Photovoltaic hybrid hydrogen production system and capacity configuration optimization method

Abstract

The invention provides a photovoltaic hybrid hydrogen production system and a capacity configuration optimization method. The system is formed by sequentially connecting a renewable energy power generation unit, an electrolytic hydrogen production unit and a hydrogen energy storage unit. Wherein the power generation unit outputs electric energy by means of a photovoltaic array; aLK and PEM are used in the water electrolysis hydrogen production unit, through cooperative coupling, the adaptability of the hydrogen production process to unstable solar power generation is enhanced, and efficient and stable preparation of green hydrogen under driving of fluctuating photovoltaic electric energy is enhanced. According to the invention, the two types of electrolytic cells are combined and researched, and the two types of electrolytic cells are combined for hydrogen production to form an electrolytic hydrogen production unit so as to give full play to respective advantages. And the economy of the whole hydrogen production system is improved by determining the optimal ratio of the two components in the system. And the prepared hydrogen can be conveyed to a user side or stored in a hydrogen storage tank through a high-pressure storage technology and the like, so that cross-regional transmission and utilization of renewable energy sources are realized.

Description

Technical Field

[0001] The present invention relates to a water electrolysis hydrogen production technology, and in particular to a photovoltaic hybrid hydrogen production system and a capacity configuration optimization method Technical Background

[0002] In recent years, the growing demand for energy and the focus on sustainable development have promoted the development of solar energy utilization, which is widely distributed on the earth and has huge reserves. However, the randomness and volatility of solar energy are the main obstacles to the use of renewable energy for power generation, which makes it difficult to control the quality and predict the quantity of its transmission and access to the power grid. The problems of abandoned light and power rationing not only seriously affect the economic benefits of power generation companies, but also cause a large amount of energy waste, which has become the main contradiction affecting the healthy development of renewable energy. Facing the diversified energy supply system in the future, in order to improve the efficiency and stability of power generation and maximize the use of photovoltaic energy, photovoltaic power generation technology has become a hot research and development field.

[0003] When there is a surplus of energy, it makes sense to produce clean fuels such as hydrogen at the same time. Hydrogen is a valuable energy carrier and an efficient storage medium. Water electrolysis is a clean, low-carbon, safe and mature technology for hydrogen production. From a life cycle perspective, solar energy is a typical low-carbon energy source, and clean energy provides electricity for electrolysis to produce hydrogen. The energy storage method of using photovoltaic power generation to electrolyze water to produce hydrogen and then using hydrogen fuel cells to generate electricity is mature and has been proven to have the following advantages: 1) Reduce fossil fuel consumption and the release of pollutants; 2) Improve energy utilization and reduce abandoned light; 3) The coupling of low-carbon energy with traditional resources can realize the localized application of solar energy.

[0004] At present, the main technical routes for hydrogen production by water electrolysis include alkaline water electrolysis (ALK), proton exchange membrane water electrolysis (PEM) and solid oxide water electrolysis (SOEC). The current technical routes of ALK and PEM are relatively mature, and PEM is more suitable for photovoltaic fluctuating power sources.

[0005] Although the ALK route has high technical maturity, due to the characteristics of the diaphragm, it has a higher contact resistance, so the current density of the ALK route is higher than that of the PEM route, resulting in a larger footprint of the alkaline electrolyzer. Fluctuating power supply conditions will affect the efficiency of ALK. The ALK-supercapacitor operation control strategy can improve the dynamic efficiency of hydrogen production, but it increases complexity and cost. In addition, under low load, hydrogen will pass through the diaphragm and mix with oxygen, which poses a potential safety risk, so there are concentration detectors on both the hydrogen and oxygen sides of the ALK route.

[0006] Since the PEM route uses proton exchange membrane, it does not have the problems of the ALK route, so it is more suitable for absorbing fluctuating power sources such as wind and solar. However, due to problems such as proton exchange membrane and the use of precious metal catalysts, the cost of the PEM route is still relatively high. Summary of the invention

[0007] The present invention aims to overcome the defects of the prior art. In view of the adverse effects of the randomness and volatility of existing solar energy resources on the dynamic operation of hydrogen electrolysis, and the problems of poor economy of PEM electrolysis hydrogen production and low efficiency of ALK electrolysis hydrogen production and great influence of photovoltaic fluctuations, a photovoltaic hydrogen production system considering the synergy of PEM-ALK technology is proposed, and the capacity configuration and operation optimization of the system are carried out.

[0008] One of the technical solutions to achieve the above purpose is: a photovoltaic hybrid hydrogen production system, characterized in that: the system is composed of a renewable energy power generation unit, an electrolytic hydrogen production unit and a hydrogen energy storage unit connected in sequence, wherein the renewable energy power generation unit is a photovoltaic power generation system, which outputs electric energy with the help of a photovoltaic array; the electrolytic hydrogen production unit uses an ALK electrolyzer and a PEM electrolyzer in parallel, and through their synergistic coupling, enhances the adaptability of the hydrogen production process to unstable solar power generation, strengthens the efficient and stable preparation of green hydrogen driven by fluctuating photovoltaic power, and the prepared hydrogen is transported to the hydrogen energy storage unit, thereby realizing the cross-regional transmission and utilization of renewable energy;

[0009] The photovoltaic hybrid hydrogen production system also includes a power supply system, which transmits direct current or alternating current to the electrolytic hydrogen production unit.

