Photovoltaic hydrogen production system capacity configuration optimization method based on optical power prediction

Abstract

The invention discloses a photovoltaic hydrogen production system capacity configuration optimization method based on optical power prediction. The method comprises the following steps: building a photovoltaic hydrogen production system; establishing an energy management strategy of the photovoltaic hydrogen production system by utilizing the predicted input power of the photovoltaic power generation subsystem in a future period of time; the total hydrogen production volume, the unit hydrogen production cost and the hydrogen production power fluctuation ratio limit value Kfm serve as target parameters, the lowest unit hydrogen production cost and the highest total hydrogen production volume of the system under the optimal condition are determined, normalization processing is conducted on all the target parameters, and a photovoltaic hydrogen production system capacity configuration optimization model is constructed; setting constraint conditions of an objective function of the capacity configuration optimization model; and solving the target function by using a particle swarm algorithm to obtain an optimal hydrogen production power fluctuation ratio limit value, an optimal electrolytic cell capacity and an optimal storage battery capacity. According to the invention, the capacity configuration optimization of the photovoltaic hydrogen production system is completed, and a result with optimal comprehensive performance is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy hydrogen production, and in particular to a method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction. Background Art

[0002] Photovoltaic hydrogen production can not only effectively utilize solar energy, an inexhaustible renewable energy source, but its product hydrogen, as a high-energy-density energy source, is clean, efficient, and pollution-free, and has broad application prospects in transportation, electricity, and industry. The performance and economy of photovoltaic hydrogen production systems are significantly affected by their capacity configuration. Capacity configuration optimization is not only the key to improving system energy efficiency and reducing costs, but also the basis for ensuring stable system operation and meeting market demand. With the continuous advancement of photovoltaic technology and water electrolysis technology, photovoltaic hydrogen production systems have gradually demonstrated their great potential in practical applications. However, how to reasonably configure the capacity of key components such as photovoltaic arrays, water electrolysis equipment, and energy storage systems to optimize the overall performance of the system has become an industry research hotspot.

[0003] The technology of producing hydrogen by electrolysis of water is the core technology in large-scale hydrogen production from renewable energy sources, including hydrogen production by alkaline electrolysis cells (AKL), proton exchange membrane electrolyzers (PEM) and solid oxide electrolyzers (SOEC). Among them, proton exchange membrane electrolyzers and solid oxide electrolyzers have outstanding advantages because they can achieve a larger current density and a higher current efficiency (more than 90%) during operation. However, their material costs are relatively high, and the solid oxide electrolyzer (SOEC) uses high-temperature electrolysis, which will accelerate the aging of the equipment. Therefore, they have not yet been widely used in the market. Alkaline electrolyzers are relatively low in cost and can flexibly and quickly respond to the random volatility of renewable energy output. The dynamic response time is at the ms to s level, and they are the mainstream P2H equipment on the market.

[0004] The core of capacity configuration optimization of renewable energy hydrogen production system lies in finding a balance, that is, maximizing the use of renewable energy, reducing energy waste, and reducing the investment cost and operating cost of the system while meeting energy demand. At present, the capacity configuration optimization method in the existing technology has the following problems:

[0005] First, the use of photovoltaic prediction data is relatively small, and the energy management strategy is more often to adjust the hydrogen production power to the safe operating range of the electrolyzer;

[0006] Second, the calculation is based on hourly data, and the modeling calculation is relatively rough;

[0007] Third, price fluctuations of commercial electricity are not fully taken into account. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention proposes a capacity configuration optimization method for a photovoltaic hydrogen production system based on light power prediction. In the process of capacity configuration, the ultra-short-term light power prediction technology is considered to smooth power fluctuations, realize the reasonable configuration of the installed capacity of photovoltaic modules, batteries, alkaline electrolyzers and compressors, and achieve the optimal operation state of the system.

