Multi-electrolytic cell power distribution method and device for off-grid wind-solar hydrogen production system

Abstract

The invention discloses a multi-electrolytic cell power distribution method and device for an off-grid type wind-solar hydrogen production system. The method comprises the steps that a model of the off-grid type wind-solar hydrogen production system is established; establishing a system operation constraint condition; determining an optimization objective function; and an optimization algorithm is applied, and according to the model of the off-grid type wind and light hydrogen production system, the system operation constraint condition and the optimization target, an optimal power distribution scheme of each electrolytic cell is solved. By means of the scheme, the potential safety hazard of explosion possibly caused by too high hydrogen concentration in oxygen can be solved, it is ensured that the system operates within the safety range by controlling the operation power and duration, and the accident risk is reduced. Meanwhile, the power distribution strategy not only considers technical feasibility, but also pays more attention to economy, and is beneficial to improving the profitability of the whole system. In addition, on the premise of meeting various constraints, unstable wind energy and solar energy resources can be more effectively utilized for hydrogen production, and the absorption proportion of renewable energy sources is increased.

Description

Technical Field

[0001] The present application generally relates to the field of energy system operation and scheduling technology. More specifically, the present application relates to a method and apparatus for distributing power among multiple electrolyzers in an off-grid wind-solar hydrogen production system. Background Art

[0002] Alkaline water electrolysis is an important way to produce green hydrogen using renewable energy sources (such as wind and solar energy). However, in alkaline water electrolysis systems, the hydrogen concentration on the oxygen side (referred to as "hydrogen-in-oxygen concentration") is a key indicator for evaluating system safety. If the hydrogen-in-oxygen concentration is too high, the risk of explosion of the hydrogen-oxygen mixture will increase significantly, potentially leading to serious safety accidents. This has become a major factor restricting the large-scale promotion and application of renewable energy hydrogen production.

[0003] The presence of hydrogen in oxygen is primarily related to factors such as the electrolyzer's diaphragm permeability, electrode reaction conditions, and system sealing. In particular, the electrolyzer's operating power has a direct impact on the concentration of hydrogen in oxygen: when operating at lower power, the relative proportion of hydrogen permeating into the oxygen side may increase, causing the hydrogen concentration in oxygen to reach the safety monitoring limit more quickly, shortening the safe operating period. However, operating at higher power can maintain a safe and stable state for a longer period of time. Therefore, to ensure safety, the industry generally sets the minimum operating power of alkaline electrolyzers at approximately 25% of their rated power.

[0004] Although it is possible to seek control methods by studying the formation mechanism of hydrogen in oxygen, it is usually difficult to achieve significant results in a short period of time. In contrast, maintaining the concentration of hydrogen in oxygen within a safe range by optimizing and controlling the operating state or operating power of the electrolyzer is a more direct and effective means. However, the existing alkaline water electrolysis hydrogen production system has limited ability to dynamically regulate the concentration of hydrogen in oxygen, especially in the face of the volatility of renewable energy such as wind and solar power. It is difficult to meet the needs of long-term safe and stable operation of large-scale hydrogen production systems, and may lead to low renewable energy absorption rate or frequent start and stop of electrolyzers.

[0005] In view of this, there is an urgent need to provide a multi-electrolyzer power distribution solution for an off-grid wind-solar hydrogen production system, so as to increase the proportion of renewable energy consumption, reduce the number of electrolyzer starts and stops, and improve equipment utilization while ensuring system safety. Summary of the Invention

[0006] In order to at least solve one or more of the technical problems mentioned above, the present application proposes a multi-electrolyzer power distribution scheme for an off-grid wind-solar hydrogen production system in multiple aspects.

[0007] In a first aspect, the present application provides a method for allocating power to multiple electrolyzers in an off-grid wind-solar hydrogen production system, comprising: establishing a model of the off-grid wind-solar hydrogen production system, wherein the model of the off-grid wind-solar hydrogen production system includes at least a wind turbine model, a photovoltaic power generation device model, and an alkaline electrolyzer model; establishing system operation constraints based on the model of the off-grid wind-solar hydrogen production system, wherein the system operation constraints include a first constraint and a second constraint, the first constraint including at least: a system power balance constraint, an electrolyzer operating power range constraint, an electrolyzer start-stop time constraint, and an electrolyzer overload continuous operation time constraint, and the second constraint including a safe operation time constraint of hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer; determining an optimization objective function based on the model of the off-grid wind-solar hydrogen production system and the system operation constraints, wherein the optimization objective is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale; applying an optimization algorithm to solve the optimal power allocation scheme for each electrolyzer according to the model of the off-grid wind-solar hydrogen production system, the system operation constraints, and the optimization objective.

[0008] In some embodiments, the expression of the first constraint is: Among them, P WT (t) is the power generated by the wind turbine at time t, P PV (t) is the power generation power of the photovoltaic device at time t, P el,i (t) is the operating power of the i-th electrolytic cell at time t, n is the total number of electrolytic cells, P waste (t) is the unabsorbed power at time t, P el,i,min is the minimum operating power of the i-th electrolytic cell, P el,i,max is the maximum operating power of the i-th electrolytic cell, T on / off is the start and stop time of the electrolytic cell, T work,i is the electrolytic cell operating time, T ele,i is the overload operation time of the i-th electrolytic cell, T max P is the continuous operation time of the electrolytic cell at maximum overload power, e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, P el,max is the maximum operating power of the electrolyzer.

[0009] In some embodiments, the safe operating time constraint of hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer is expressed as follows: Among them, T el,i is the operating time of the i-th electrolytic cell, P e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, T 25％-50％ For electrolyzers operating at 25% P e-50% P e Safe and stable operation time, T 50％-75％ For electrolyzers operating at 50% P e -75% P e Safe and stable operation time, T h is the safe operation time after the electrolyzer increases power, t-Δt is the previous time period before the current time t, P el,i,t-Δt is the operating power of the i-th electrolytic cell in the previous time period before the current time t, P el,i,t is the operating power of the i-th electrolyzer in the current time period t.

[0010] In some embodiments, the expression of the optimization objective function is: Among them, T is the total time range, C is the profit of hydrogen production within the time range T, P ele,t is the power consumed by the electrolyzer for hydrogen production during time period t, C H2 is the unit hydrogen selling price, q H2 is the power consumed to produce unit hydrogen, C on is the electrolytic cell startup cost coefficient, C off is the electrolytic cell shutdown cost coefficient, Y el,x,t is the electrolytic cell startup state variable, indicating whether electrolytic cell x is started in the current time period t, Z el,y,t is the electrolytic cell stop state variable, indicating whether electrolytic cell y is stopped in the current time period t, p is the number of electrolytic cells started in the current time period, q is the number of electrolytic cells stopped in the current time period, C ele is the electrolytic cell operation and maintenance cost coefficient, P el,k,t is the operating power of electrolytic cell k in time period t, and n is the total number of electrolytic cells.