[0010] In the above photovoltaic hybrid hydrogen production system, the electrolytic hydrogen production unit optimizes the hydrogen production coupling of the PEM electrolyzer and the ALK electrolyzer to improve the hydrogen production capacity. The optimization method adopts an improved NSGA-Ⅱ algorithm based on a genetic algorithm, which includes:

[0011] Starting from the "start" node, an initial population is randomly generated, which is marked as the first generation Gen=1. At this stage, the decision variables involved include electrolyzers, hydrogen storage tanks, and fuel cells, which form the basis of the initial population;

[0012] Perform non-dominated sorting on the initial population and select a portion of individuals as the parent population. These parent individuals will participate in the subsequent reproduction and evolution process.

[0013] The generated offspring population will be combined with the parent population and non-dominated sorted;

[0014] Determine the parent population for the next generation through combination and sorting;

[0015] When selecting fit individuals, dominance relationships and crowding are used to ensure population diversity and evolutionary stability;

[0016] During each iteration, it is checked whether the preset maximum number of iterations has been reached. If not, the process returns to a new round of offspring generation and selection; if it has been reached, the program flow ends, marking the completion of the optimization process.

[0017] In the photovoltaic hybrid hydrogen production system described above, the photovoltaic power generation system uses an engineering model to simplify its model in order to improve the working efficiency and dynamic response speed of simulation and simulation, that is, on the basis of the theoretical model, two assumptions are established: one is that the series resistance is negligible; the other is that the parallel resistance is very large;

[0018] Combined with the parameters of solar panels, the engineering model of solar cells is as follows:

[0019]

[0020] Where I is the output current of the photovoltaic cell; U is the output voltage of the photovoltaic cell; U m is the working voltage at maximum power under reference conditions; I m is the working current at maximum power under reference conditions; I sC is the short-circuit current under reference conditions; U OC is the open circuit voltage under reference conditions; C1 and C2 are photovoltaic cell parameters affected by temperature and light intensity.

[0021] The second technical solution to achieve the above-mentioned purpose is: a capacity configuration optimization method for the photovoltaic hybrid hydrogen production system as mentioned above, including photovoltaic hydrogen production system modeling and system capacity optimization model, and the process ranges from parameter input, capacity planning, operation optimization to final solution selection, wherein a system capacity optimization model is established to improve the dynamic energy conversion characteristics of the system electrolysis hydrogen production, and the capacity configuration of the ALK electrolyzer, PEM electrolyzer and hydrogen storage tank is used as the optimization decision variable, and the levelized hydrogen production cost and the abandoned light rate are used as the objective function of the capacity configuration optimization to minimize the two.

[0022] In the above-mentioned capacity configuration optimization method of the photovoltaic hybrid hydrogen production system, the system capacity optimization configuration is based on the objective function and constraints of the system to improve the dynamic efficiency of energy conversion in the electrolysis process, including:

[0023] Step 1: Determine the objective function and decision variables

[0024] Taking ALK, PEM and the rated capacity of hydrogen storage tank as decision variables, the levelized hydrogen production cost f1 of the system and the abandoned solar power rate R aban As the objective function of capacity optimization, the two can be minimized as shown in the following formula:

[0025]

[0026] In the formula, f1 and f2 are the levelized hydrogen production costs of the system; R aban is the system abandonment rate; η sys is the photovoltaic utilization rate; P EL is the total hydrogen production of the electrolyzer; P PV For photovoltaic output; P grid is the amount of electricity purchased from the power grid; LCOH is the levelized cost of hydrogen production;

[0027] Step 2: Constraints

[0028] 1) Decision variables

[0029] After determining the optimization objective function and decision variables, we start to set boundaries for these decision parameters. The total scale of the electrolysis equipment must be controlled below the peak output of solar energy. First, we need to determine the constraint range of the decision variables. The total capacity of the electrolyzer should be lower than the maximum value of the photovoltaic output and the power grid purchase; the capacity of the hydrogen storage tank should be higher than the maximum difference between the hydrogen production and the hydrogen demand, as shown in equations (15)-(16):

[0030]

[0031] E EL =E ALK +E PEM (16)

[0032] Where P ALK,r , P PEM,r 、E HT，r Respectively represent the rated capacity of ALK, PEM and hydrogen storage tank; P PV For photovoltaic output; P grid E is the amount of electricity purchased from the power grid; EL is the hydrogen production of the electrolyzer; E load For hydrogen energy demand; E ALK 、E PEM are the hydrogen production of ALK and PEM respectively;

[0033] 2) Energy balance constraints

[0034] The photovoltaic output at any time is always greater than or equal to the power load of the electrolyzer; the hydrogen production of the electrolyzer and the hydrogen discharge of the hydrogen storage tank should be equal to the hydrogen filling amount of the hydrogen storage tank and the hydrogen load demand; as shown in equations (17) and (18):

[0035] P PV +P grid ≥P ALK +P PEM (17)

[0036] E EL +E HT，dis ≥E HT，ch+E load (18)

[0037] Where P ALK is the electrical load of alkaline hydrogen production; P PEM is the electrical load of PEM hydrogen production; E EL is the hydrogen production of the electrolyzer; E HT，dis is the amount of hydrogen released from the hydrogen storage tank; E HT，ch The hydrogen filling amount of the hydrogen storage tank.

[0038] In the above-mentioned capacity configuration optimization method of the photovoltaic hybrid hydrogen production system, the photovoltaic hydrogen production optimization strategy is based on an improved NSGA-II algorithm of a genetic algorithm, including:

[0039] First, the node starts to start up and randomly generates an initial population, which is marked as the first generation Gen=1. At this stage, the decision variables involved include electrolyzers, hydrogen storage tanks, and fuel cells, which form the basis of the initial population.