[0009] To achieve the above object, the present invention designs a method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction, which is particularly characterized in that it includes the following steps:

[0010] S1) constructing a photovoltaic hydrogen production system, the system comprising a photovoltaic power generation subsystem, a battery energy storage subsystem, an alkaline electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, each of which is connected to a DC bus;

[0011] S2) Establishing energy management strategy for photovoltaic hydrogen production system;

[0012] S21) using the predicted input power of the photovoltaic power generation system in the future to adjust the output power of the photovoltaic power generation system at the current moment, thereby reducing the overall power volatility of the photovoltaic power generation system; the output power of the photovoltaic power generation system at the current moment is adjusted by the following formula

[0013]

[0014] In the formula,

[0015] P pv,st is the target value of the output power of the photovoltaic power generation system at time t,

[0016] P pv,t is the output power of the photovoltaic power generation system at time t,

[0017] P pv,t+1 ~P pv,t+8 are the predicted input power of the photovoltaic power generation system at time t+1 to t+8,

[0018] w1~w8 are weight coefficients;

[0019] S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem by adjusting the output power target value of the photovoltaic power generation subsystem;

[0020] S23) Setting the hydrogen production power fluctuation rate limit K fm , when -K fm ≤Real-time fluctuation rate of photovoltaic power generation system≤K fmWhen the power of the battery is adjusted, the power of the hydrogen production of the alkaline electrolyzer is stabilized, thereby determining the final power of the hydrogen production of the alkaline electrolyzer;

[0021] S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit K fm As target parameters, the lowest unit hydrogen production cost and the highest total hydrogen production volume of the system under the optimal conditions are determined, each target parameter is normalized, and a capacity configuration optimization model of the photovoltaic hydrogen production system is constructed. The objective function of the capacity configuration optimization model is as follows:

[0022]

[0023] In the formula,

[0024] M is the objective function value,

[0025] C h2,min For the lowest unit hydrogen production cost,

[0026] C h2 is the unit hydrogen production cost,

[0027] V h2 is the total hydrogen production volume,

[0028] V h2,max is the maximum total hydrogen production volume,

[0029] K fm is the volatility limit,

[0030] w1~w3 are weight coefficients respectively;

[0031] S4) setting constraints of the objective function of the capacity configuration optimization model, wherein the constraints include system power balance constraints, grid power constraints, battery power constraints, and battery capacity constraints;

[0032] S5) Under the constraints of step S4), the objective function of step S3) is solved using a particle swarm algorithm to obtain an optimal hydrogen production power fluctuation rate limit, an optimal electrolyzer capacity, and an optimal battery capacity.

[0033] Further, in S22), the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula:

[0034]

[0035] In the formula,

[0036] K_fluc is the real-time fluctuation rate of the photovoltaic power generation system,

[0037] P pv,stis the regulation target value of the output power of the PV power generation subsystem at time t,

[0038] P pv,t is the output power of the PV power generation subsystem at time t.

[0039] Furthermore, in S23), when -K fm ≤K_fluc≤K fm the specific strategy for battery power regulation is as follows: when the battery SOC > bat_u, the battery discharges to increase the hydrogen production power of the alkaline electrolyzer; when the battery SOC < bat_i, the battery charges to reduce the hydrogen production power of the alkaline electrolyzer; when bat_i ≤ battery SOC ≤ bat_u, no operation is performed; where bat_u is the upper limit of battery power regulation and bat_i is the lower limit of battery power regulation.

[0040] Furthermore, in S23), when K_fluc > K fm if the battery capacity margin and power are sufficient, the battery charges until K_fluc = K fm ; if the battery capacity margin and power are insufficient, the excess PV power is curtailed until K_fluc = K fm .

[0041] Furthermore, in S23), when K_fluc < -K fm if the battery capacity and power are sufficient, the battery discharges until K_fluc = -K fm ; if the battery capacity and power are insufficient, the battery discharges or power is purchased from the grid as supplementary power regulation until K_fluc = -K fm .