[0011] In some embodiments, in the process of solving the optimal power allocation scheme for each electrolyzer according to the model, system operating constraints and optimization objectives of the off-grid wind-solar hydrogen production system, the improved Beluga optimization algorithm is applied to solve the optimization objective. The execution process of the improved Beluga optimization algorithm includes: initializing algorithm parameters, Beluga populations representing the power allocation schemes of each electrolyzer, the operating powers of each electrolyzer corresponding to the Beluga population, the hydrogen production profit corresponding to each beluga in the Beluga population, and the leading Beluga, wherein the algorithm parameters include the maximum number of iterations; determining the stage corresponding to the current iteration according to the balance factor; updating the operating powers of each electrolyzer corresponding to the Beluga population based on the determined stage, and evaluating the corresponding optimal hydrogen production profit; recording the current optimal hydrogen production profit and its corresponding optimal operating power combination of each electrolyzer, and adding 1 to the current number of iterations N; judging whether the maximum number of iterations has been reached; in response to not reaching the maximum number of iterations, returning to the step of determining the stage of the current iteration according to the balance factor; in response to reaching the maximum number of iterations, outputting the recorded optimal hydrogen production profit and its corresponding optimal operating power combination of each electrolyzer.

[0012] In some embodiments, in the process of initializing algorithm parameters, beluga whale populations representing power allocation schemes for each electrolyzer, the operating power of each electrolyzer corresponding to the beluga whale population, the hydrogen production profit corresponding to each beluga whale in the beluga whale population, and the leading beluga whale, the following steps are performed: obtaining the number of beluga whale populations based on the number of electrolyzers; initializing the position of each beluga whale in the beluga whale population, and generating the operating power of each electrolyzer based on the position of each beluga whale; calculating the hydrogen production profit corresponding to each beluga whale based on the operating power of each electrolyzer through the optimization objective function; sorting the hydrogen production profit corresponding to each beluga whale in the current beluga whale population from high to low, and selecting the beluga whales corresponding to the top M hydrogen production profits as the leading beluga whales.

[0013] In some embodiments, in the process of determining the stage corresponding to the current iteration according to the balance factor, the following steps are performed: obtaining the balance factor using a balance factor calculation formula, wherein the balance factor calculation formula is: N is the current iteration number, N max is the maximum number of iterations, B0 is a random value between 0 and 1, γ is the adjustment coefficient, and D is the distance difference of the leading beluga whale; judge whether the balance factor is greater than the balance factor threshold; in response to the balance factor being greater than the balance factor threshold, determine that the stage corresponding to the current iteration is the exploration stage; in response to the balance factor being not greater than the balance factor threshold, determine that the stage corresponding to the current iteration is the mining stage; after determining that the stage corresponding to the current iteration is the exploration stage or the mining stage, judge whether the balance factor is greater than the whale fall probability; in response to the balance factor being greater than the whale fall probability, determine that the stage corresponding to the current iteration is the whale fall stage; in response to the balance factor being not greater than the whale fall probability, determine that the current iteration does not enter the whale fall stage.

[0014] In some embodiments, when the stage corresponding to the current iteration is the exploration stage, in the process of updating the operating power of each electrolyzer corresponding to the beluga whale population based on the determined stage, the following steps are performed:

[0015] The weighted average position of the leading beluga whale is calculated using the weighted average position calculation formula, where the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale;

[0016] The updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whale through the first updated position calculation formula, where the first updated position calculation formula is:

[0017] is the updated position of the i-th beluga whale in the j-th dimension at iteration number N+1, p j is a random number selected from the d-dimensional space, j = 1, 2, ..., d, is the number of iterations N when the rth beluga whale is in the pth j The position of the dimension, r is the ordinal number of a beluga whale randomly selected from the beluga whale population, h1 is the first random operator, and h2 is the second random operator;

[0018] The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale;

[0019] When the stage corresponding to the current iteration is the mining stage, in the process of updating the operating power of each electrolytic cell corresponding to the beluga whale population based on the determined stage, the following steps are performed: the weighted average position of the leading beluga whale is calculated using the weighted average position calculation formula, wherein the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale; the updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whale through the second updated position calculation formula, where the second updated position calculation formula is: in, is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, where r is the ordinal number of a beluga whale randomly selected from the beluga whale population. is the optimal position of all beluga whales at the iteration number N, h3 is the third random operator, h4 is the fourth random operator, C1 is the random jump intensity used to measure the Lexy flight of beluga whales, N max is the maximum number of iterations, L F For Lexy flight function, u and v are both normally distributed random numbers, β is a constant, The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale.

[0020] In some embodiments, when the stage corresponding to the current iteration is the whale fall stage, in the process of updating the operating power of each electrolyzer corresponding to the beluga whale population based on the determined stage, the following steps are performed: the weighted average position of the leading beluga whale is calculated using a weighted average position calculation formula, wherein the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale; the step length of the whale fall is calculated by the whale fall step length calculation formula, where the whale fall step length calculation formula is: X step is the step size of whale fall, N is the number of iterations, N max is the maximum number of iterations, u b and l b are the upper and lower bounds of the beluga whale population search space, respectively. C2 is the step factor, C2 = 2W f ×n,W f is the probability of whale fall, n is the total number of beluga whales in the beluga whale population, The updated position of each beluga whale is obtained by the third updated position calculation formula based on the weighted average position of the leading beluga whale and the step length of the whale fall. The third updated position calculation formula is: is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, r is the ordinal number of a beluga whale randomly selected from the beluga whale population, h5 is the fifth random operator, h6 is the sixth random operator, and h7 is the seventh random operator. The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale.

[0021] In a second aspect, the present application provides an off-grid wind-solar hydrogen production system multi-electrolyzer power distribution device, which uses the off-grid wind-solar hydrogen production system multi-electrolyzer power distribution method as described in any embodiment of the first aspect to perform multi-electrolyzer power distribution, and the device includes: a system model establishment module for establishing a model of the off-grid wind-solar hydrogen production system, wherein the model of the off-grid wind-solar hydrogen production system includes at least a wind turbine model, a photovoltaic power generation device model, and an alkaline electrolyzer model;

[0022] A constraint establishment module is used to establish system operation constraints based on the model of the off-grid wind-solar hydrogen production system, wherein the system operation constraints include a first constraint and a second constraint, the first constraint includes at least: system power balance constraint, electrolyzer operating power range constraint, electrolyzer start-stop time constraint, and electrolyzer overload continuous operation time constraint, and the second constraint includes the safe operation time constraint of hydrogen concentration in oxygen corresponding to the electrolyzer at different power levels; an optimization target determination module is used to determine the optimization objective function based on the model of the off-grid wind-solar hydrogen production system and the system operation constraints, wherein the optimization objective is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale; an allocation scheme acquisition module is used to apply an optimization algorithm to solve the optimal power allocation scheme for each electrolyzer according to the model of the off-grid wind-solar hydrogen production system, the system operation constraints and the optimization objective.

