv-max , the working power P corresponding to the maximum efficiency point of the working temperature of the AWE load-ref , and the SOC value of the battery.

[0027] Furthermore, step 4 selects the control strategy suitable for the current working condition by judging the maximum power output value P of the photovoltaic pv-max , the working power P at the maximum efficiency point of the AWE load-ref , and the SOC value of the battery, and is divided into five working states, as follows:

[0028] (1) SOC≥90%, P pv-max ≥P load-ref, at this time, the photovoltaic system switches from the MPPT mode to the constant voltage mode to maintain the stability of the bus voltage. At this time, the light intensity is high, the energy stored in the battery has reached the upper limit, the battery is on standby, and the AWE operates in the maximum efficiency tracking control mode;

[0029] (2) SOC≥90%, P pv-max <P load-ref , at this time, the light intensity is low, the photovoltaic system operates in the MPPT mode, and at the same time the battery is in the discharging state to maintain the stability of the bus voltage, and the AWE operates in the maximum efficiency tracking control mode;

[0030] (3) 25%<SOC<90%, at this time, the photovoltaic system operates in the MPPT mode, the battery is in the charge-discharge state to maintain the stability of the bus voltage, and the AWE operates in the maximum efficiency tracking control mode;

[0031] (4) SOC≤25%, P pv-max ≥P load-ref , at this time, the light intensity is high, the photovoltaic system operates in the MPPT mode, the battery is in the charging state, and the AWE operates in the maximum efficiency tracking control mode;

[0032] (5) SOC≤25%, P pv-max <P load-ref , at this time, the light intensity is low, the battery is on standby, and the photovoltaic system operating in the MPPT mode is not sufficient to satisfy the AWE operating at the maximum efficiency point; the photovoltaic system switches from the MPPT mode to the load power tracking mode, and direct coupling is performed through the power balance between the photovoltaic system and the AWE.

[0033] 3. Beneficial effects

[0034] Adopting the technical solution provided by the present invention, compared with the existing well-known technologies, it has the following remarkable effects:

[0035] (1) A method for coordinated control of a photovoltaic-battery-hydrogen DC microgrid based on maximum efficiency point tracking of the present invention first establishes a photovoltaic system power generation model, a battery voltage stabilization model, and an actual operation model of an alkaline water electrolyzer AWE. Through the efficiency characteristics of the alkaline water electrolyzer, a maximum efficiency tracking scheme based on the particle swarm algorithm is established. Then, according to the actual operation model of the alkaline water electrolyzer, a method combining direct and indirect coupling of the hydrogen production system is proposed for dynamic coordinated control, and a maximum efficiency tracking control based on sliding mode variable structure control is designed. Finally, according to the voltage and current values corresponding to the maximum efficiency point of the alkaline water electrolyzer at different temperatures, multiple coordinated control strategy scenarios are designed to make the dynamic coordinated control optimization model of the photovoltaic-battery-hydrogen DC microgrid operate under various working conditions.

[0036] (2) A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking according to the present invention combines the high power utilization efficiency of direct coupling and the hydrogen production stability of indirect coupling, avoiding the problem that the series-parallel structure of photovoltaic modules and hydrogen production devices is frequently adjusted in a direct-coupled photovoltaic hydrogen production system, which affects the service life of the devices, further improving the stability of the photovoltaic hydrogen production system in the off-grid mode and making the hydrogen production of the photovoltaic hydrogen production system more stable. At the same time, by judging the maximum output power of the photovoltaic and the state of charge of the battery, different working conditions are switched, so that the electrolyzer operates at the maximum efficiency point, improving the consumption capacity of the hydrogen production system for new energy, improving the magnitude of the current ripple on the hydrogen production side, and improving the efficiency of the photovoltaic hydrogen production system. Description of the Drawings

[0037] Figure 1 It is a schematic diagram of the overall structure of the photovoltaic-storage-hydrogen system in the present invention;

[0038] Figure 2 It is a diagram showing the relationship between the efficiency and current of an alkaline water electrolyzer (AWE) in the present invention;

[0039] Figure 3 It is a flowchart for optimizing the efficiency of AWE in the present invention;

[0040] Figure 4 It is a coordinated control strategy diagram for photovoltaic hydrogen production in the present invention;

[0041] Figure 5 It is a simulation diagram of Embodiment 1 in the present invention;

[0042] Figure 6 It is a simulation diagram of Embodiment 2 in the present invention;

[0043] Figure 7 It is a simulation diagram of Embodiment 3 in the present invention;

[0044] Figure 8 It is a simulation diagram of Embodiment 4 in the present invention. Detailed Embodiments

[0045] To further understand the content of the present invention, the present invention will be described in detail in combination with the drawings and embodiments.