[0042] Further, in S3), the unit hydrogen production cost C h2 is solved by the following formula

[0043]

[0044]

[0045] C cap = C pv + C el + C bat + C compre

[0046] C om = C om_pv + C om_el + C om_bat + C om_compre + C h2o + C gridIn the formula,

[0047] C h2 is the unit hydrogen production cost,

[0048] C sys is the total discounted cost of the system during the project operation period,

[0049] ∑ is the sum,

[0050] V h2,t is the hydrogen production volume at time t,

[0051] C cap(i) is the equipment investment cost in year i,

[0052] C om(i) is the operation and maintenance cost in the i-th year,

[0053] r is the discount rate,

[0054] Y i is the ordinal number of the year of capital investment,

[0055] C om is the investment cost of the equipment,

[0056] C pv is the investment cost of PV modules,

[0057] C el is the investment cost of the electrolytic cell,

[0058] C bat is the investment cost of the battery,

[0059] C compre is the investment cost of the compressor,

[0060] C om The operation and maintenance cost of the equipment.

[0061] C om_pv is the operation and maintenance cost of PV panels,

[0062] C om_el is the operation and maintenance cost of the electrolytic cell,

[0063] C om_bat is the operation and maintenance cost of the battery,

[0064] C om_compre The electricity cost and maintenance cost for the operation of the compressor,

[0065] C h2o The water consumption cost of the system operation is

[0066] C grid The cost of purchasing electricity to supplement power to the grid.

[0067] Further, in S4), the system power balance constraint is

[0068]

[0069] In the formula,

[0070] P pv is the output power of the photovoltaic power generation system,

[0071] P grid is the grid power,

[0072] P j2 (i) is the hydrogen production power of the alkaline electrolyzer hydrogen production subsystem in year i,

[0073] P bat is the battery power.

[0074] Furthermore, in S4), the power grid constraint condition is

[0075]

[0076] In the formula,

[0077] P grid is the grid power,

[0078] is the maximum grid power.

[0079] Furthermore, in S4), the battery power constraint condition is

[0080]

[0081] In the formula,

[0082] P bat is the battery power,

[0083] is the maximum battery power.

[0084] Furthermore, in S4), the battery capacity constraint condition;

[0085] 0≤SOC≤1

[0086] In the formula,

[0087] SOC is the battery limit capacity.

[0088] The advantages of the present invention are:

[0089] 1. The present invention adjusts the photovoltaic output power at the current time node in real time based on the subsequent photovoltaic power change trend, and cooperates with the power adjustment of the battery and the distribution network to achieve the purpose of suppressing the volatility of hydrogen power, making the electrolyzer operation more stable, reducing the number of start-stop times of the electrolyzer, and extending the life of the electrolyzer to a certain extent;

[0090] 2. The present invention combines the energy management strategy of the system with the capacity configuration optimization method of the system. The allocation boundary determination of the system energy flow requires the determined capacity configuration information, and the determination of the capacity configuration information requires the hydrogen production power allocated according to the energy management strategy as a necessary process parameter. Therefore, the present invention considers the joint optimization of the volatility with photovoltaic modules, batteries, and electrolytic cells;

[0091] The present invention proposes a capacity configuration optimization method for a photovoltaic hydrogen production system based on light power prediction. During the capacity configuration process, ultra-short-term light power prediction technology is used to smooth power fluctuations, and 15-minute data is used for detailed modeling and calculation. The capacity configuration optimization of the photovoltaic hydrogen production system is completed, and the best overall performance result is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 is an energy management strategy diagram of the system in the present invention;

[0093] Figure 2 This is a flow chart of capacity configuration optimization in the present invention. DETAILED DESCRIPTION

[0094] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0095] The present invention provides a method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction, comprising the following steps:

[0096] S1) Building a photovoltaic hydrogen production system, the system includes a photovoltaic power generation subsystem, a battery energy storage subsystem, an alkaline electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, which are respectively connected to a DC bus.

[0097] S2) Establish an energy management strategy for the photovoltaic hydrogen production system. The specific steps are as follows.

[0098] S21) using the predicted input power of the photovoltaic power generation system in the future to adjust the output power of the photovoltaic power generation system at the current moment, thereby reducing the overall power volatility of the photovoltaic power generation system; the output power of the photovoltaic power generation system at the current moment is adjusted by the following formula

[0099]

[0100] In the formula,

[0101] P pv,st is the target value of the output power of the photovoltaic power generation system at time t,

[0102] P pv,t is the output power of the photovoltaic power generation system at time t,

[0103] P pv,t+1 ~P pv,t+8 They are the predicted input power of the photovoltaic power generation system at time t+1 to t+8, and the adjacent time interval is 15 minutes.