[0023] Through the multi-electrolyzer power allocation scheme for the off-grid wind-solar hydrogen production system provided above, the embodiment of the present application solves the safety hazard that excessive hydrogen concentration in oxygen may cause explosion by taking the safe operation time of the electrolyzer at different power levels as a constraint, and ensures that the system operates within a safe range and reduces the risk of accidents by controlling the operating power and duration. By taking the maximization of hydrogen production profits as the optimization goal, the power allocation strategy not only considers technical feasibility, but also pays more attention to economic efficiency, which helps to improve the profitability of the entire system. Through intelligent optimization and scheduling, it is possible to more effectively utilize unstable wind and solar energy resources for hydrogen production while meeting various constraints, thereby increasing the absorption ratio of renewable energy. By considering the operating power range, start-stop time, overload operation time and other constraints of the electrolyzer, it helps to avoid the equipment from operating under inappropriate working conditions, reduce the damage caused by frequent start-stop or long-term overload, and may extend the life of the equipment and improve the utilization rate of the equipment.

[0024] Furthermore, in some embodiments, an improved Beluga optimization algorithm is applied to achieve optimal power allocation for multiple electrolyzers in an off-grid wind-solar hydrogen production system. By always taking maximizing hydrogen production profits as the optimization goal and evaluating and recording the optimal solution in each iteration, it is ensured that the algorithm converges towards the predetermined goal. By simulating the social behavior of Beluga whales, global searches and local fine searches can be performed in complex solution spaces, which helps to escape local optimality and find higher hydrogen production profits. The power allocation scheme is continuously updated and improved through multiple iterations, gradually approaching the optimal solution, which can achieve better optimization results than a one-time analytical calculation or a simple rule-based method. By setting the maximum number of iterations as the stopping condition, it is ensured that the algorithm can end and output the results within a limited time.

[0025] Furthermore, in some embodiments, during the initialization and initial evaluation phases, by initializing the positions of the beluga whale population, it is equivalent to generating a set of random, potential power allocation schemes in the solution space. By generating the operating power of each electrolyzer based on the position, the abstract position is converted into a specific power allocation value that meets the actual constraints. The hydrogen production profit corresponding to each beluga whale is calculated by optimizing the objective function, and the quality of these initial random solutions is quantified. By selecting a part of the initial optimal beluga whales as leading beluga whales, these better initial solutions will play a role in guiding the search direction of the population in subsequent algorithm iterations, helping the algorithm to find high-quality solution areas more quickly. At the same time, directly using the hydrogen production profit of the problem as the fitness function ensures that the search direction of the algorithm is completely consistent with the optimization goal of the actual problem.

[0026] Furthermore, in some embodiments, the algorithm's search behavior is dynamically adjusted based on the number of iterations and a certain degree of randomness, deciding whether to conduct a global, extensive search (exploration phase), a local, refined mining phase (exploitation phase), or implement a special escape mechanism (whale fall phase). This provides a powerful perturbation mechanism for the algorithm, helping it escape from local optima and enhancing its global optimization capabilities. The calculation of the balance factor is related to the current iteration number, allowing the algorithm's behavior to adaptively transition from exploration to exploitation as the iterations progress, rather than using a fixed strategy switching, which is more flexible and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0028] Figure 1 An exemplary flow chart of a multi-electrolyzer power allocation method for an off-grid wind-solar hydrogen production system according to an embodiment of the present application is shown;

[0029] Figure 2 The structural topology diagram of the off-grid wind-solar hydrogen production system according to an embodiment of the present application is shown;

[0030] Figure 3 An exemplary flow chart of executing the improved Beluga Whale optimization algorithm according to an embodiment of the present application is shown;

[0031] Figure 4 An exemplary flowchart of the initialization process of an embodiment of the present application is shown;

[0032] Figure 5 An exemplary flow chart of determining the phase corresponding to the current iteration according to the balance factor according to an embodiment of the present application is shown;

[0033] Figure 6 An exemplary structural block diagram of a multi-electrolyzer power distribution device for an off-grid wind-solar hydrogen production system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0035] It should be understood that the terms "include" and "comprising" used in the description and claims of this application indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0036] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" as used in this specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0037] As used in this specification and claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0038] The specific implementation of the present application will be described in detail below with reference to the accompanying drawings.

[0039] Figure 1 An exemplary flow chart of a multi-electrolyzer power allocation method 100 for an off-grid wind-solar hydrogen production system according to an embodiment of the present application is shown.

[0040] like Figure 1 As shown, in step S110, a model of an off-grid wind-solar hydrogen production system is established.

[0041] In the embodiments of this application, the specific components of the off-grid wind-solar hydrogen production system can be found in Figure 2 .

[0042] Figure 2 The structural topology diagram of the off-grid wind-solar hydrogen production system of an embodiment of the present application is shown.

[0043] like Figure 2 As shown, the off-grid wind-solar hydrogen production system includes a wind turbine, a photovoltaic power generation device, and an alkaline electrolyzer, all connected via a DC microgrid. Specifically, the AC power generated by the wind turbine is converted to the required DC power via an AC / DC converter, while the photovoltaic power generation device directly generates DC power. The DC power generated by the photovoltaic power generation device is then regulated to the required DC power via a DC / DC converter. This DC power is then supplied to multiple alkaline electrolyzers for water electrolysis to produce hydrogen.

[0044] In an embodiment of the present application, the constructed off-grid wind-solar hydrogen production system model includes at least a wind turbine model, a photovoltaic power generation device model and an alkaline electrolyzer model.

[0045] Specifically, the expression of the wind turbine model is: Among them, P WT The power generated by the wind turbine, is the air density of the wind turbine environment, S is the area swept by the wind turbine blades, v is the wind speed of the wind turbine environment, C P is the wind energy utilization coefficient of the wind turbine generator set, v in is the cut-in wind speed, i.e. the minimum wind speed at which the wind turbine starts generating electricity, vRate is the rated wind speed, that is, the wind speed when the fan reaches the rated power, P Rate is the rated power, that is, the maximum power that the fan can output at the rated wind speed, v out To cut out the wind speed, that is, the maximum wind speed at which the wind turbine stops generating electricity, in order to protect the wind turbine from being damaged by excessively high wind speeds.

[0046] Specifically, the expression of the photovoltaic power generation device model is: Among them, P PV is the power generation power of the photovoltaic device, P stc is the rated power of the photovoltaic device, S pv is the radiation intensity of the photovoltaic device, S stc is the standard solar radiation intensity, T pv is the photovoltaic device temperature, T ref is the standard ambient temperature, μ is the temperature coefficient, A r is the surface area of ​​the photovoltaic panel, η pv The power generation efficiency of photovoltaic devices.