[0046] Embodiment 1

[0047] A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking according to the present invention has the following specific steps:

[0048] Step 1: Establish a power generation model of the photovoltaic system, a voltage stabilization model of the battery, and an actual operation model of the AWE;

[0049] Step 2: Derive the AWE efficiency characteristic curve through the actual operation model of AWE, design a maximum efficiency tracking scheme based on the particle swarm algorithm, and track the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures;

[0050] Step 3: Design the control strategies for the photovoltaic system, the battery, and the AWE control strategy based on sliding mode variable structure control according to the photovoltaic system power generation model, the battery voltage stabilization model, and the actual operation model of AWE;

[0051] Step 4: Combine the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures in Step 2, design multiple coordinated control strategy scenarios, and enable the underlying control strategies established in Step 3 to operate under various working conditions.

[0052] In this embodiment, the photovoltaic system power generation model, the battery voltage stabilization model, and the actual operation model of AWE established in Step 1 are respectively:

[0053] (1) Photovoltaic system power generation model

[0054] The principle of photovoltaic power generation is to convert light energy into electrical energy using the photovoltaic effect. A single diode in series with parallel resistors is used to simulate the equivalent circuit. The expression of the power generation model of the photovoltaic system is:

[0055]

[0056] Where: I is the output current of the photovoltaic system, A; I pv is the photocurrent, A; I0 is the reverse saturation current, A; V is the output voltage, V; R b is the shunt resistance, Ω; R c is the parasitic resistance, Ω; T is the working temperature, °C; n is the diode factor, 1.95; k is the Boltzmann constant, 1.380×10 -23 J / K; q is the unit charge, 1.608×10 -19 C.

[0057] (2) Battery voltage stabilization model

[0058] Lithium batteries have high energy density, long life, and no memory effect. Compared with other batteries, they are more efficient, safe and reliable, and environmentally friendly. The expression of the battery voltage stabilization model established based on lithium batteries in this embodiment is:

[0059]

[0060] Where: E0 is the internal electric potential of the battery, V; Q is the maximum capacity of the battery; K e is the polarization voltage, V; A b is the exponential region voltage amplitude; B is the reciprocal of the exponential region time constant; I tFor extracting electric energy, A; I * Is a low-frequency current, A; I bat Is the output current, A; R b , R res Is the equivalent resistance, Ω.

[0061] The state of charge (SOC) of the storage battery can feedback the remaining power of the battery, and the SOC value is an important parameter for system operation control. The mathematical expression of the storage battery SOC is:

[0062]

[0063] (3) AWE actual operation model

[0064] AWE converts water into hydrogen and oxygen through a redox reaction, with oxygen generated at the internal anode plate and hydrogen generated at the cathode plate. In practical applications, the hydrogen production load is composed of several electrolytic cell monomers, and AWE is equivalent to a non-linear load. Based on the U-I characteristics of AWE, the expression of the AWE actual operation model established is:

[0065]

[0066] In the formula: U ele Is the terminal voltage of the electrolytic cell monomer, V; U rev Is the reversible voltage, V; T is the working temperature, °C; I is the working current, A; N is the number of electrolytic cells; U0 is the output voltage of the hydrogen production device, V; A ele Is the area of the cathode electrode plate; r1, r2 are the parameters of the ohmic resistance of the electrolytic cell; s1, s2, s3, t1, t2, t3, α are the overvoltage coefficients of the electrode.