[0104] w1~w8 are all weight coefficients.

[0105] R pv,t+1 ~P pv,t+8 The ultra-short-term optical power prediction data for the last 8 time nodes, the ultra-short-term prediction accuracy based on cloud map information and other methods has fully met the requirements. In this embodiment, w1-w8 are 0.23, 0.20, 0.17, 0.14, 0.11, 0.08, 0.05, 0.02 respectively.

[0106] S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem through adjusting the target value of the output power of the photovoltaic power generation subsystem.

[0107] Specifically, the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula:

[0108]

[0109] In the formula,

[0110] K_fluc is the real-time fluctuation rate of the photovoltaic power generation system,

[0111] P pv,st is the target value of the output power of the photovoltaic power generation system at time t,

[0112] P pv,t is the output power of the photovoltaic power generation system at time t.

[0113] S23) Setting the hydrogen production power fluctuation rate limit K fmn , when -K fm ≤Real-time fluctuation rate of photovoltaic power generation system≤K fm When the battery is powered, the power of the battery is regulated to achieve the stabilization of the hydrogen production power of the alkaline electrolyzer, thereby determining the final hydrogen production power of the alkaline electrolyzer.

[0114] By setting the hydrogen production power fluctuation rate limit K fmTo determine the real-time hydrogen production power, in a photovoltaic hydrogen production system, the battery power and grid power are the main ways to regulate the hydrogen production power. The energy storage capacity of the battery is limited, and the output power of the photovoltaic system is weak during sunrise and sunset periods and strongest at noon. Therefore, it often occurs that the battery runs out of power during sunrise and sunset periods and is fully charged at noon, resulting in the waste of surplus optical power at noon. To achieve "peak shaving and valley filling" for the SOC state of the battery to a certain extent and improve the utilization rate of optical power, set bat_u as the upper limit of the battery power regulation and bat_i as the lower limit of the battery power regulation. The specific energy management strategy of the photovoltaic hydrogen production system is shown in Figure 1 as follows.

[0115] When -K fm ≤K_fluc≤K fm , the specific strategy for battery power regulation is: when the battery SOC > bat_u, the battery discharges to increase the hydrogen production power of the alkaline electrolyzer; when the battery SOC < bat_i, the battery charges to reduce the hydrogen production power of the alkaline electrolyzer; when bat_i ≤ battery SOC ≤ bat_u, no operation is performed;

[0116] When K_fluc > K fm , if the battery capacity margin and power are sufficient, the battery charges until K_fluc = K fm ; if the battery capacity margin and power are insufficient, the excess photovoltaic power generation is the curtailed power until K_fluc = K fm ;

[0117] When K_fluc < -K fm , if the battery capacity and power are sufficient, the battery discharges until K_fluc = -K fm ; if the battery capacity and power are insufficient, the battery discharges or the grid purchases electricity for supplementary power regulation until K_fluc = -K fm .

[0118] The photovoltaic hydrogen production system in the present invention mainly consists of a photovoltaic system, an energy storage system, and a hydrogen production system. The purpose of optimizing the capacity configuration is to reasonably configure the installed capacities of photovoltaic modules, batteries, electrolyzers, and compressors to achieve the best operating state of the system. The research on the energy management strategy and capacity configuration of the photovoltaic hydrogen production system cannot be carried out separately. The determination of the distribution boundary of the system energy flow requires the determined capacity configuration information, and the determination of the capacity configuration information requires the hydrogen production power allocated according to the energy management strategy as a necessary process parameter. Therefore, the present invention selects to conduct a joint optimization analysis of these two studies. Therefore, it is considered to jointly optimize the volatility in combination with photovoltaic modules, batteries, and electrolyzers.

[0119] S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit K fm As the target parameter, a capacity configuration optimization model for the photovoltaic hydrogen production system is constructed.