[0047] Specifically, the expression of the alkaline electrolyzer model is:

[0048] Among them, U el is the cell voltage, n is the number of cells in the cell, U rev is the reversible voltage, T is the cell temperature, P is the cell pressure, r1, r2, d1, d2, s, t1, t2 and t3 are coefficients, I is the current, A is the electrode surface area of ​​the cell, P ele is the power consumed by the electrolyzer for hydrogen production, P el is the operating power of the electrolytic cell, f1 is the first fitting parameter, f2 is the second fitting parameter, U th is the thermal neutral voltage of the electrolytic cell.

[0049] The alkaline electrolyzer model calculates the voltage of the electrolyzer (U el ), power consumed for hydrogen production (P ele ) and total operating power (P el ), taking into account the influence of factors such as current (I), temperature (T), pressure (P), electrode surface area (A) and the number of electrolyzer chambers (n) on voltage and power, providing a basis for the subsequent optimization of power allocation strategy. This model can be used to calculate the power consumption of the electrolyzer under different operating conditions, and then calculate the hydrogen production (related to current I or P ele Related) and operating costs are the prerequisites for achieving the optimization goal of maximizing hydrogen production profits and applying the improved Beluga optimization algorithm to solve the problem.

[0050] By constructing an off-grid wind-solar hydrogen production system model, we can better understand and predict the interactions between wind power generation, photovoltaic power generation and alkaline electrolyzers, thereby optimizing resource allocation and improving the energy utilization efficiency of the entire off-grid wind-solar hydrogen production system.

[0051] After executing step S110 , in step S120 , system operation constraints are established based on the model of the off-grid wind-solar hydrogen production system.

[0052] Specifically, the system operating constraints include a first constraint and a second constraint. The first constraint includes at least: a system power balance constraint, an electrolyzer operating power range constraint, an electrolyzer start / stop time constraint, and an electrolyzer overload continuous operation time constraint. The second constraint includes a safe operating time constraint for the electrolyzer's hydrogen-in-oxygen concentration at different power levels.

[0053] Specifically, the expression of the first constraint is: Among them, P WT (t) is the power generated by the wind turbine at time t, P PV (t) is the power generation power of the photovoltaic device at time t, P el,i (t) is the operating power of the i-th electrolytic cell at time t, n is the total number of electrolytic cells, P waste (t) is the unabsorbed power at time t, P el,i,min is the minimum operating power of the i-th electrolytic cell, P el,i,max is the maximum operating power of the i-th electrolytic cell, T on / off is the start and stop time of the electrolytic cell, T work,i is the electrolytic cell operating time, T ele,i is the overload operation time of the i-th electrolytic cell, T max P is the continuous operation time of the electrolytic cell at maximum overload power, e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, P el,max is the maximum operating power of the electrolyzer.

[0054] Through the first constraint, the operating power range, start-stop time, overload operation time and other constraints of the electrolyzer are taken into account, which helps to avoid the equipment from operating under inappropriate working conditions, reduce the damage caused by frequent start-stop or long-term overload, and may extend the equipment life and improve the equipment utilization rate.

[0055] Specifically, the expression for the safe operating time constraint of hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer is: Among them, T el,i is the operating time of the i-th electrolytic cell, P e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, T25％-50％ For electrolyzers operating at 25% P e -50% P e Safe and stable operation time, T 50％-75％ For electrolyzers operating at 50% P e -75% P e Safe and stable operation time, T h is the safe operation time after the electrolyzer increases power, t-Δt is the previous time period before the current time t, P el,i,t-Δt is the operating power of the i-th electrolytic cell in the previous time period before the current time t, P el,i,t is the operating power of the i-th electrolyzer in the current time period t.

[0056] Through the safe operation time constraints of hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer, it is clear that the electrolyzer has different power ranges (especially the lower power range, such as 25%-50% of the rated power P e ) The upper limit of the safe time for sustainable operation. By incorporating this constraint into the subsequent power allocation strategy, the system can be forced to avoid being in a low-power operating state for a long time during operation, which is likely to cause the hydrogen concentration in oxygen to exceed the standard, thereby effectively preventing safety accidents and improving the overall safety of the system. Compared with simply setting a minimum operating power (such as 25% P e ), which allows the system to operate at lower power but limits its duration. This provides a more flexible and refined safety management method, allowing the system to make better use of renewable energy (even during periods of low power generation) while ensuring safety. By precisely controlling the low-power operation time rather than completely prohibiting it, this constraint helps reduce the frequent start and stop of electrolyzers caused by power fluctuations, thereby improving equipment utilization. At the same time, it allows the system to absorb lower levels of wind and solar power generation to a certain extent, helping to increase the overall absorption ratio of renewable energy and reduce energy waste.

[0057] By constructing the corresponding operating constraints for the off-grid wind-solar hydrogen production system model, the intermittent and fluctuating nature of wind and solar energy, as well as the operating limits of the alkaline electrolyzer, can be taken into account, thereby ensuring that the off-grid wind-solar hydrogen production system model can operate stably even under adverse conditions.

[0058] After executing step S120, in step S130, an optimization objective function is determined based on the model of the off-grid wind-solar hydrogen production system and the system operation constraints.

[0059] Specifically, the optimization goal is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale.

[0060] In the embodiment of the present application, the expression of the optimization objective function is:

[0061] Among them, T is the total time range, C is the profit of hydrogen production within the time range T, P ele,t is the power consumed by the electrolyzer for hydrogen production during time period t, C H2 is the unit hydrogen selling price, q H2 is the power consumed to produce unit hydrogen, C on is the electrolytic cell startup cost coefficient, C off is the electrolytic cell shutdown cost coefficient, Y el,x,t is the electrolytic cell startup state variable, indicating whether electrolytic cell x is started in the current time period t, Z el,y,t is the electrolytic cell stop state variable, indicating whether electrolytic cell y is stopped in the current time period t, p is the number of electrolytic cells started in the current time period, q is the number of electrolytic cells stopped in the current time period, C ele is the electrolytic cell operation and maintenance cost coefficient, P el,k,t is the operating power of electrolytic cell k in time period t, and n is the total number of electrolytic cells.

[0062] By optimizing the objective function, directly targeting hydrogen production profits, the system comprehensively considers hydrogen sales revenue and various costs in the production process (unit hydrogen electricity consumption cost, electrolyzer start-up and shutdown costs, and operation and maintenance costs), so that the power allocation strategy is guided by achieving optimal economic benefits. Including the start-up and shutdown costs and operation and maintenance costs of the electrolyzer in the optimization objectives helps to reduce unnecessary start-up and shutdown operations, reduce equipment losses and maintenance expenses, thereby extending equipment life and reducing total costs. By optimizing the power allocation of each electrolyzer, it is ensured that while meeting various operating constraints (such as power balance and safe operating time), as much renewable energy (wind and solar) as possible is converted into hydrogen and maximized profits are obtained, thereby improving the energy utilization efficiency and economic efficiency of the entire system.

