Capacity configuration optimization method of photovoltaic hydrogen production system based on optical power prediction

Abstract

The present invention discloses a method for optimizing the capacity configuration of a photovoltaic hydrogen production system based on light power prediction. The method comprises the following steps: constructing a photovoltaic hydrogen production system; using the predicted input power of the photovoltaic power generation system in the future to establish an energy management strategy for the photovoltaic hydrogen production system; and optimizing the capacity configuration of a photovoltaic hydrogen production system based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit K. fm As target parameters, the system's minimum unit hydrogen production cost and maximum total hydrogen production volume under optimal conditions are determined. Each target parameter is normalized to construct a capacity configuration optimization model for the photovoltaic hydrogen production system. Constraints are set for the objective function of this capacity configuration optimization model. A particle swarm algorithm is used to solve the objective function to obtain the optimal hydrogen production power fluctuation rate limit, optimal electrolyzer capacity, and optimal battery capacity. This method optimizes the capacity configuration of the photovoltaic hydrogen production system and achieves the best overall performance.

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 not only effectively utilizes solar energy, an inexhaustible and 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, industry, and other fields. 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] Hydrogen production from water electrolysis is a core technology for large-scale renewable energy hydrogen production. It includes alkaline electrolysis cells (AKL), proton exchange membrane electrolyzers (PEM), and solid oxide electrolyzers (SOEC). PEM and SOECs offer significant advantages due to their ability to achieve high current densities and high current efficiencies (above 90%). However, their high material costs and the high temperature used in SOECs accelerate equipment aging, hindering their widespread market adoption. Alkaline electrolyzers, on the other hand, offer lower costs and the flexibility to rapidly respond to the random fluctuations in renewable energy output, with dynamic response times ranging from milliseconds to seconds. They are currently the mainstream P2H equipment on the market.

[0004] The core of capacity configuration optimization for renewable energy hydrogen production systems lies in finding a balance, that is, maximizing the use of renewable energy, reducing energy waste, and lowering the system's investment and operating costs while meeting energy demand. Currently, existing capacity configuration optimization methods have the following problems:

[0005] First, there is little use of photovoltaic forecast data, and energy management strategies are more often focused on regulating hydrogen production power to within 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 response to the shortcomings of the existing technology, 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, 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 objectives, the present invention provides a method for optimizing the capacity configuration of a photovoltaic hydrogen production system based on light power prediction, which is particularly characterized by comprising 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 an energy management strategy for the photovoltaic hydrogen production system;

[0012] S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, the output power of the photovoltaic power generation subsystem at the current moment is adjusted to reduce the overall power volatility of the photovoltaic power generation subsystem; the output power of the photovoltaic power generation subsystem at the current moment is adjusted by the following formula

[0013] ;

[0014] Where,

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

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

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

[0018] All are weight coefficients;

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

[0020] S23) Set the hydrogen production power fluctuation rate limit ,when Real-time fluctuation rate of photovoltaic power generation subsystem 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;

[0021] S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit 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] Where,

[0024] M is the objective function value,

[0025] For the lowest unit hydrogen production cost,

[0026] is the unit hydrogen production cost,

[0027] is the total hydrogen production volume,

[0028] is the maximum total hydrogen production volume,

[0029] is the volatility limit,

[0030] ~ are weight coefficients respectively;

[0031] S4) setting constraints for 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 particle swarm algorithm is used to solve the objective function of step S3) to obtain the optimal hydrogen production power fluctuation rate limit, the optimal electrolyzer capacity, and the optimal battery capacity.

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

[0034] ;

[0035] Where,

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

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

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

[0039] Furthermore, in S23), when the specific strategy for the 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 the battery power regulation and bat_i is the lower limit of the battery power regulation.

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

[0041] Furthermore, in S23), when if the battery capacity and power are sufficient, the battery discharges until ; if the battery capacity and power are insufficient, the battery discharges or power is purchased from the grid for supplementary power regulation until .

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

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] In the formula,

[0048] is the unit hydrogen production cost,

[0049] is the total discounted cost of the system during the project operation period,

[0050] ∑ is the summation,

[0051] is the volume of hydrogen produced at time t,

[0052] is the equipment investment cost in year i,

[0053] is the operation and maintenance cost in year i,

[0054] r is the discount rate,

[0055] is the ordinal number of the year of capital investment,

[0056] is the investment cost of the equipment,

[0057] is the investment cost of photovoltaic modules,

[0058] is the investment cost of the electrolytic cell,

[0059] is the investment cost of the battery,

[0060] is the investment cost of the compressor,

[0061] The operation and maintenance cost of the equipment,

[0062] is the operation and maintenance cost of photovoltaic modules,

[0063] is the operation and maintenance cost of the electrolytic cell,

[0064] is the operation and maintenance cost of the battery,

[0065] The electricity cost and maintenance cost of the compressor are consumed.