[0120] Since the magnitude of the target parameters is different, normalization is required. First, by optimizing a single target parameter and running the entire capacity configuration model, the lowest unit hydrogen production cost and the highest hydrogen production volume of the system under the optimal conditions can be obtained. The hydrogen production power fluctuation rate limit K fm It is already between 0 and 1 and can be called directly.

[0121] Therefore, the objective function of the capacity configuration optimization model is as follows:

[0122]

[0123] In the formula,

[0124] M is the objective function value,

[0125] C h2,min For the lowest unit hydrogen production cost,

[0126] C h2 is the unit hydrogen production cost,

[0127] V h2 is the total hydrogen production volume,

[0128] V h2,max is the maximum total hydrogen production volume,

[0129] K fm is the volatility limit,

[0130] w1~w3 are weight coefficients, which are adjusted according to project requirements.

[0131] w3 are 0.5, 0.47, and 0.03 respectively.

[0132] Total hydrogen production volume refers to the total amount of hydrogen produced during the project operation, Nm 3 The unit hydrogen production cost is the energy cost of hydrogen production, which refers to the ratio of the overall investment, operation and maintenance costs of the photovoltaic hydrogen production system to the overall hydrogen production volume, reflecting the economy of the system.

[0133] Specifically, the unit hydrogen production cost C h2 Solve by the following formula

[0134]

[0135] C cap =C pv +C el +C bat +Ccomp

[0136] C om =C om_pv +C om_el +C om_bat +C om_compre +C h2o +C grid

[0137] In the formula,

[0138] C h2 is the unit hydrogen production cost,

[0139] C sys is the total discounted cost of the system during the project operation period,

[0140] ∑ is the sum,

[0141] V h2,t is the hydrogen production volume at time t,

[0142] C cap(i) is the equipment investment cost in year i,

[0143] C om(i) is the operation and maintenance cost in the i-th year,

[0144] r is the discount rate,

[0145] Y i is the ordinal number of the year of capital investment,

[0146] C om is the investment cost of the equipment,

[0147] C pv is the investment cost of PV modules,

[0148] C el is the investment cost of the electrolytic cell,

[0149] C bat is the investment cost of the battery,

[0150] C compre is the investment cost of the compressor,

[0151] C om The operation and maintenance cost of the equipment.

[0152] C om_pv is the operation and maintenance cost of PV panels,

[0153] C om_el is the operation and maintenance cost of the electrolytic cell,

[0154] C om_bat is the operation and maintenance cost of the battery,

[0155] C om_compre The electricity cost and maintenance cost for the operation of the compressor,

[0156] C h2o The water consumption cost of the system operation is

[0157] C grid The cost of purchasing electricity to supplement power to the grid.

[0158] In this embodiment, r is 5%; the compressor also consumes 0.15-0.26 kWh of electricity during operation. This paper assumes that the compression is 1 Nm 3 The operating cost of hydrogen is 1 yuan; for every 1Nm of hydrogen produced by the electrolytic cell, 0.89kg of water is consumed, and the cost of water used for electrolysis is 10 yuan / ton.

[0159] In this embodiment, we select data for the whole year of 2016, and then use this data as a basis to calculate the total output and consumption for 15 years (the annual attenuation rate of photovoltaic output is 1%). The cost parameters of each device are shown in Table 1.

[0160] Table 1 Cost parameters of each equipment

[0161]

[0162]

[0163] Taking into account the volatility of domestic electricity prices, electricity prices are divided into peak-time electricity prices, valley-time electricity prices and parity prices. When the electricity prices are at valley-time and parity prices, the power grid is given priority to supplement the power shortage, and when the electricity prices are at peak and peak times, the battery power is given priority to supplement the shortage. The time-of-use electricity price for commercial electricity in a certain place in China is shown in Table 2.

[0164] Table 2 Time-of-use electricity price for commercial electricity in a certain place

[0165]

[0166] S4) setting constraints of the objective function of the capacity configuration optimization model, wherein the constraints include system power balance constraints, grid power constraints, battery power constraints, and battery capacity constraints.