[0063] After executing step S130, in step S140, an optimization algorithm is applied to obtain the optimal power allocation plan for each electrolyzer based on the model of the off-grid wind-solar hydrogen production system, system operation constraints and optimization objectives.

[0064] In an embodiment of the present application, in the process of solving the optimal power allocation plan for each electrolyzer based on the model, system operation constraints and optimization objectives of the off-grid wind-solar hydrogen production system, the improved Beluga optimization algorithm is applied to solve the optimization objectives.

[0065] In the embodiment of the present application, the execution process of the improved Beluga optimization algorithm can be found in Figure 3 .

[0066] Figure 3 An exemplary flow chart of executing the improved Beluga Whale optimization algorithm according to an embodiment of the present application is shown.

[0067] like Figure 3 As shown, in step S310, the algorithm parameters, the beluga whale population representing the power allocation scheme for each electrolyzer, the operating power of each electrolyzer corresponding to the beluga whale population, the hydrogen production profit corresponding to each beluga whale in the beluga whale population, and the leading beluga whale are initialized. In step S320, the stage corresponding to the current iteration is determined based on the balance factor. In step S330, the operating power of each electrolyzer corresponding to the beluga whale population is updated based on the determined stage, and the corresponding optimal hydrogen production profit is evaluated. In step S340, the current optimal hydrogen production profit and its corresponding optimal operating power combination for each electrolyzer are recorded. In step S350, 1 is added to the current number of iterations N. In step S360, it is determined whether the maximum number of iterations has been reached. In response to not reaching the maximum number of iterations, the process returns to step S320, that is, returns to the step of determining the stage of the current iteration based on the balance factor. In response to reaching the maximum number of iterations, in step S370, the recorded optimal hydrogen production profit and its corresponding optimal operating power combination for each electrolyzer are output.

[0068] In an embodiment of the present application, the algorithm parameters include a maximum number of iterations, a balance factor threshold, a random operator, a normally distributed random number, a constant β, a step factor, and the like.

[0069] In the embodiments of the present application, the maximum number of iterations, the balance factor threshold, the random operator, the normally distributed random number, the constant β, and the step factor are set according to actual needs and historical experience, and the present application does not impose any restrictions thereon.

[0070] In the embodiment of the present application, the specific process involved in executing step S310 can be found in Figure 4 .

[0071] Figure 4 An exemplary flowchart of the initialization process of an embodiment of the present application is shown.

[0072] like Figure 4 As shown, in step S410, the number of beluga whales in the population is obtained based on the number of electrolyzers. In step S420, the position of each beluga whale in the population is initialized, and the operating power of each electrolyzer is generated based on the position of each beluga whale. In step S430, the hydrogen production profit corresponding to each beluga whale is calculated based on the operating power of each electrolyzer by optimizing the objective function. In step S440, the hydrogen production profit corresponding to each beluga whale in the current population is sorted from high to low, and the beluga whales corresponding to the top M hydrogen production profits are selected as the leading beluga whales.

[0073] Specifically, the operating power of each electrolyzer is generated based on the position of each beluga whale. n is the number of beluga whales in the beluga whale population, and d is the dimension of the variable corresponding to each beluga whale.

[0074] Specifically, the hydrogen production profit combinations corresponding to all white whales are: f(·) is the optimization objective function, which is to obtain the maximum value of the corresponding hydrogen production profit according to the operating power combination of the electrolyzer corresponding to each beluga whale as the hydrogen production profit of each beluga whale.

[0075] In the embodiments of the present application, the value of M is set according to actual needs and historical experience, and the present application does not limit it. For example, in some embodiments, M is set to 3, that is, the number of leading white whales is 3.

[0076] After each iteration, the beluga whales are reordered based on their corresponding hydrogen production profits, and the leader whale is updated. During each iteration, some beluga whales are randomly selected from the beluga whale population as challenge whales. If the challenge whale's hydrogen production profit is higher than that of a leader whale, the leader whale is replaced.

[0077] In the embodiment of the present application, the specific process involved in executing step S320 can be found in Figure 5 .

[0078] Figure 5 An exemplary flowchart of determining the phase corresponding to the current iteration according to the balance factor according to an embodiment of the present application is shown.

[0079] like Figure 5 As shown, in step S510, the balance factor is obtained by the balance factor calculation formula. In step S520, it is determined whether the balance factor is greater than the balance factor threshold. In response to the balance factor being greater than the balance factor threshold, in step S530, it is determined that the stage corresponding to the current iteration is the exploration stage. In response to the balance factor being not greater than the balance factor threshold, in step S540, it is determined that the stage corresponding to the current iteration is the mining stage. After executing step S530 or step S540, in step S550, it is determined whether the balance factor is greater than the whale fall probability. In response to the balance factor being greater than the whale fall probability, in step S560, it is determined that the stage corresponding to the current iteration is the whale fall stage. In response to the balance factor being not greater than the whale fall probability, in step S570, it is determined that the current iteration does not enter the whale fall stage.

[0080] Specifically, the balance factor calculation formula is: N is the current iteration number, N max is the maximum number of iterations, B0 is a random value between 0 and 1, γ is the adjustment coefficient, and D is the distance difference of the leading beluga whale.

[0081] In an embodiment of the present application, when the current iteration corresponds to the exploration phase, when updating the operating power of each electrolytic cell corresponding to the beluga whale population based on the determined phase, the weighted average position of the leading beluga whales is first calculated using the weighted average position calculation formula. Next, the updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whales using the first updated position calculation formula. Finally, the operating power of the corresponding electrolytic cell is obtained based on the updated position of each beluga whale.

[0082] Specifically, the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale.

[0083] Specifically, the first updated position calculation formula is:

[0084] is the updated position of the i-th beluga whale in the j-th dimension at iteration number N+1, p j is a random number selected from the d-dimensional space, j = 1, 2, ..., d, is the number of iterations N when the rth beluga whale is in the pth j dimension, r is the ordinal number of a beluga whale randomly selected from the beluga whale population, h1 is the first random operator, h2 is the second random operator, sin(2πh2) and cos(2πh2) indicate that the fin of the mirrored beluga whale is facing the water surface.

[0085] Through the above process, the exploration phase simulates the paired swimming behavior of beluga whales (synchronous or mirrored), introduces random operators (h1, h2, h3, h4) and randomly selects the positions of other beluga whales. This enables the algorithm to conduct a wider search in the solution space and explore more diverse electrolyzer power allocation schemes. Through extensive exploration, it can effectively avoid the algorithm from falling into local optimal solutions too early, and increase the chances of finding the electrolyzer power allocation strategy corresponding to the global optimal (or near-optimal) hydrogen production profit. Using the social behavior of beluga whales (paired swimming) as inspiration provides an effective mechanism for generating new candidate solutions (i.e., new power allocation schemes).