[0066] The water consumption cost of the system operation is

[0067] The cost of purchasing electricity to supplement power to the grid.

[0068] Furthermore, in S4), the grid power constraint condition is

[0069] ;

[0070] Where,

[0071] is the grid power,

[0072] is the maximum grid power.

[0073] Furthermore, in S4), the battery power constraint condition is

[0074] ;

[0075] Where,

[0076] is the battery power,

[0077] is the maximum battery power.

[0078] Furthermore, in S4), the battery capacity constraint condition;

[0079] ;

[0080] Where,

[0081] SOC is the battery capacity.

[0082] The advantages of the present invention are:

[0083] 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 regulation 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 starts and stops of the electrolyzer, and extending the life of the electrolyzer to a certain extent;

[0084] 2. The present invention combines the system's energy management strategy with the system's capacity configuration optimization method. Determining the distribution boundary of the system's energy flow requires certain capacity configuration information, and determining 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 jointly optimizing the volatility with photovoltaic modules, batteries, and electrolytic cells.

[0085] The present invention proposes a capacity configuration optimization method for photovoltaic hydrogen production systems 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. This completes the capacity configuration optimization of the photovoltaic hydrogen production system and achieves the best overall performance results. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 is an energy management strategy diagram of the system in the present invention;

[0087] Figure 2 This is a flow chart of capacity configuration optimization in the present invention. DETAILED DESCRIPTION

[0088] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0089] 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:

[0090] S1) Build a photovoltaic hydrogen production system, the system including 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.

[0091] S2) Establish an energy management strategy for the photovoltaic hydrogen production system. The specific steps are as follows.

[0092] S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, the output power of the photovoltaic power generation subsystem at the current moment is adjusted to reduce the overall power volatility of the photovoltaic power generation subsystem; the output power of the photovoltaic power generation subsystem at the current moment is adjusted by the following formula

[0093] ;

[0094] Where,

[0095] is the target value of the photovoltaic power generation system output power at time t,

[0096] is the output power of the photovoltaic power generation system at time t,

[0097] ~ They are the predicted input power of the photovoltaic power generation system at time t+1 to t+8, and the adjacent time intervals are 15 minutes.

[0098] are all weight coefficients.

[0099] ~ 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. They are 0.23, 0.20, 0.17, 0.14, 0.11, 0.08, 0.05, and 0.02 respectively.

[0100] 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.

[0101] Specifically, the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula:

[0102] ;

[0103] In the formula,

[0104] is the real-time volatility of the photovoltaic power generation subsystem,

[0105] is the adjustment target value of the output power of the photovoltaic power generation subsystem at time t,

[0106] is the output power of the photovoltaic power generation subsystem at time t.

[0107] S23) Set the limit value of the hydrogen production power volatility , when the real-time volatility of the photovoltaic power generation subsystem , perform power regulation on the battery to suppress the hydrogen production power of the alkaline electrolyzer, so as to determine the final hydrogen production power of the alkaline electrolyzer.

[0108] By setting the limit value of the hydrogen production power volatility to determine the real-time hydrogen production power. In the photovoltaic hydrogen production system, the battery power and the grid power are the main ways to suppress the hydrogen production power. The energy storage capacity of the battery is limited, and the photovoltaic output power is weak in the sunrise and sunset time periods and the strongest at noon. Therefore, it often occurs that the battery is short of power in the sunrise and sunset time periods and full of power at noon, resulting in the waste of the surplus optical power at noon. In order to achieve "peak shaving and valley filling" of the battery SOC state to a certain extent and improve the utilization rate of the 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 energy management strategy of the photovoltaic hydrogen production system is shown in Figure 1 as shown.

[0109] When , the specific strategy of the 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, do nothing;

[0110] When , if the battery capacity margin and power are sufficient, the battery charges until ; if the battery capacity margin and power are insufficient, the excess photovoltaic power generation power is the abandoned optical power until ;

[0111] When , if the battery capacity and power are sufficient, the battery discharges until ; if the battery capacity and power are insufficient, the battery discharges or the grid purchases electricity for supplementary power regulation until .

[0112] The photovoltaic hydrogen production system in this invention primarily consists of a photovoltaic system, an energy storage system, and a hydrogen production system. The purpose of capacity configuration optimization is to rationally allocate the installed capacity of photovoltaic modules, batteries, electrolytic cells, and compressors to achieve optimal system operation. Research on the energy management strategy and capacity configuration of the photovoltaic hydrogen production system cannot be conducted separately. Determining the distribution boundaries of the system's energy flow requires certain capacity configuration information, and determining this capacity configuration information requires the hydrogen production power allocated according to the energy management strategy as a necessary process parameter. Therefore, this invention chooses to conduct a joint optimization analysis of these two studies. Therefore, consideration is given to combining volatility with optimization of photovoltaic modules, batteries, and electrolytic cells.