[0040] Next, the initial population is sorted non-dominatedly, and a portion of individuals are selected as the parent population. These parent individuals will participate in the subsequent reproduction and evolution process;

[0041] The generated offspring population will be combined with the parent population and non-dominated sorted;

[0042] Determine the parent population for the next generation through combination and sorting;

[0043] When selecting fit individuals, dominance relationships and crowding are used to ensure population diversity and evolutionary stability;

[0044] During each iteration, a check is performed to see whether the preset maximum number of iterations has been reached. If not, the process returns to a new round of offspring generation and selection; if it has been reached, the program flow ends, marking the completion of the optimization process.

[0045] In the above-mentioned capacity configuration optimization method of the photovoltaic hybrid hydrogen production system, the photovoltaic hydrogen production optimization strategy includes three key genetic operations: binary tournament selection, binary crossover and polynomial mutation.

[0046] In the above-mentioned capacity configuration optimization method for the photovoltaic hybrid hydrogen production system, in the binary tournament selection, two individuals are randomly selected from the population for comparison, and the individual with higher fitness is selected as one of the parents;

[0047] Binary crossover is a genetic operation that generates new individuals, i.e. offspring, by crossing the genes of the parent generation to promote the diversity and fitness of the population;

[0048] Polynomial mutation is used to introduce genetic mutations, helping the population avoid local optimal solutions and explore a wider solution space.

[0049] In the above-mentioned capacity configuration optimization method of the photovoltaic hybrid hydrogen production system, it includes the following steps:

[0050] First, starting from the node, it enters the parameter input stage and receives the initial parameter information including the alternative capacity and energy demand;

[0051] Next, we enter the capacity planning stage. Based on the energy balance constraint and objective function 1, we formulate a preliminary capacity configuration plan. This stage outputs a capacity configuration plan set M1, M2, ..., Mn for subsequent optimization.

[0052] After obtaining the capacity configuration scheme set, the process enters the operation optimization stage. At this time, the optimization is performed through objective function 2, combining the conditions of maximum power constraint and energy storage constraint to optimize the operation of each capacity configuration scheme and output the operation results.

[0053] Next, these operating results are analyzed to determine whether there is an optimal capacity configuration solution;

[0054] If there is an optimal capacity configuration solution, the process enters the stage of maximizing system reliability and profit, and makes a final evaluation of the system reliability and profit to ensure that the economy and reliability of system operation meet the expected goals; if all conditions are met, the optimal capacity configuration solution is finally selected and the process ends;

[0055] If a suitable capacity configuration solution cannot be found during the optimization process, the process will return to the initial stage, readjust the parameters and capacity configuration, and conduct a new round of optimization iterations.

[0056] In the above-mentioned capacity configuration optimization method of the photovoltaic hybrid hydrogen production system, the objective function 1 is the hydrogen production cost and the energy loss rate; the objective function 2 is the daily operating profit.

[0057] The beneficial effects of the present invention are as follows: in view of the adverse effects of the randomness and volatility of existing solar energy resources on the dynamic operation of hydrogen electrolysis, the poor economic efficiency of hydrogen electrolysis by PEM and the low efficiency of hydrogen electrolysis by ALK and the great influence of photovoltaic fluctuations, the present invention proposes a photovoltaic hydrogen production system that considers the synergy of PEM-ALK technology, and optimizes the capacity and operation of the system. The system consists of a renewable energy power generation unit, a hydrogen energy storage unit and an electrolysis hydrogen production unit. Among them, the power generation unit outputs electrical energy with the help of a photovoltaic array; the water electrolysis hydrogen production unit uses ALK and PEM, and through synergistic coupling, enhances the adaptability of the hydrogen production process to unstable solar power generation, and strengthens the efficient and stable preparation of green hydrogen driven by fluctuating photovoltaic power. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a schematic diagram of the photovoltaic hydrogen production system of the present invention;

[0059] Figure 2 It is the flow chart of the improved NSGA-Ⅱ algorithm in the present invention;

[0060] Figure 3 It is a double-layer optimization flow chart of the photovoltaic hydrogen production system of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described below in conjunction with the accompanying drawings.

[0062] See also Figure 1 In view of the adverse effects of the randomness and volatility of existing solar energy resources on the dynamic operation of hydrogen electrolysis, and the problems of poor economy of PEM electrolysis hydrogen production and low efficiency of ALK electrolysis hydrogen production and great influence of photovoltaic fluctuations, the present invention proposes a photovoltaic hydrogen production system considering the synergy of PEM-ALK technology, and optimizes the capacity configuration and operation of the system.

[0063] The photovoltaic hybrid hydrogen production system consists of a renewable energy power generation unit, a hydrogen energy storage unit and an electrolytic hydrogen production unit. The power generation unit uses a photovoltaic array to output electricity; the water electrolysis hydrogen production unit uses ALK and PEM, and through synergistic coupling, it enhances the adaptability of the hydrogen production process to unstable solar power generation and strengthens the efficient and stable production of green hydrogen driven by fluctuating photovoltaic power. Figure 1 shown.

[0064] ALK electrolyzers have high requirements for power quality, while PEM electrolyzers have high costs, about three times that of ALK electrolyzers. In addition, ALK electrolyzers are relatively weak in power regulation range and dynamic response speed, while PEM electrolyzers have better matching characteristics with renewable energy electricity. Therefore, this application considers combining these two types of electrolyzers for research, and the two are combined to produce hydrogen to form an electrolytic hydrogen production unit to give full play to their respective advantages. By determining the optimal ratio of the two in the system, the economy of the entire hydrogen production system is improved. The prepared hydrogen can be transported to the user end or stored in hydrogen storage tanks through technologies such as high-pressure storage, thereby realizing the cross-regional transmission and utilization of renewable energy.