[0086] In an embodiment of the present application, when the current iteration corresponds to the mining phase, when updating the operating power of each electrolytic cell corresponding to the beluga whale population based on the determined phase, the weighted average position of the leading beluga whales is first calculated using the aforementioned weighted average position calculation formula. Next, the updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whales using a second updated position calculation formula. Finally, the operating power of the corresponding electrolytic cell is obtained based on the updated position of each beluga whale.

[0087] Specifically, the second updated position calculation formula is: in, is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, where r is the ordinal number of a beluga whale randomly selected from the beluga whale population. is the optimal position of all beluga whales at the iteration number N, h3 is the third random operator, h4 is the fourth random operator, C1 is the random jump intensity used to measure the Lexy flight of beluga whales, N max is the maximum number of iterations, L F For Lexy flight function, u and v are both normally distributed random numbers, β is a constant,

[0088] Through the above process, the mining phase simulates the behavior of beluga whales exchanging information and jointly hunting in the ocean. This simulation allows for more efficient exploration of the solution space, facilitating a convergence towards the optimal solution (the power allocation scheme that optimizes hydrogen production profit). This utilizes the Levy flight strategy, which helps improve algorithm convergence.

[0089] In an embodiment of the present application, when the stage corresponding to the current iteration is the whale fall stage, in the process of updating the operating power of each electrolytic cell corresponding to the beluga whale population based on the determined stage, first, the weighted average position of the leading beluga whale is calculated by the aforementioned weighted average position calculation formula. Next, the step length of the whale fall is calculated by the whale fall step length calculation formula. Then, the updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whale and the step length of the whale fall by using the third updated position calculation formula. Finally, the operating power of the corresponding electrolytic cell is obtained by the updated position of each beluga whale.

[0090] Specifically, the calculation formula for whale fall step length is: X step is the step size of whale fall, N is the number of iterations, N max is the maximum number of iterations, u b and l b are the upper and lower bounds of the beluga whale population search space, respectively. C2 is the step factor, C2 = 2W f×n,W f is the probability of whale fall, n is the total number of beluga whales in the beluga whale population,

[0091] Specifically, the third updated position calculation formula is: is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, r is the ordinal number of the beluga whale randomly selected from the beluga whale population, h5 is the fifth random operator, h6 is the sixth random operator, and h7 is the seventh random operator.

[0092] Through the above process, the positions of the beluga whales are updated during the whale drop phase using random numbers (h5, h6, h7), the whale drop step size, the upper and lower bounds of the variables, the step factor, the whale drop probability, and the total number of beluga whales in the population. This introduction of a significant amount of randomness and step size factors is generally intended to help the algorithm escape potential local optima and explore regions of the search space that may be far from the current optimal solution, thereby increasing the probability of finding the global optimal solution (i.e., the optimal electrolyzer power allocation and maximum hydrogen production profit). This specific, probabilistically triggered update mechanism introduces new diversity into the population (representing the set of power allocation solutions) and prevents the algorithm from prematurely converging to a suboptimal solution. Therefore, updating the beluga whale population (i.e., updating the candidate solutions for each electrolyzer's operating power) during the whale drop phase enhances the algorithm's global search capabilities, helps avoid local optima, and maintains population diversity, thereby increasing the probability of finding the optimal electrolyzer power allocation and maximizing hydrogen production profit.

[0093] In summary, through the multi-electrolyzer power allocation scheme for the off-grid wind-solar hydrogen production system provided above, the embodiment of the present application solves the safety hazard that excessive hydrogen concentration in oxygen may cause explosion by taking the safe operation time of the electrolyzer at different power levels as a constraint, and ensures that the system operates within a safe range and reduces the risk of accidents by controlling the operating power and duration. By taking the maximization of hydrogen production profits as the optimization goal, the power allocation strategy not only considers technical feasibility, but also pays more attention to economic efficiency, which helps to improve the profitability of the entire system. Through intelligent optimization and scheduling, it is possible to more effectively utilize unstable wind and solar energy resources for hydrogen production while meeting various constraints, thereby increasing the absorption ratio of renewable energy. By considering the operating power range, start-stop time, overload operation time and other constraints of the electrolyzer, it helps to avoid the equipment from operating under inappropriate working conditions, reduce the damage caused by frequent start-stop or long-term overload, and may extend the life of the equipment and improve the utilization rate of the equipment.

[0094] Furthermore, in some embodiments, an improved Beluga optimization algorithm is applied to achieve optimal power allocation for multiple electrolyzers in an off-grid wind-solar hydrogen production system. By always taking maximizing hydrogen production profits as the optimization goal and evaluating and recording the optimal solution in each iteration, it is ensured that the algorithm converges towards the predetermined goal. By simulating the social behavior of Beluga whales, global searches and local fine searches can be performed in complex solution spaces, which helps to escape local optimality and find higher hydrogen production profits. The power allocation scheme is continuously updated and improved through multiple iterations, gradually approaching the optimal solution, which can achieve better optimization results than a one-time analytical calculation or a simple rule-based method. By setting the maximum number of iterations as the stopping condition, it is ensured that the algorithm can end and output the results within a limited time.

[0095] Furthermore, in some embodiments, during the initialization and initial evaluation phases, by initializing the positions of the beluga whale population, it is equivalent to generating a set of random, potential power allocation schemes in the solution space. By generating the operating power of each electrolyzer based on the position, the abstract position is converted into a specific power allocation value that meets the actual constraints. The hydrogen production profit corresponding to each beluga whale is calculated by optimizing the objective function, and the quality of these initial random solutions is quantified. By selecting a part of the initial optimal beluga whales as leading beluga whales, these better initial solutions will play a role in guiding the search direction of the population in subsequent algorithm iterations, helping the algorithm to find high-quality solution areas more quickly. At the same time, directly using the hydrogen production profit of the problem as the fitness function ensures that the search direction of the algorithm is completely consistent with the optimization goal of the actual problem.

[0096] Furthermore, in some embodiments, the algorithm's search behavior is dynamically adjusted based on the number of iterations and a certain degree of randomness, deciding whether to conduct a global, extensive search (exploration phase), a local, refined mining phase (exploitation phase), or implement a special escape mechanism (whale fall phase). This provides a powerful perturbation mechanism for the algorithm, helping it escape from local optima and enhancing its global optimization capabilities. The calculation of the balance factor is related to the current iteration number, allowing the algorithm's behavior to adaptively transition from exploration to exploitation as the iterations progress, rather than using a fixed strategy switching, which is more flexible and efficient.