[0167] Specifically, the system power balance constraint is:

[0168]

[0169] In the formula,

[0170] P pv is the output power of the photovoltaic power generation system,

[0171] P gridis the grid power,

[0172] P h2 (i) is the hydrogen production power of the alkaline electrolyzer hydrogen production subsystem in year i,

[0173] P bat is the battery power.

[0174] Specifically, the grid power constraint condition is:

[0175]

[0176] In the formula,

[0177] P grid is the grid power,

[0178] is the maximum grid power.

[0179] Specifically, the battery power constraint condition is:

[0180]

[0181] In the formula,

[0182] P bat is the battery power,

[0183] is the maximum battery power.

[0184] Specifically, the battery capacity constraint condition;

[0185] 0≤SOC≤1

[0186] In the formula,

[0187] SOC is the battery limit capacity.

[0188] S5) Under the constraints of step S4), the objective function of step S3) is solved using a particle swarm algorithm to obtain an optimal hydrogen production power fluctuation rate limit, an optimal electrolyzer capacity, and an optimal battery capacity.

[0189] In the present invention, the particle swarm optimization algorithm (PSO) is used, and the photovoltaic data is on a 15-minute scale. The operation logic is shown in Figure 2 shown.

[0190] After the capacity configuration optimization model is optimized and calculated, the results are shown in Tables 3 to 5.

[0191] Table 3 Taking unit hydrogen production cost as an independent optimization target

[0192]

[0193]

[0194] Table 3 runs the optimization model with unit hydrogen production cost as the independent optimization target, that is, M = 1 / C h2 The lowest unit hydrogen production price is 2.2064 yuan / Nm 3 .

[0195] Table 4 Taking hydrogen production volume as an independent optimization target

[0196]

[0197] Table 4 runs the optimization model with hydrogen production volume as the independent optimization target, that is, M = V h2 , the maximum total hydrogen production volume is 1.5395×10 6 Nm 3 .

[0198] The lowest unit hydrogen production price is 2.2064 yuan / Nm 3 and the maximum total hydrogen production volume is 1.5395×10 6 Nm 3 As a basis, the total objective function is brought into the optimization calculation, that is, The results are shown in Table 5. It can be seen that the capacity configuration optimization model successfully completed the capacity configuration optimization of the photovoltaic hydrogen production system and achieved the best comprehensive performance result.

[0199] Table 5 Taking hydrogen production volume as an independent optimization target

[0200]

[0201]

[0202] The present invention proposes a capacity configuration optimization method for a photovoltaic hydrogen production system based on light power prediction. During the capacity configuration process, ultra-short-term light power prediction technology is used to smooth power fluctuations, and 15-minute data is used for detailed modeling and calculation. The capacity configuration optimization of the photovoltaic hydrogen production system is completed, and the best overall performance result is achieved.

[0203] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction, characterized in that: The steps include: S1) constructing a photovoltaic hydrogen production system, the system comprising a photovoltaic power generation subsystem, a battery energy storage subsystem, an alkaline electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, each of which is connected to a DC bus; S2) Establishing energy management strategy for photovoltaic hydrogen production system; S21) using the predicted input power of the photovoltaic power generation system in the future to adjust the output power of the photovoltaic power generation system at the current moment, thereby reducing the overall power volatility of the photovoltaic power generation system; the output power of the photovoltaic power generation system at the current moment is adjusted by the following formula In the formula, P pv,st is the target value of the output power of the photovoltaic power generation system at time t, P py,t is the output power of the photovoltaic power generation system at time t, P py,t+1 ~P pv,t+8 are the predicted input power of the photovoltaic power generation system at time t+1 to t+8, w1~w8 are weight coefficients; S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem by adjusting the output power target value of the photovoltaic power generation subsystem; S23) Setting the hydrogen production power fluctuation rate limit K fm , when -K fm ≤Real-time fluctuation rate of photovoltaic power generation system≤K fm When the power of the battery is adjusted, the power of the hydrogen production of the alkaline electrolyzer is stabilized, thereby determining the final power of the hydrogen production of the alkaline electrolyzer; S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit K fm As target parameters, the lowest unit hydrogen production cost and the highest total hydrogen production volume of the system under the optimal conditions are determined, each target parameter is normalized, and a capacity configuration optimization model of the photovoltaic hydrogen production system is constructed. The objective function of the capacity configuration optimization model is as follows: In the formula, M is the objective function value, C h2,min For the lowest unit hydrogen production cost, C h2 is the unit hydrogen production cost, V h2 is the total hydrogen production volume, V h2,max is the maximum total hydrogen production volume, K fm is the volatility limit, w1~w3 are weight coefficients respectively; S4) setting constraints of the objective function of the capacity configuration optimization model, wherein the constraints include system power balance constraints, grid power constraints, battery power constraints, and battery capacity constraints; S5) Under the constraints of step S4), the objective function of step S3) is solved using a particle swarm algorithm to obtain an optimal hydrogen production power fluctuation rate limit, an optimal electrolyzer capacity, and an optimal battery capacity.

2. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 1 is characterized in that: S22), the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula: In the formula, K_fluc is the real-time fluctuation rate of the photovoltaic power generation system, P pv,st is the target value of the output power of the photovoltaic power generation system at time t, P pv,t is the output power of the photovoltaic power generation system at time t.

3. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction according to claim 2 is characterized in that: In S23), when -K fm ≤K_fluc≤K fm the specific strategy for regulating the battery power is as follows: when the battery SOC > bat_u, the battery discharges to increase the hydrogen production power of the alkaline electrolyzer; when the battery SOC < bat_i, the battery charges to reduce the hydrogen production power of the alkaline electrolyzer; when bat_i ≤ battery SOC ≤ bat_u, no operation is performed; where bat_u is the upper limit of the battery power regulation and bat_i is the lower limit of the battery power regulation.

4. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 3 is characterized in that: In S23), when K_fluc>K fm If the battery capacity and power are sufficient, the battery will be charged until K_fluc = K fm ; If the battery capacity and power are insufficient, the excess photovoltaic power will be abandoned until K_fluc = K fm .

5. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 4 is characterized in that: In S23), when K_fluc<-K fm If the battery capacity and power are sufficient, the battery will discharge until K_fluc = -K fm ; If the battery capacity and power are insufficient, the battery is discharged or the grid purchases electricity to supplement power regulation until K_fluc = -K fm .

6. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 1 is characterized in that: S3), the unit hydrogen production cost C h2 Solve by the following formula C cap =C pv +C el +C bat +C compre C om =C om_pv +C om_el +C om_bat +C om_compre +C h2o +C grid In the formula, C h2 is the unit hydrogen production cost, C sys is the total discounted cost of the system during the project operation period, ∑ is the sum, V h2,t is the hydrogen production volume at time t, C cap(i) is the equipment investment cost in year i, C om(i) is the operation and maintenance cost in the i-th year, r is the discount rate, Y i is the ordinal number of the year of capital investment, C om is the investment cost of the equipment, C pv is the investment cost of PV modules, C el is the investment cost of the electrolytic cell, C bat is the investment cost of the battery, C compre is the investment cost of the compressor, C om The operation and maintenance cost of the equipment. C om_pv is the operation and maintenance cost of PV panels, C om_el is the operation and maintenance cost of the electrolytic cell, C om_bat is the operation and maintenance cost of the battery, C om_compre The electricity cost and maintenance cost for the operation of the compressor, C h2o The water consumption cost of the system operation is C grid The cost of purchasing electricity to supplement power to the grid.

7. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction according to claim 1 is characterized in that: In S4), the system power balance constraint is: In the formula, P pv is the output power of the photovoltaic power generation system, P grid is the grid power, P h2 (i) is the hydrogen production power of the alkaline electrolyzer hydrogen production subsystem in year i, P bat is the battery power.

8. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction according to claim 7 is characterized in that: S4), the power grid power constraint condition is: In the formula, P grid is the grid power, is the maximum grid power.

9. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 8 is characterized in that: In S4), the battery power constraint condition is: In the formula, P bat is the battery power, is the maximum battery power.

10. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction according to claim 9, characterized in that: S4), the battery capacity constraint; 0≤SOC≤1 In the formula, SOC is the battery limit capacity.

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