[0065] Therefore, the present application also provides a process for the photovoltaic hydrogen production system considering the synergy of PEM-ALK technology. The process can be divided into two parts, namely photovoltaic hydrogen production system modeling and system capacity optimization model. Among them, a system capacity optimization model is established to further improve the dynamic energy conversion characteristics of the system electrolysis hydrogen production. In order to comprehensively consider the economy and resource utilization efficiency of the system, the capacity configuration of ALK electrolyzer, PEM electrolyzer and hydrogen storage tank is used as the optimization decision variable, and the levelized cost of hydrogen production (LCOH) and the abandoned light rate are used as the objective functions of capacity configuration optimization to minimize both.

[0066] Photovoltaic power generation system model

[0067] Since the parameters in the theoretical model change with environmental conditions, it is not conducive to simulation calculations. Within the scope of engineering application accuracy, people have proposed using engineering models to simplify the model to improve the efficiency and dynamic response speed of simulation and simulation. Based on the theoretical model, two assumptions are established:

[0068] 1. Series resistance is negligible;

[0069] 2. The parallel resistance is very large.

[0070] Combined with the parameters provided by the solar panel manufacturer, the engineering model of the solar cell is proposed as follows:

[0071]

[0072] Among them, I is the output current of the photovoltaic cell, U is the output voltage of the photovoltaic cell, and U m is the working voltage at maximum power under reference conditions, I n is the operating current at maximum power under reference conditions, I SC is the short-circuit current under reference conditions.

[0073] Levelized Unit Cost Model

[0074] In order to comprehensively evaluate the economic feasibility of projects of different scales, technologies and ratios during their life cycle, the levelized cost of hydrogen production (LCOH) and its calculation method are used to provide standardized evaluation indicators for the full life cycle quantification of hydrogen production costs. Based on factors such as initial investment, operating costs, taxes, and residual value of assets, the income tax on the benefits of hydrogen sales is considered, and oxygen is treated as being directly discharged into the air, without involving civil engineering and installation costs.

[0075] The daily levelized hydrogen production cost of the hydrogen production system is given by formula (11):

[0076]

[0077] In formula (11), COST total is the daily production cost, Q total is the total amount of hydrogen produced per day. total The factors including initial investment, operating costs, taxes, and residual value of assets are as follows:

[0078]

[0079] In formula (12), I in Initial investment of electrolysis system; assuming the system life cycle is 10 years; C o is the cost of running the system for one day; br is the daily discount rate; TX d V is the income tax on the dth day of hydrogen sales by the system; r is the estimated residual value recovery rate of the electrolysis system.

[0080] C o Considering the costs of electricity, water and system maintenance, the calculation formula is as follows:

[0081] C o =P pve *N pv，d +P e *N p，d +P W *Q d +C othe，d +C o&m，d (13)

[0082] In formula (13), N pv，d is the photovoltaic power generation consumed by the system for hydrogen production on day d; P pve is the price of photovoltaic power generation; N p,d The amount of electricity purchased from the power grid for hydrogen production by the system on day d; P e is the grid electricity price; Q d and P w is the water consumed and unit price for producing hydrogen by electrolysis of water every day; C other,d The cost of other materials consumed by water electrolysis to produce hydrogen every day; C o&m，d It is only the daily operation and maintenance cost of the electrolysis system.

[0083] System capacity configuration optimization

[0084] The photovoltaic hydrogen production system integrates two different types of hydrogen production equipment, aiming to give full play to its unique advantages in dynamic response and economic operation. A system capacity optimization model is established to further improve the dynamic energy conversion characteristics of the system's electrolytic hydrogen production. In addition, the system will further improve the dynamic efficiency of energy conversion during the electrolysis process based on the objective function and constraints.

[0085] (1) Objective function and decision variables

[0086] Taking ALK, PEM and the rated capacity of hydrogen storage tank as decision variables, the levelized hydrogen production cost f1 of the system and the abandoned solar power rate R a a As the objective function of capacity optimization, the two can be minimized as shown in the following formula:

[0087]

[0088] Where η sys is the photovoltaic utilization rate; η ALK , η PEM are the electrolysis hydrogen production efficiencies of ALK and PEM respectively; P EL is the total hydrogen production of the electrolyzer; P PV For photovoltaic output; P grid The amount of electricity purchased from the power grid.

[0089] (2) Constraints

[0090] ①Decision variables

[0091] After determining the optimization objective function and decision variables, we start to set boundaries for these decision parameters. The total scale of the electrolysis equipment must be controlled below the peak output of solar energy. First, we need to determine the constraint range of the decision variables. The total capacity of the electrolyzer should be lower than the maximum value of the photovoltaic output and the power grid purchase; the capacity of the hydrogen storage tank should be higher than the maximum value of the maximum difference between the hydrogen production and the hydrogen demand. As shown in equations (15)-(16):

[0092]

[0093] E EL =E ALK +E PEM (16)

[0094] Where P ALK,r , P PEM,r 、E HT,r Respectively represent the equipment rated capacity of ALK, PEM and hydrogen storage tank; E loa For hydrogen energy demand; E ALK 、E PEM They are the hydrogen production of ALK and PEM respectively.