[0067] In this embodiment, in step 2, the AWE efficiency characteristic curve is deduced through the AWE actual operation model, and a maximum efficiency tracking scheme based on the particle swarm algorithm is designed to track the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures. The specific process is as follows:

[0068] Step 2-1) Solve the efficiency characteristic of the AWE electrolytic cell

[0069] According to Faraday's electrolysis law, the amount of electric charge flowing into the electrolytic cell per unit time is the same as the amount of electric charge required for the redox reaction. Combining with the hydrogen production rate per unit time, the chemical heat energy Q of hydrogen production per unit time h Is:

[0070]

[0071] In the formula: v h Is the hydrogen production rate; R his the calorific value of the chemical energy of hydrogen; n is the number of moles; K is the coefficient related to electrochemistry; F is the Faraday coefficient; a is the change in the valence of the reactant.

[0072] The input power of the electrolyzer per unit time is:

[0073] Q power = U0I (6)

[0074] Under the action of the polarization voltage, the electrolyzer will generate additional heat energy. Then the heat energy Q generated per unit time heat and the AWE efficiency η are:

[0075]

[0076] In the formula: S is the entropy value; λ is the heat dissipation coefficient; β is the temperature fluctuation coefficient.

[0077] It can be seen from formula (8) that the relationship between the hydrogen production efficiency η of AWE and the current density is very close. When the current increases, the AWE efficiency first increases and then decreases, there is a maximum point, that is, the maximum efficiency point. The hydrogen production efficiency is also affected by temperature. When the temperature rises, the hydrogen production efficiency decreases, and the maximum efficiency point moves to the right.

[0078] Step 2-2) Solve the maximum efficiency point of AWE based on the particle swarm optimization algorithm

[0079] The particle swarm optimization algorithm originated from the study of bird flocks foraging. Its principle is simple, easy to implement, and has a fast convergence speed. Its biggest disadvantage is that it is easy to fall into local optimum, resulting in not getting the global optimum solution. Due to the unique single-peak efficiency characteristic of AWE, there is a unique optimal solution in the same temperature environment. Therefore, in this embodiment, the particle swarm optimization algorithm is selected as the method for solving the maximum efficiency point.

[0080] The algorithm objective function is the efficiency expression of AWE. The position of the particle represents the current value of AWE, and the optimal position g is the current value corresponding to the maximum efficiency point. By comparing the magnitudes of the AWE efficiency η, it is judged whether the position of the particle is the optimal position, that is, the value of the optimal solution.

[0081] In each iteration, the particle updates itself by tracking two "extreme values". The first is the optimal solution of the maximum efficiency point found by the particle itself, which is defined as the individual extreme value pbest. The second is the optimal solution of the maximum efficiency point currently found by the entire population, which is defined as the global extreme value gbest. Each particle has a fitness value determined by the AWE efficiency characteristic curve, and records the currently discovered optimal current pbest and the current particle current position x i .

[0082] The update formulas for the particle current velocity and position are:

[0083]

[0084] Where: v i is the particle velocity; x i is the particle current position; w is the inertia factor; c1, c2 are learning factors; rand1, rand2 are random numbers in the interval [0, 1], and gbest i is the global extreme value, and pbest i is the individual extreme value.

[0085] The inertia factor w represents the influence of the particle's previous generation velocity on the current velocity. In this embodiment, the inertia factor w is set to linearly vary dynamically, and the dynamic inertia factor can obtain a better optimization result than a fixed value. The calculation formula for the inertia factor w is:

[0086]

[0087] Where: w ini is the initial inertia weight; w end is the inertia weight when the iteration value reaches the maximum number of evolutions; G k is the maximum number of iterations; g is the optimal position.

[0088] As Figure 3 shown, in the initial stage, the particle swarm and its related parameters are initialized, and then the iteration index variable i is assigned the value 1. On this basis, the efficiency value η of AWE is calculated. According to the calculated efficiency value, the individual optimal current value pbest and the global optimal current value gbest are updated, and these two values correspond to the best state experienced by the individual particle and the best state found by the entire particle swarm, respectively. Then, it is judged whether the current optimization process reaches the expected convergence condition through the convergence criterion. If the convergence condition is met, the global optimal current value gbest and the corresponding number of iterations are output, and the optimization process ends; if not, according to the rules of the particle swarm optimization algorithm, the current value and velocity of each particle are updated, and then the iteration index variable i is incremented by 1, and it returns to the step of calculating the efficiency value of AWE to continue the next round of iteration until the convergence criterion is met.