[0097] An embodiment of the present application also provides a multi-electrolyzer power distribution device for an off-grid wind-solar hydrogen production system, which can use the aforementioned off-grid wind-solar hydrogen production system multi-electrolyzer power distribution method 100 to perform multi-electrolyzer power distribution, or can use other methods to perform multi-electrolyzer power distribution, which is not limited in the present application.

[0098] The following combination Figure 6 A detailed explanation is given of the multi-electrolyzer power distribution device of the off-grid wind-solar hydrogen production system.

[0099] Figure 6FIG1 shows an exemplary structural block diagram of a multi-electrolyzer power distribution device 600 for an off-grid wind-solar hydrogen production system according to an embodiment of the present application.

[0100] like Figure 6 As shown, the apparatus 600 includes a system model building module 610, a constraint condition building module 620, an optimization target determination module 630, and an allocation solution acquisition module 640. In the embodiment of the present application, the system model building module 610, the constraint condition building module 620, the optimization target determination module 630, and the allocation solution acquisition module 640 may be separate units or integrated into the same integrated circuit, which is not limited in the present application.

[0101] Specifically, the system model building module 610 is used to build a model of an off-grid wind-solar hydrogen production system, wherein the model of the off-grid wind-solar hydrogen production system at least includes a wind turbine model, a photovoltaic power generation model and an alkaline electrolyzer model.

[0102] Specifically, the constraint condition establishment module 620 is used to establish system operation constraints based on the model of the off-grid wind-solar hydrogen production system, wherein the system operation constraints include a first constraint and a second constraint, the first constraint includes at least: system power balance constraint, electrolyzer operating power range constraint, electrolyzer start-stop time constraint and electrolyzer overload continuous operation time constraint, the second constraint includes the safe operation time constraint of hydrogen concentration in oxygen corresponding to the electrolyzer at different power levels.

[0103] Specifically, the optimization target determination module 630 is used to determine the optimization objective function based on the model of the off-grid wind-solar hydrogen production system and the system operation constraints, wherein the optimization target is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale.

[0104] Specifically, the allocation scheme acquisition module 640 is used to apply an optimization algorithm to obtain the optimal power allocation scheme for each electrolyzer based on the model of the off-grid wind-solar hydrogen production system, system operation constraints and optimization objectives.

[0105] When device 600 employs the aforementioned off-grid wind-solar hydrogen production system multi-electrolyzer power allocation method 100 to perform multi-electrolyzer power allocation, system model establishment module 610 executes the aforementioned step S110, constraint condition establishment module 620 executes the aforementioned step S120, optimization target determination module 630 executes the aforementioned step S130, and allocation scheme acquisition module 640 executes the aforementioned step S140. The specific execution process can be found in the previous text and will not be repeated here.

[0106] Although multiple embodiments of the present application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art can conceive of many changes, modifications, and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be adopted. The accompanying claims are intended to define the scope of protection of the present application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system, characterized in that: include: Establishing a model of an off-grid wind-solar hydrogen production system, wherein the model of the off-grid wind-solar hydrogen production system includes at least a wind turbine model, a photovoltaic power generation device model, and an alkaline electrolyzer model; Based on the model of the off-grid wind-solar hydrogen production system, establishing system operation constraints, wherein the system operation constraints include a first constraint and a second constraint, the first constraint including at least: a system power balance constraint, an electrolyzer operating power range constraint, an electrolyzer start-stop time constraint, and an electrolyzer overload continuous operation time constraint, and the second constraint including a safe operation time constraint for hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer; Determining an optimization objective function based on the model of the off-grid wind-solar hydrogen production system and system operation constraints, wherein the optimization objective is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale; By applying an optimization algorithm, the optimal power allocation scheme for each electrolyzer is obtained according to the model of the off-grid wind-solar hydrogen production system, system operation constraints and optimization objectives.

2. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 1 is characterized in that: The expression of the first constraint is: Among them, P WT (t) is the power generated by the wind turbine at time t, P PV (t) is the power generation power of the photovoltaic device at time t, P el,i (t) is the operating power of the i-th electrolytic cell at time t, n is the total number of electrolytic cells, P waste (t) is the unabsorbed power at time t, P el,i,min is the minimum operating power of the i-th electrolytic cell, P el,i,max is the maximum operating power of the i-th electrolytic cell, T on / off is the start and stop time of the electrolytic cell, T work,i is the electrolytic cell operating time, T ele,i is the overload operation time of the i-th electrolytic cell, T max P is the continuous operation time of the electrolytic cell at maximum overload power, e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, P el,max is the maximum operating power of the electrolyzer.

3. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 1 is characterized in that: The expression for the safe operating time constraint of hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer is: Among them, T el,i is the operating time of the i-th electrolytic cell, P e is the rated power of the electrolytic cell, P el,i is the operating power of the i-th electrolytic cell, T 25％-50％ For electrolyzers operating at 25% P e -50% P e Safe and stable operation time, T 50％-75％ For electrolyzers operating at 50% P e -75% P e Safe and stable operation time, T h is the safe operation time after the electrolyzer increases power, t-Δt is the previous time period before the current time t, P el,i,t-Δt is the operating power of the i-th electrolytic cell in the previous time period before the current time t, P el,i,t is the operating power of the i-th electrolyzer in the current time period t.

4. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 1, characterized in that: The expression of the optimization objective function is: Among them, T is the total time range, C is the profit of hydrogen production within the time range T, P ele,t is the power consumed by the electrolyzer for hydrogen production during time period t, is the unit hydrogen selling price, is the power consumed to produce unit hydrogen, C on is the electrolytic cell startup cost coefficient, C off is the electrolytic cell shutdown cost coefficient, Y el,x,t is the electrolytic cell startup state variable, indicating whether electrolytic cell x is started in the current time period t, Z el,y,t is the electrolytic cell stop state variable, indicating whether electrolytic cell y is stopped in the current time period t, p is the number of electrolytic cells started in the current time period, q is the number of electrolytic cells stopped in the current time period, C ele is the electrolytic cell operation and maintenance cost coefficient, P el,k,t is the operating power of electrolytic cell k in time period t, and n is the total number of electrolytic cells.

5. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 1 is characterized in that: In the process of solving the optimal power allocation scheme for each electrolyzer based on the model, system operation constraints and optimization objectives of the off-grid wind-solar hydrogen production system, the improved Beluga optimization algorithm is applied to solve the optimization objectives. The execution process of the improved Beluga optimization algorithm includes: Initialize the algorithm parameters, the beluga whale population representing the power allocation plan for each electrolyzer, the operating power of each electrolyzer corresponding to the beluga whale population, the hydrogen production profit corresponding to each beluga whale in the beluga whale population, and the leading beluga whale. The algorithm parameters include the maximum number of iterations; Determine the stage corresponding to the current iteration based on the balance factor; Based on the determined stage, the operating power of each electrolyzer corresponding to the beluga whale population is updated, and the corresponding optimal hydrogen production profit is evaluated; Record the current optimal hydrogen production profit and its corresponding optimal operating power combination for each electrolyzer, and add 1 to the current number of iterations N; Determine whether the maximum number of iterations has been reached; In response to not reaching the maximum number of iterations, returning to the step of determining the phase of the current iteration according to the balance factor; In response to reaching the maximum number of iterations, the recorded optimal hydrogen production profit and its corresponding optimal operating power combination of each electrolyzer are output.

6. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 5, characterized in that: To initialize the algorithm parameters, the beluga whale population representing the power allocation scheme for each electrolyzer, the operating power of each electrolyzer corresponding to the beluga whale population, the hydrogen production profit corresponding to each beluga whale in the beluga whale population, and the leading beluga whale, the following steps are performed: The number of beluga whale populations is obtained based on the number of electrolyzers; Initialize the position of each beluga whale in the beluga whale population, and generate the operating power of each electrolyzer based on the position of each beluga whale; Calculating the hydrogen production profit corresponding to each beluga whale based on the operating power of each electrolyzer through the optimization objective function; Sort the hydrogen production profits corresponding to each beluga whale in the current beluga whale population from high to low, and select the beluga whales corresponding to the top M hydrogen production profits as the leading beluga whales.

7. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 5, characterized in that: In the process of determining the phase corresponding to the current iteration based on the balance factor, the following steps are performed: The balance factor is obtained by the balance factor calculation formula, where the balance factor calculation formula is: N is the current iteration number, N max is the maximum number of iterations, B0 is a random value between 0 and 1, γ is the adjustment coefficient, and D is the distance difference of the leading beluga whale; Determine whether the balance factor is greater than the balance factor threshold; In response to the balance factor being greater than the balance factor threshold, determining that the phase corresponding to the current iteration is an exploration phase; In response to the balance factor being not greater than the balance factor threshold, determining that the phase corresponding to the current iteration is a mining phase; After determining whether the current iteration corresponds to the exploration phase or the mining phase, determine whether the balance factor is greater than the whale fall probability; In response to the balance factor being greater than the whale fall probability, determining that the phase corresponding to the current iteration is the whale fall phase; In response to the balance factor being not greater than the whale fall probability, determining that the current iteration does not enter the whale fall phase.

8. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 7, characterized in that: When the phase corresponding to the current iteration is the exploration phase, in the process of updating the operating power of each electrolyzer corresponding to the beluga whale population based on the determined phase, the following steps are performed: The weighted average position of the leading beluga whale is calculated using the weighted average position calculation formula, where the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale; The updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whale through the first updated position calculation formula, where the first updated position calculation formula is: is the updated position of the i-th beluga whale in the j-th dimension at iteration number N+1, p j is a random number selected from the d-dimensional space, j = 1, 2, ..., d, is the number of iterations N when the rth beluga whale is in the pth j The position of the dimension, r is the ordinal number of a beluga whale randomly selected from the beluga whale population, h1 is the first random operator, and h2 is the second random operator; The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale; When the stage corresponding to the current iteration is the mining stage, in the process of updating the operating power of each electrolytic cell corresponding to the beluga whale population based on the determined stage, the following steps are performed: The weighted average position of the leading beluga whale is calculated using the weighted average position calculation formula, where the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale; The updated position of each beluga whale is obtained based on the weighted average position of the leading beluga whale through the second updated position calculation formula, wherein the second updated position calculation formula is: in, is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, where r is the ordinal number of a beluga whale randomly selected from the beluga whale population. is the optimal position of all beluga whales at the iteration number N, h3 is the third random operator, h4 is the fourth random operator, C1 is the random jump intensity used to measure the Lexy flight of beluga whales, N max is the maximum number of iterations, L F For Lexy flight function, u and v are both normally distributed random numbers, β is a constant, The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale.

9. The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to claim 7 or 8, characterized in that: When the stage corresponding to the current iteration is the whale fall stage, in the process of updating the operating power of each electrolyzer corresponding to the beluga whale population based on the determined stage, the following steps are performed: The weighted average position of the leading beluga whale is calculated using the weighted average position calculation formula, where the weighted average position calculation formula is: M is the total number of leading beluga whales, is the weighted average position of the M leading white whales at the iteration number N, is the position of the i-th leading white whale at iteration number N, ω i is the weight of the i-th leading beluga whale; The step length of a whale fall is calculated using the whale fall step length calculation formula, where the whale fall step length calculation formula is: X step is the step size of whale fall, N is the number of iterations, N max is the maximum number of iterations, u b and l b are the upper and lower bounds of the beluga whale population search space, respectively. C2 is the step factor, C2 = 2W f ×n,W f is the probability of whale fall, n is the total number of beluga whales in the beluga whale population, The updated position of each beluga whale is obtained by the third updated position calculation formula based on the weighted average position of the leading beluga whale and the step length of the whale fall. The third updated position calculation formula is: is the updated position of the i-th beluga whale at iteration number N+1, is the position of the rth beluga whale at iteration number N, r is the ordinal number of a beluga whale randomly selected from the beluga whale population, h5 is the fifth random operator, h6 is the sixth random operator, and h7 is the seventh random operator; The operating power of the corresponding electrolyzer is obtained through the updated position of each beluga whale.

10. A multi-electrolyzer power distribution device for an off-grid wind-solar hydrogen production system, characterized in that: The multi-electrolyzer power distribution method for an off-grid wind-solar hydrogen production system according to any one of claims 1 to 9 is used to distribute power to multiple electrolyzers, the device comprising: A system model building module, used to build a model of an off-grid wind-solar hydrogen production system, wherein the model of the off-grid wind-solar hydrogen production system includes at least a wind turbine model, a photovoltaic power generation device model, and an alkaline electrolyzer model; a constraint establishment module, configured to establish system operation constraints based on a model of the off-grid wind-solar hydrogen production system, wherein the system operation constraints include a first constraint and a second constraint, the first constraint including at least: a system power balance constraint, an electrolyzer operating power range constraint, an electrolyzer start-stop time constraint, and an electrolyzer overload continuous operation time constraint, and the second constraint including a safe operation time constraint for hydrogen concentration in oxygen corresponding to different power levels of the electrolyzer; an optimization target determination module, configured to determine an optimization objective function based on the model of the off-grid wind-solar hydrogen production system and system operation constraints, wherein the optimization objective is to maximize the hydrogen production profit of the electrolyzer module within a preset time scale; The allocation scheme acquisition module is used to apply an optimization algorithm to solve the optimal power allocation scheme for each electrolyzer based on the model of the off-grid wind-solar hydrogen production system, system operation constraints and optimization objectives.

Images