[0095] ②Energy balance constraint

[0096] In the dynamic operation of the system, the principle of energy conservation must be adhered to, and the total power allocated to the electrolytic hydrogen production unit should not exceed the total capacity of solar power generation. The energy conservation constraint must be met during the dynamic operation of the system. Specifically, the photovoltaic output at any time is always greater than or equal to the power load of the electrolyzer; the hydrogen production of the electrolyzer and the hydrogen discharge of the hydrogen storage tank should be equal to the hydrogen storage tank's hydrogen charge and hydrogen load demand; as shown in equations (17)-(18).

[0097] P PV +P grid ≥P ALK +P PEM (17)

[0098] E EL +E HT，dis ≥E HT，ch +E load (18)

[0099] Where P ALK is the electrical load of alkaline hydrogen production; P PEM is the electrical load of PEM hydrogen production; E EL is the hydrogen production of the electrolyzer; E HT，dis is the amount of hydrogen released from the hydrogen storage tank; E HT，ch The hydrogen filling amount of the hydrogen storage tank.

[0100] Run optimization modeling

[0101] The inner model aims to maximize the daily operating profit of the electrolysis hydrogen production system and establishes an operation optimization model for the electrolysis hydrogen production system, in which the decision variable is the operating power of each device. Under the premise that the outer planning ensures the reliable operation of the electrolysis system, the operator of the electrolysis system aims to maximize the daily operating profit, which mainly includes hydrogen sales revenue, equipment operation and maintenance costs, and daily investment costs of the equipment.

[0102] In order to ensure the safe and efficient operation of the system, it is necessary to constrain the relevant boundary conditions of the system operation. In addition to satisfying the outer power balance constraints, the inner operation optimization also needs to satisfy the equipment maximum power constraints and the energy storage system operation constraints.

[0103] During the operation of the electrolytic hydrogen production system, considering the characteristics of the equipment and the safety and reliability requirements of the system, the power of the equipment must be maintained within a certain output power range.

[0104] Two-level optimization modeling

[0105] (1) Solution of the two-layer operation optimization model

[0106] The capacity optimization of photovoltaic hydrogen production systems is a multi-variable, multi-constraint, and multi-objective optimization problem. Since these objectives are nonlinear, contradictory, and immeasurable, the global optimal solution of the multiple objectives is usually obtained through the Pareto optimal solution set. According to the characteristics of the model, this application proposes a two-layer optimization model. The upper-level capacity planning of this model is a typical complex multi-objective optimization problem with the levelized hydrogen production cost and energy loss rate as the targets. According to the characteristics of the model, this application intends to use an improved non-inferior classification genetic algorithm (NSGA-II) to solve the model. The lower-level operation optimization of the model is a typical MILP model, which can be directly solved using the MILP toolbox. Figure 2 To improve the NSGA-Ⅱ algorithm flow chart.

[0107] Figure 2 The complete process of photovoltaic hydrogen production optimization strategy based on the improved NSGA-Ⅱ algorithm of genetic algorithm is shown. First, the whole optimization process starts from the "start" node and randomly generates the initial population, which is marked as the first generation (Gen=1). At this stage, the decision variables involved include electrolyzers, hydrogen storage tanks and fuel cells, which form the basis of the initial population.

[0108] Next, the initial population is sorted non-dominatedly, and a portion of individuals are selected as the parent population. These parent individuals will participate in the subsequent reproduction and evolution process. For optimization, the process applies the photovoltaic hydrogen production optimization strategy, which includes three key genetic operations: binary tournament selection, binary crossover, and polynomial mutation. In the binary tournament selection, two individuals are randomly selected from the population for comparison, and the individual with higher fitness is selected as one of the parents. Binary crossover is a genetic operation that generates new individuals (offspring) by crossing the genes of the parent to promote the diversity and fitness of the population. Polynomial mutation is used to introduce gene mutations to help the population avoid local optimal solutions and explore a wider solution space. The generated offspring population will be combined with the parent population and sorted non-dominatedly. Through this combination and sorting, the parent population of the next generation is determined. When selecting suitable individuals, the dominance relationship and crowding degree are used to ensure the diversity and evolutionary stability of the population. During each iteration, it is checked whether the preset maximum number of iterations has been reached. If it has not been reached, the process returns to a new round of offspring generation and selection; if it has been reached, the program flow ends, marking the completion of the optimization process.

[0109] The entire block diagram reflects a typical iterative process of genetic algorithms: the population gradually approaches the optimal solution through continuous selection, crossover and mutation. By combining the photovoltaic hydrogen production optimization strategy, it can effectively Figure 1 The PEM and ALK hydrogen production coupling system shown is optimized to improve the hydrogen production capacity. This process also provides powerful tools and methodological support for the development of photovoltaic hydrogen production technology.

[0110] (2) Two-layer optimization process of photovoltaic hydrogen production system

[0111] This application uses a two-layer optimization method to optimize the capacity of the photovoltaic hydrogen production system. The upper layer is the planning optimization stage, which optimizes at the planning level to obtain a set of multiple equipment configuration schemes for the system; the lower layer is the operation optimization stage, which optimizes at the operation level to obtain the optimal operating conditions of the system under multiple planning schemes. Figure 3 Double-layer optimization flow chart for photovoltaic hydrogen production system.

[0112] This flowchart shows a comprehensive process for energy system capacity configuration and optimization. First, starting from the "Start" node, enter the "Parameter Input" stage to receive initial parameter information such as alternative capacity and energy demand. Then, enter the "Capacity Planning" stage to formulate a preliminary capacity configuration plan based on the energy balance constraint and objective function 1 (hydrogen production cost, energy loss rate). This stage outputs a capacity configuration plan set M1, M2, ..., Mn for subsequent optimization.