[0089] Based on the particle swarm algorithm, the maximum efficiency point of AWE at different temperatures can finally be tracked, and then the working current I ref corresponding to the maximum efficiency point can be obtained. Through the actual operation model of AWE, the working voltage U ref can be obtained.

[0090] In this embodiment, in step 3, according to the photovoltaic system power generation model, the battery voltage stabilization model, and the AWE actual operation model, a photovoltaic system control strategy, a battery control strategy, and an AWE control strategy based on sliding mode variable structure control are designed, where:

[0091] The photovoltaic system control strategy is as follows:

[0092] The main circuit of the photovoltaic system is composed of a boost circuit, and the control strategy is divided into three strategies: maximum power point tracking (MPPT), constant voltage mode, and load power tracking. The photovoltaic system works in MPPT mode with a power output value of P pv-max When the light intensity is strong and the battery SOC reaches the upper limit, the photovoltaic system works in constant voltage mode. When the light intensity is weak and the battery SOC reaches the lower limit, the photovoltaic system works in load power tracking mode. In addition, the photovoltaic system works in MPPT mode.

[0093] The battery control strategy is as follows:

[0094] The battery has a limited capacity problem. In order to avoid affecting the life of the battery equipment, when SOC ≥ 90%, the battery stops charging, when SOC ≤ 25%, the battery stops discharging, and when 25% < SOC < 90%, the battery is in a normal charging and discharging working state. When the light intensity is strong, the power generated by the photovoltaic system supplies power to the AWE, and the excess energy is provided to the battery for energy storage; when the light intensity is weak, the photovoltaic system and the battery jointly supply power to the AWE. In the natural environment, due to the uncertainty of light conditions and temperature, the power generated by the photovoltaic system and the power consumed by the load may fluctuate, and the bus voltage may also fluctuate. The battery-side converter can effectively maintain the stability of the bus voltage through the constant voltage mode.

[0095] The AWE control strategy based on sliding mode variable structure control is as follows:

[0096] The working state of AWE is generally low voltage and high current, and its load characteristic is a nonlinear voltage source. Therefore, the converter between AWE and bus voltage is a Buck converter, and there is no capacitor element in the Buck circuit.

[0097] AWE control strategies are divided into direct coupling and maximum efficiency tracking. When the light intensity is weak and SOC ≤ 25%, the AWE side converter adopts direct coupling control. In addition, the AWE side converter adopts maximum efficiency tracking control.

[0098] The direct coupling means that the switch duty cycle of the Buck circuit is 1, the battery is on standby, and the photovoltaic panel is connected to the AWE through the Boost circuit. The specific control method is introduced in step 4 working condition 5.

[0099] The maximum efficiency tracking control first tracks U through the particle swarm optimization algorithm. ref, and then the state of the switching transistor in the Buck circuit is adjusted through PI control and sliding mode control. Among them, the power of AWE working in the maximum efficiency tracking control mode is P load-ref . In the voltage outer loop of the maximum efficiency tracking control, PI control is adopted, and in the current inner loop, proportional integral sliding mode variable structure control is adopted. Compared with PI control, the sliding mode variable structure control has better anti-interference performance and stronger robustness.

[0100] In this embodiment, the design of the sliding mode controller first selects the sliding mode surface and then designs the sliding mode control law. The specific process is as follows:

[0101] Step 3-1) Modeling of the Buck circuit

[0102] In practical applications, the Buck circuit works in the continuous conduction mode (boundary conduction mode, CCM). First, the state space averaging method is used to model and analyze the Buck circuit. u is the switching control function, and based on Kirchhoff's law, the following can be listed:

[0103]

[0104] Within a switching period T, the weighted average of the state space differential equation of the system is:

[0105]

[0106] Since the duty cycle D = T on / T, Equation (13) can be written as:

[0107]

[0108] Comparing Equation (12) and Equation (14), the switching control function u and the duty cycle D are equivalent relationships, and D is the average value of u within a switching period.