[0113] After obtaining the capacity configuration scheme set, the process enters the "operation optimization" stage. At this time, through the optimization of objective function 2 (daily operating profit), combined with the maximum power constraint and energy storage constraint and other conditions, the operation of each capacity configuration scheme is optimized and the operation results are output. Then, these operation results are analyzed to determine whether there is an optimal capacity configuration scheme.

[0114] If there is an optimal capacity configuration solution, the process enters the "system reliability and maximum profit" stage, and the system reliability and profit are finally evaluated to ensure that the economy and reliability of the system operation meet the expected goals. If all conditions are met, the "optimal capacity configuration solution" is finally selected and the process ends. If a suitable capacity configuration solution is not found during the optimization process, the process will return to the initial stage, readjust the parameters and capacity configuration, and conduct a new round of optimization iterations.

[0115] The entire process, from parameter input, capacity planning, operation optimization to final solution selection, reflects Figure 1 The systematic and scientific configuration and optimization of the capacity of the PEM and ALK hydrogen production coupling system shown ensures that the dual goals of economic benefits and system reliability are achieved while meeting the hydrogen production demand.

[0116] The purpose of the system operation phase is to obtain the optimal operating conditions of the system under multiple capacity configuration schemes. In the operation optimization phase, the operation optimization takes the maximum power constraint and energy storage capacity status as constraints and the maximum daily profit as the optimization goal. The operation optimization is performed on the multiple configuration schemes selected in the capacity planning phase to obtain the operation results of the system's multiple planning schemes.

[0117] According to the operation results of multiple groups of system capacity configuration schemes obtained in the operation optimization phase, the system capacity configuration scheme is selected. According to the daily operation profit of each scheme in the capacity planning phase, energy supply reliability and other decision-making factors, the upper-level planning schemes are compared and selected, and the optimal capacity configuration scheme that maximizes the system economic benefits and energy utilization is finally determined, and the capacity and operating power of each device in the system are output.

[0118] The specific steps of the implementation example of the method provided by the present invention are as follows: Figure 1 As shown, a photovoltaic hydrogen production system is established, the system includes a photovoltaic power generation system, the power supply system transmits direct current or alternating current to the hydrogen production system, and the hydrogen production system transmits hydrogen to the hydrogen storage and transportation system. The hydrogen production unit in the hydrogen production system is an electrolyzer, including a proton exchange membrane electrolyzer and an alkaline electrolyzer, and the power supply system, hydrogen storage, and hydrogen transportation system in the photovoltaic hydrogen production system can all use existing technologies in the art.

[0119] The photovoltaic power generation method of the efficient water electrolysis hydrogen production device of the present invention includes developing a capacity optimization model, an operation optimization modeling and a double-layer optimization model to further improve the dynamic efficiency of energy conversion during the electrolysis process. The specific steps are as follows:

[0120] (1) System capacity optimization

[0121] 1) Objective function and decision variables

[0122] Taking ALK, PEM and the rated capacity of hydrogen storage tank as decision variables, the levelized hydrogen production cost f1 of the system and the abandoned solar power rate R aa As the objective function of capacity optimization, the two can be minimized as shown in the following formula:

[0123]

[0124] Where η sys is the photovoltaic utilization rate; η ALK , η PEM are the electrolysis hydrogen production efficiencies of ALK and PEM respectively; P EL is the total hydrogen production of the electrolyzer; P PV For photovoltaic output; P grid The amount of electricity purchased from the power grid.

[0125] 2) Constraints

[0126] ①Decision variables

[0127] After determining the optimization objective function and decision variables, we first need to determine the constraint range of the decision variables. The total capacity of the electrolyzer should be lower than the maximum value of the photovoltaic output and the power grid purchase; the capacity of the hydrogen storage tank should be higher than the maximum value of the maximum difference between the hydrogen production and the hydrogen demand, as shown in the following formula:

[0128]

[0129] E EL =E ALK +E PEM

[0130] Among them, P ALK,r , P PEM,r 、E HT,r Respectively represent the equipment rated capacity of ALK, PEM and hydrogen storage tank; E loa For hydrogen energy demand; E ALK 、E PEM They are the hydrogen production of ALK and PEM respectively.

[0131] ②Energy balance constraint

[0132] The energy conservation constraint must be met during the dynamic operation of the system. Specifically, the photovoltaic output at any time is always greater than or equal to the power load of the electrolyzer; the hydrogen production of the electrolyzer and the hydrogen release of the hydrogen storage tank should be equal to the hydrogen filling amount of the hydrogen storage tank and the hydrogen load demand, as shown in the following formula.

[0133] P PV +P grid ≥P ALK +P PEM

[0134] E EL +E HT，dis ≥E HT，ch +E load

[0135] Where P ALK is the electrical load of alkaline hydrogen production; P PEM is the electrical load of PEM hydrogen production; E EL is the hydrogen production of the electrolyzer; E HT，dis is the amount of hydrogen released from the hydrogen storage tank; E HT，ch The hydrogen filling amount of the hydrogen storage tank.

[0136] (3) Operation optimization modeling

[0137] The inner model aims to maximize the daily operating profit of the electrolysis hydrogen production system and establishes an operation optimization model for the electrolysis hydrogen production system, in which the decision variable is the operating power of each device. Under the premise that the outer planning ensures the reliable operation of the electrolysis system, the operator of the electrolysis system aims to maximize the daily operating profit, which mainly includes hydrogen sales revenue, equipment operation and maintenance costs, and daily investment costs of the equipment.

[0138]

[0139] In the formula, is the unit price of hydrogen; μ iniIt is the conversion factor of the initial investment cost of the equipment.