[0109] Step 3-2) Design of the sliding mode surface

[0110] The control objective of the system is to stably track its output current. Take the load current tracking error as the state variable e = x1 = I ref -I load , and define the linear sliding mode surface function:

[0111] s = e + a∫edt (15)

[0112] When the system moving point is on the sliding mode surface, that is, let s = 0, and solve the equation to get:

[0113] e = e(0)exp(-at) (16)

[0114] Where: a is the sliding mode surface parameter, 20000; e(0) is the error variable at the initial moment. It can be seen from Equation (16) that when the system moves to the sliding mode surface and t→∞, the tracking error will converge to 0 in an exponential approaching manner. Similarly, the change rate of the tracking error will also converge to 0 in an exponential approaching manner. Let be the change rate of the tracking error, then is:

[0115]

[0116] Where: x(t) is the system perturbation, including input voltage fluctuation, parasitic parameter variation, etc. x(t) is a finite value, and M is a bounded value greater than zero.

[0117] |x(t)| < M (18)

[0118] Step 3-3) Design the control law

[0119] If the uncertain factors of the system are not considered and let the equivalent sliding mode control law can be obtained:

[0120]

[0121] Since the equivalent sliding mode control law cannot ensure that the system state is always on the sliding mode surface. To ensure the reliability of the sliding mode system, a switching control law needs to be designed.

[0122]

[0123] Where: η0 is the control law gain parameter, 36000; sign(s) is the sign function. Among them, η0 > M. Finally, the sliding mode control law is determined as:

[0124]

[0125] Step 3-4) Stability analysis

[0126] According to the Lyapunov stability theory, the function:

[0127]

[0128] Substitute Equation (15) into its first derivative to get:

[0129]

[0130] Since η0 > M, so always holds; when and only when s = 0, Therefore, the selected sliding mode control law meets the conditions.

[0131] In this embodiment, in step 4, by combining the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures in step 2, multiple coordinated control strategy scenarios are designed, and the process of the underlying control strategy established in step 3 operating under multiple working conditions is as follows:

[0132] The photovoltaic hydrogen production system schedules the flow of energy through coordinated control, and selects a control strategy suitable for the current working condition by judging the maximum power output value P of the photovoltaic system pv-max , the working power P at the maximum efficiency point of AWE load-ref and the SOC value of the battery. By combining the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures in step 2, multiple coordinated control strategy scenarios are designed. The system is divided into five working states as follows:

[0133] (1) SOC≥90%, P pv-max ≥P load-ref . At this time, the photovoltaic system switches from MPPT control to constant voltage mode to maintain the stability of the bus voltage. At this time, the light intensity is high, the energy stored in the battery has reached the upper limit, the battery is on standby, and AWE works in the maximum efficiency tracking control mode.

[0134] (2) SOC≥90%, P pv-max <P load-ref . At this time, the light intensity is low, the photovoltaic system selects MPPT control, and at the same time the battery is in the discharge state to maintain the stability of the bus voltage. AWE works in the maximum efficiency tracking control mode.

[0135] (3) 25%<SOC<90%. At this time, the photovoltaic system selects MPPT control, the battery is in the charge and discharge state to maintain the stability of the bus voltage, and AWE works in the maximum efficiency tracking control mode.

[0136] (4) SOC≤25%, P pv-max ≥P load-ref . At this time, the light intensity is high, the photovoltaic system works in the MPPT mode, the battery is in the charging state, and AWE works in the maximum efficiency tracking control mode.

[0137] (5) SOC≤25%, P pv-max <P load-ref . At this time, the light intensity is low, the battery is on standby, and the photovoltaic system working in the MPPT mode is not sufficient to make AWE work at the maximum efficiency point. The photovoltaic system switches from the MPPT mode to the load power tracking mode, and is directly coupled through the power balance between the photovoltaic system and AWE. At this time, the duty ratio of the switch tube in the Buck circuit in the front stage of AWE is 1. The selected photovoltaic cell voltage level of this system is lower than the electrolytic cell U ref , and the photovoltaic system directly performs power matching with AWE through the Boost circuit.