[0140] In order to ensure the safe and efficient operation of the system, it is necessary to constrain the relevant boundary conditions of the system operation. In addition to satisfying the outer power balance constraints, the inner operation optimization also needs to satisfy the equipment maximum power constraints and the energy storage system operation constraints.

[0141] ① Maximum power constraint

[0142] During the operation of the electrolytic hydrogen production system, considering the characteristics of the equipment and the safety and reliability requirements of the system, the power of the equipment must be maintained within a certain output power range.

[0143]

[0144] In the formula, They are the lower and upper limits of ALK operating power respectively; are the lower and upper limits of PEM operating power, respectively; They are respectively the lower and upper limits of the electric power transmitted by the upper power grid to the system.

[0145] ②Hydrogen storage tank operation constraints

[0146]

[0147] In the formula, They are the upper and lower limits of hydrogen filling in the hydrogen storage tank respectively; The upper and lower limits of hydrogen discharge from the hydrogen storage tank are respectively.

[0148] In summary, the present invention provides a photovoltaic hybrid hydrogen production system and a capacity configuration optimization method, which is composed of a renewable energy power generation unit, a hydrogen energy storage unit and an electrolytic hydrogen production unit. Among them, the power generation unit outputs electric energy with the help of a photovoltaic array; the water electrolysis hydrogen production unit uses ALK and PEM, and through synergistic coupling, enhances the adaptability of the hydrogen production process to unstable solar power generation, and strengthens the efficient and stable preparation of green hydrogen driven by fluctuating photovoltaic power. The ALK electrolyzer has high requirements for power quality, and the cost of the PEM electrolyzer is high, about 3 times that of the ALK electrolyzer. In addition, the ALK electrolyzer is relatively weak in power regulation range and dynamic response speed, while the PEM electrolyzer has better matching characteristics with renewable energy power. Therefore, this application considers combining these two types of electrolyzers for research, and the two are combined to produce hydrogen together to form an electrolytic hydrogen production unit to give full play to their respective advantages. By determining the optimal ratio of the two in the system, the economy of the entire hydrogen production system is improved. The prepared hydrogen can be transported to the user end or stored in a hydrogen storage tank through technologies such as high-pressure storage, thereby realizing the cross-regional transmission and utilization of renewable energy.

[0149] The present invention is described in detail above in conjunction with the embodiments of the accompanying drawings. A person skilled in the art can make various variations of the present invention according to the above description. Therefore, certain details in the embodiments should not constitute a limitation of the present invention, and the scope of protection of the present invention shall be defined by the scope of the attached claims.

Claims

1. A photovoltaic hybrid hydrogen production system, characterized in that: The system is composed of a renewable energy power generation unit, an electrolytic hydrogen production unit and a hydrogen energy storage unit connected in sequence, wherein the renewable energy power generation unit is a photovoltaic power generation system, which outputs electric energy with the help of a photovoltaic array; the electrolytic hydrogen production unit uses an ALK electrolyzer and a PEM electrolyzer in parallel, and through their synergistic coupling, enhances the adaptability of the hydrogen production process to unstable solar power generation, strengthens the efficient and stable preparation of green hydrogen driven by fluctuating photovoltaic power, and the prepared hydrogen is transported to the hydrogen energy storage unit, thereby realizing the cross-regional transmission and utilization of renewable energy; The photovoltaic hybrid hydrogen production system also includes a power supply system, which transmits direct current or alternating current to the electrolytic hydrogen production unit.

2. A photovoltaic hybrid hydrogen production system according to claim 1, characterized in that: In the electrolytic hydrogen production unit, the hydrogen production coupling of the PEM electrolyzer and the ALK electrolyzer is optimized to improve the hydrogen production capacity. The optimization method adopts an improved NSGA-Ⅱ algorithm based on a genetic algorithm, which includes: Starting from the "start" node, the initial population is randomly generated, which is marked as the first generation Gen=1. At this stage, the decision variables involved include electrolyzers, hydrogen storage tanks and fuel cells, which form the basis of the initial population; Perform non-dominated sorting on the initial population and select a portion of individuals as the parent population. These parent individuals will participate in the subsequent reproduction and evolution process. The generated offspring population will be combined with the parent population and non-dominated sorted; Determine the parent population of the next generation through combination and sorting; When selecting fit individuals, dominance relationships and crowding are used to ensure population diversity and evolutionary stability; During each iteration, it is checked whether the preset maximum number of iterations has been reached. If not, the process returns to a new round of offspring generation and selection; if it has been reached, the program flow ends, marking the completion of the optimization process.

3. A photovoltaic hybrid hydrogen production system according to claim 1 or 2, characterized in that: The photovoltaic power generation system uses an engineering model to simplify its model in order to improve the working efficiency and dynamic response speed of simulation and simulation, that is, based on the theoretical model, two assumptions are established: one is that the series resistance is negligible; the other is that the parallel resistance is very large; Combined with the parameters of solar panels, the engineering model of solar cells is as follows: Where I is the output current of the photovoltaic cell; U is the output voltage of the photovoltaic cell; U m is the working voltage at maximum power under reference conditions; I m is the working current at maximum power under reference conditions; I SC is the short-circuit current under reference conditions; U OC is the open circuit voltage under reference conditions; C1 and C2 are photovoltaic cell parameters affected by temperature and light intensity.

4. A capacity configuration optimization method for a photovoltaic hybrid hydrogen production system as claimed in claim 1, comprising photovoltaic hydrogen production system modeling and system capacity optimization modeling, and the process ranges from parameter input, capacity planning, operation optimization to final solution selection, wherein: A system capacity optimization model is established to improve the dynamic energy conversion characteristics of the system's electrolytic hydrogen production. The capacity configuration of ALK electrolyzer, PEM electrolyzer and hydrogen storage tank is used as the optimization decision variable, and the levelized hydrogen production cost and abandoned light rate are used as the objective functions of capacity configuration optimization to minimize the two.

5. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 4 is characterized in that: The system capacity optimization configuration is based on the objective function and constraints of the system to improve the dynamic efficiency of energy conversion in the electrolysis process, including: Step 1: Determine the objective function and decision variables Taking ALK, PEM and the rated capacity of hydrogen storage tank as decision variables, the levelized hydrogen production cost f1 of the system and the abandoned solar power rate R aban As the objective function of capacity optimization, the two can be minimized as shown in the following formula: In the formula, f1 and f2 are the levelized hydrogen production costs of the system; R aban is the system abandonment rate; η sys is the photovoltaic utilization rate; P EL is the total hydrogen production of the electrolyzer; P PV For photovoltaic output; P grid is the amount of electricity purchased from the power grid; LCOH is the levelized cost of hydrogen production; Step 2: Constraints 1) Decision variables After determining the optimization objective function and decision variables, we start to set boundaries for these decision parameters. The total scale of the electrolysis equipment must be controlled below the peak output of solar energy. First, we need to determine the constraint range of the decision variables. The total capacity of the electrolyzer should be lower than the maximum value of the photovoltaic output and the power grid purchase; the capacity of the hydrogen storage tank should be higher than the maximum difference between the hydrogen production and the hydrogen demand, as shown in equations (15)-(16): AND EL =And ALK +E PEM (16) Where P ALK,r , P PEM,r 、E HT,r Represent the rated capacity of ALK, PEM and hydrogen storage tank respectively; P PV For photovoltaic output; P grid E is the amount of electricity purchased from the power grid; EL is the hydrogen production of the electrolyzer; E load For hydrogen energy demand; E ALK 、E PEM are the hydrogen production of ALK and PEM respectively; 2) Energy balance constraints The photovoltaic output at any time is always greater than or equal to the power load of the electrolyzer; the hydrogen production of the electrolyzer and the hydrogen discharge of the hydrogen storage tank should be equal to the hydrogen filling amount of the hydrogen storage tank and the hydrogen load demand; as shown in equations (17) and (18): P PV +P grid ≥P ALK +P PEM (17) AND EL +E HT,dis ≥E HT,ch +E load (18) Where P ALK is the electrical load of alkaline hydrogen production; P PEM is the electrical load of PEM hydrogen production; E EL is the hydrogen production of the electrolyzer; E HT,dis is the amount of hydrogen released from the hydrogen storage tank; E HT,ch The hydrogen filling amount of the hydrogen storage tank.

6. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 4 is characterized in that: The photovoltaic hydrogen production optimization strategy is based on an improved NSGA-Ⅱ algorithm of a genetic algorithm, including: First, the node starts to start up and randomly generates an initial population, which is marked as the first generation Gen=1. At this stage, the decision variables involved include electrolyzers, hydrogen storage tanks, and fuel cells, which form the basis of the initial population. Next, the initial population is sorted non-dominatedly, and a portion of individuals are selected as the parent population. These parent individuals will participate in the subsequent reproduction and evolution process; The generated offspring population will be combined with the parent population and non-dominated sorted; Determine the parent population of the next generation through combination and sorting; When selecting fit individuals, dominance relationships and crowding are used to ensure population diversity and evolutionary stability; During each iteration, a check is performed to see whether the preset maximum number of iterations has been reached. If not, the process returns to a new round of offspring generation and selection; if it has been reached, the program flow ends, marking the completion of the optimization process.

7. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 6 is characterized in that: The proposed photovoltaic hydrogen production optimization strategy includes three key genetic operations: binary tournament selection, binary crossover, and polynomial mutation.

8. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 7 is characterized in that: In binary tournament selection, two individuals are randomly selected from the population for comparison, and the individual with the higher fitness is chosen as one of the parents; Binary crossover is a genetic operation that generates new individuals, i.e. offspring, by crossing the genes of the parent generation to promote the diversity and fitness of the population; Polynomial mutation is used to introduce genetic mutations, helping the population avoid local optimal solutions and explore a wider solution space.

9. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 4, comprising the following steps: First, starting from the node, it enters the parameter input stage and receives the initial parameter information including the alternative capacity and energy demand; Next, we enter the capacity planning stage. Based on the energy balance constraint and objective function 1, we formulate a preliminary capacity configuration plan. This stage outputs a capacity configuration plan set M1, M2, ..., Mn for subsequent optimization. After obtaining the capacity configuration scheme set, the process enters the operation optimization stage. At this time, the optimization is performed through objective function 2, combining the conditions of maximum power constraint and energy storage constraint to optimize the operation of each capacity configuration scheme and output the operation results. Next, these operating results are analyzed to determine whether there is an optimal capacity configuration solution; If there is an optimal capacity configuration solution, the process enters the stage of maximizing system reliability and profit, and makes a final evaluation of the system reliability and profit to ensure that the economy and reliability of system operation meet the expected goals; if all conditions are met, the optimal capacity configuration solution is finally selected and the process ends; If a suitable capacity configuration solution cannot be found during the optimization process, the process will return to the initial stage to readjust the parameters and capacity configuration and conduct a new round of optimization iterations.

10. The capacity configuration optimization method of the photovoltaic hybrid hydrogen production system according to claim 9, characterized in that: The objective function 1 is the hydrogen production cost and energy loss rate; the objective function 2 is the daily operating profit.

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