[0138] The present invention first establishes a power generation model of a photovoltaic system, a voltage stabilization model of a storage battery, and an actual operation model of an alkaline water electrolyzer. Through the efficiency characteristics of the alkaline water electrolyzer, a maximum efficiency tracking scheme based on a particle swarm algorithm is established. Then, according to the mathematical model of the alkaline water electrolyzer, a dynamic coordination control method combining direct and indirect coupling of the hydrogen production system is proposed, and a maximum efficiency tracking control based on sliding mode variable structure control is designed. Finally, corresponding voltage and current values at the maximum efficiency point of the alkaline water electrolyzer at different temperatures are used to design multiple scenarios of coordination control strategies, enabling the dynamic coordination control optimization model of the photovoltaic-storage-hydrogen DC microgrid to operate under various working conditions. The present invention improves the stability of the photovoltaic hydrogen production system, makes the hydrogen production of the photovoltaic hydrogen production system more stable, simultaneously improves the accommodation capacity of the hydrogen production system for new energy, improves the current ripple size on the hydrogen production side, and improves the efficiency of the photovoltaic hydrogen production system.

[0139] To verify the feasibility of the coordination control strategy of the photovoltaic-storage-hydrogen DC microgrid of the present invention, a simulation model is built on the Matlab / Simulink platform. AWE is replaced by a nonlinear voltage source, and the model parameters of the alkaline electrolyzer are shown in Table 1, and the circuit parameters and control parameters of the system are shown in Table 2. Among them, the maximum output power of the photovoltaic system is 25.3 kW, the rated power of the alkaline electrolyzer is 19 kW, and the capacity of the storage battery is 40 Ah. As Figure 1 shown, the photovoltaic system, the storage battery, and the electrolyzer are connected to the DC bus through DC / DC converters. The upper-layer coordination control system selects the control modes of the lower-layer DC / DC converters as MPPT control, constant voltage mode, and load power tracking by judging the maximum power output value P pv-max of the photovoltaic, the working power P load-ref at the maximum efficiency point of AWE, and the SOC value of the storage battery, and constitutes a coordination control system for the photovoltaic-storage-hydrogen DC microgrid through a coordination control method.

[0140] Table 1 Model parameters of alkaline electrolyzer

[0141]

[0142] Table 2 Parameters of the simulation platform

[0143]

[0144] The relationship diagram between AWE efficiency and current is as Figure 2 shown. The hydrogen production efficiency of AWE is closely related to the current density. When the current increases, the AWE efficiency first increases and then decreases, with a maximum value point, that is, the maximum efficiency point. The hydrogen production efficiency is also affected by temperature. When the temperature rises, the hydrogen production efficiency decreases, and the maximum efficiency point moves to the right. In the control strategies of the photovoltaic system and AWE of the present invention, the working current corresponding to the maximum efficiency point of AWE is selected as I ref, the working voltage U can be obtained through calculation ref . As Figure 3 shown, the flowchart of AWE efficiency optimization. In this algorithm, the objective function is the efficiency expression of AWE, the position of the particle represents the current value of AWE, and the optimal position g is the current value corresponding to the maximum efficiency point. By comparing the magnitude of the efficiency η of AWE to determine whether the position of the particle is the optimal position, that is, the value of the optimal solution. As Figure 4 shown, the coordinated control strategy diagram of photovoltaic hydrogen production. This system selects the control strategy suitable for the current working condition by judging the maximum power output value P of the photovoltaic pv-max , the working power P at the maximum efficiency point of AWE load-ref and the SOC value of the battery. Due to the long charge and discharge time of the battery, 4 simulation experiment examples are designed. Examples 1-3 will show the control effects when switching different working conditions, and Example 4 will show the influence of proportional-integral sliding mode variable structure control on the current ripple compared with PI control. Among them, the system simulation time is 3s, the working temperature of the photovoltaic system is 25°C, and the working temperature of AWE is 75°C.

[0145] Example 1: The initial SOC of the battery is 25.04%, and the light intensity is 500 W / m 2 . From Figure 5 the simulation results, it can be seen that when the energy provided by the photovoltaic system is not enough to supply power to the battery and AWE at the same time, the photovoltaic system operates in the MPPT mode. When the battery SOC > 25%, the battery maintains the bus voltage stable, and AWE operates at the maximum efficiency point; when the battery power SOC ≤ 25%, the battery will not provide energy to AWE to protect the battery. The AWE system changes from operating at the maximum efficiency point to being directly coupled with the photovoltaic system. At this time, P pv = P load , and the photovoltaic cell boosts the voltage through the Boost circuit to provide energy for AWE.

[0146] Example 2: The initial SOC of the battery is 50%, and the light intensity is 500 W / m 2 , and at 1.5 s, the light intensity becomes 1000 W / m 2 . From Figure 6 the simulation results, it can be seen that in the normal operation mode of the battery, the photovoltaic system operates in the MPPT mode. The battery provides energy under the condition of weak light intensity and absorbs energy under the condition of strong light intensity, and AWE operates at the maximum efficiency point.

[0147] Example 3: The initial SOC of the battery is 89.97%, and the light intensity is 1000 W / m 2 . From Figure 7From the simulation results, when the light intensity is strong, the energy provided by the photovoltaic system can supply power to the battery and AWE simultaneously. When the battery SOC≥90%, the working mode of the photovoltaic system switches from MPPT to constant voltage mode. The photovoltaic system maintains the bus voltage stability through the converter, the battery stops working, and AWE works at the maximum efficiency point. At this time, P pv =P load . The busbar steps down the voltage through the Buck circuit to provide energy for AWE.

[0148] Example 4: For the maximum efficiency tracking in the AWE control strategy, compare the influence of using PI control and proportional integral sliding mode control in the current inner loop on the current ripple. As Figure 8 From the simulation results, the ripple magnitude = (maximum value - minimum value) / average value. The ripple of the current inner loop using sliding mode control is 0.56%, and the ripple using PI control is 0.586%. By comparison, it can be seen that the control strategy proposed in the present invention can reduce the current ripple to a certain extent, thereby improving the hydrogen production efficiency.

[0149] The embodiment of the present invention designs a method for tracking the maximum efficiency point of the electrolyzer based on the particle swarm optimization algorithm and sliding mode variable structure control. Combining the advantages of direct coupling and indirect coupling, a dynamic coordinated control strategy for photovoltaic hydrogen production with the maximum efficiency point as the target is proposed, enabling the control system to flexibly switch among 5 working conditions.

[0150] The above schematically describes the present invention and its implementation manners. This description is not restrictive, and only one of the implementation manners of the present invention is shown in the drawings. The actual structure is not limited thereto. Therefore, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments to this technical solution without creative efforts without departing from the purpose of the present invention creation, they shall fall within the protection scope of the present invention.

Claims

1. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, characterized in that, It includes the following steps: Step 1, establish a photovoltaic system power generation model, a battery voltage stabilization model, and an AWE actual operation model; Step 2, based on the AWE efficiency characteristic curve, use the particle swarm optimization algorithm to track the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures; Step 3, according to the photovoltaic system power generation model, the battery voltage stabilization model, and the AWE actual operation model, design a photovoltaic system control strategy, a battery control strategy, and an AWE control strategy based on sliding mode variable structure control; Step 4, combine the voltage and current values corresponding to the maximum efficiency point of AWE at different temperatures, design multiple coordinated control strategy scenarios, and enable the photovoltaic-battery-hydrogen DC microgrid to operate under various working conditions.

2. The coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking according to claim 1, characterized in that: In Step 2, the particle swarm optimization algorithm takes the AWE efficiency as the objective function, the current value of AWE as the position of the particle, and the current value corresponding to the maximum efficiency point as the optimal position g; By dynamically adjusting the inertia factor and iteratively updating the velocity and position of the particle current, the current value I corresponding to the maximum efficiency point is tracked ref , and the corresponding voltage value U is calculated through the actual operation model of AWE ref .

3. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, as claimed in claim 2, wherein: The update formulas for the particle current velocity and position are: where v i is the particle velocity; x i is the particle current position; w is the inertia factor; c1, c2 are the learning factors; rand1, rand2 are random numbers in the range of [0, 1], gbest i is the global extreme value, pbest i is the individual extreme value.

4. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, as claimed in claim 3, characterized in that: The calculation formula for the inertia factor w is: where: w ini is the initial inertia weight; w end is the inertia weight when the iteration value reaches the maximum number of evolutionary generations; G k is the maximum number of iterations; g is the optimal position.

5. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, characterized in that: In Step 3, the photovoltaic system control strategy includes the MPPT mode, the constant voltage mode, and the load power tracking mode; when the light intensity is strong and the battery SOC reaches the upper limit, the photovoltaic system operates in the constant voltage mode; when the light intensity is weak and the battery SOC reaches the lower limit, the photovoltaic system operates in the load power tracking mode; otherwise, the photovoltaic system operates in the MPPT mode.

6. The coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking according to claim 5, characterized in that: In Step 3, the battery control strategy is as follows: when SOC≥90%, the battery stops charging; when SOC≤25%, the battery stops discharging; when 25%<SOC<90%, the battery is in a normal charge and discharge working state; when the light intensity is strong, the power generated by the photovoltaic system supplies power to AWE, and the excess energy is provided to the battery for energy storage; when the light intensity is weak, the photovoltaic system and the battery jointly supply power to AWE.

7. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, characterized in that: In Step 3, the AWE control strategy includes: when the light intensity is weak and the battery SOC≤25%, direct coupling control is adopted to directly connect the photovoltaic system to AWE through the photovoltaic side converter; under other working conditions, maximum efficiency tracking control based on sliding mode variable structure control is adopted.

8. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, characterized in that: The maximum efficiency tracking control first tracks U through the particle swarm optimization algorithm ref , and then adjusts the switching tube state of the AWE-side converter circuit through PI control and sliding mode control; the voltage outer loop of the maximum efficiency tracking control uses PI control, and the current inner loop uses proportional integral sliding mode variable structure control.

9. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, according to any one of claims 1-8, characterized in that: In the photovoltaic-storage-hydrogen DC microgrid, the photovoltaic system, the storage battery, and the electrolyzer AWE are connected to the DC bus through DC / DC converters. The upper-layer coordinated control system selects the control mode of the lower-layer DC / DC converter by judging the maximum power output value P of the photovoltaic pv-max , the operating power P at the maximum efficiency point corresponding to the operating temperature of AWE load-ref and the SOC value of the storage battery.

10. A coordinated control method for a photovoltaic-storage-hydrogen DC microgrid based on maximum power point tracking, characterized in that: The said step 4 selects a control strategy suitable for the current working condition by judging the photovoltaic maximum power output value P pv-max , the working power P load-ref at the maximum efficiency point of AWE, and the SOC value of the storage battery, which is divided into five working states as follows: (1)SOC ≥ 90%, P pv-max ≥ P load-ref At this time, the photovoltaic system switches from the MPPT mode to the constant voltage mode to maintain the stability of the bus voltage. At this time, the light intensity is high, the energy stored in the battery has reached the upper limit, the battery is on standby, and the AWE works in the maximum efficiency tracking control mode; (2)SOC ≥ 90%, P pv-max <P load-ref , at this time, the light intensity is low, the photovoltaic system operates in the MPPT mode, and at the same time the battery is in the discharge state to maintain the stability of the bus voltage, and the AWE operates in the maximum efficiency tracking control mode; (3) 25%<SOC<90%, at this time the photovoltaic system operates in the MPPT mode, the battery is in a charge and discharge state, maintaining the stability of the bus voltage, and AWE operates in the maximum efficiency tracking control mode; (4) SOC ≤ 25%, P pv-max ≥ P load-ref At this time, the light intensity is high, the photovoltaic system operates in the MPPT mode, the battery is in the charging state, and the AWE operates in the maximum efficiency tracking control mode; (5) SOC ≤ 25%, P pv-max < P load-ref At this time, the light intensity is low, the battery is on standby, and it is insufficient for the photovoltaic system to operate in the MPPT mode to meet the AWE operating at the maximum efficiency point; the photovoltaic system switches from the MPPT mode to the load power tracking mode and is directly coupled through the power balance between the photovoltaic system and the AWE.

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