[0113] S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit As the target parameter, a capacity configuration optimization model for the photovoltaic hydrogen production system is constructed.

[0114] Since the magnitude of the target parameters is different, normalization is required. First, through the optimization of a single target parameter, the entire capacity configuration model is run to obtain the lowest unit hydrogen production cost and the highest hydrogen production volume of the system under the optimal conditions, and the hydrogen production power fluctuation rate limit is It is already between 0 and 1 and can be called directly.

[0115] Therefore, the objective function of the capacity configuration optimization model is as follows:

[0116] ;

[0117] Where,

[0118] M is the objective function value,

[0119] For the lowest unit hydrogen production cost,

[0120] is the unit hydrogen production cost,

[0121] is the total hydrogen production volume,

[0122] is the maximum total hydrogen production volume,

[0123] is the volatility limit,

[0124] ~ They are weight coefficients, which are adjusted according to project requirements. ~ They are 0.5, 0.47, and 0.03 respectively.

[0125] 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 system's economic efficiency.

[0126] Specifically, the unit hydrogen production cost Solve by the following formula

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] Where,

[0132] is the unit hydrogen production cost,

[0133] is the total discounted cost of the system during the project operation period,

[0134] ∑ is the sum,

[0135] is the volume of hydrogen produced at time t,

[0136] is the equipment investment cost in year i,

[0137] is the operation and maintenance cost in year i,

[0138] r is the discount rate,

[0139] is the ordinal number of the year of capital investment,

[0140] is the investment cost of the equipment,

[0141] is the investment cost of photovoltaic modules,

[0142] is the investment cost of the electrolytic cell,

[0143] is the investment cost of the battery,

[0144] is the investment cost of the compressor,

[0145] The operation and maintenance cost of the equipment,

[0146] is the operation and maintenance cost of photovoltaic modules,

[0147] is the operation and maintenance cost of the electrolytic cell,

[0148] is the operation and maintenance cost of the battery,

[0149] The electricity cost and maintenance cost of the compressor are consumed.

[0150] The water consumption cost of the system operation is

[0151] The cost of purchasing electricity to supplement power to the grid.

[0152] 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 1 Nm of hydrogen produced by the electrolytic cell, 0.89 kg of water is consumed, and the cost of water used for electrolysis is 10 yuan / ton.

[0153] In this example, we selected data from the entire year of 2016 and used this data as a basis to calculate the total output and consumption over 15 years (the annual photovoltaic output attenuation rate is 1%). The cost parameters of each device are shown in Table 1.

[0154] Table 1 Cost parameters of various equipment

[0155]

[0156] Taking into account the volatility of domestic electricity prices, electricity prices are categorized into peak, off-peak, and par prices. During off-peak and par prices, the grid is prioritized for filling power shortages, while during peak and peak prices, battery power is prioritized for filling power shortages. Table 2 shows the time-of-use electricity prices for commercial electricity in a certain location in China.

[0157] Table 2 Time-of-use electricity prices for commercial electricity in a certain place

[0158]

[0159] 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.

[0160] Specifically, the grid power constraint condition is:

[0161] ;

[0162] Where,

[0163] is the grid power,

[0164] is the maximum grid power.

[0165] Specifically, the battery power constraint condition is:

[0166] ;

[0167] Where,

[0168] is the battery power,

[0169] is the maximum battery power.

[0170] Specifically, the battery capacity constraint condition;

[0171] ;

[0172] Where,

[0173] SOC is the battery capacity.

[0174] S5) Under the constraints of step S4), the particle swarm algorithm is used to solve the objective function of step S3) to obtain the optimal hydrogen production power fluctuation rate limit, the optimal electrolyzer capacity, and the optimal battery capacity.

[0175] In this 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.

[0176] After the capacity configuration optimization model is optimized and calculated, the results are shown in Tables 3 to 5.

[0177] Table 3 Taking unit hydrogen production cost as an independent optimization target

[0178]

[0179] Table 3 runs the optimization model with unit hydrogen production cost as the independent optimization target. The lowest unit hydrogen production price is 2.2064 yuan / Nm 3 .

[0180] Table 4 Taking hydrogen production volume as an independent optimization target

[0181]

[0182] Table 4 runs the optimization model with hydrogen production volume as the independent optimization target. The maximum total hydrogen production volume is 1.5395×10 6 Nm 3 .

[0183] The lowest unit hydrogen production price is 2.2064 yuan / Nm 3 and the maximum total hydrogen production volume of 1.5395×10 6 Nm 3 As the 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 overall performance.

[0184] Table 5 Taking hydrogen production volume as an independent optimization target

[0185]

[0186] The present invention proposes a capacity configuration optimization method for photovoltaic hydrogen production systems 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. This completes the capacity configuration optimization of the photovoltaic hydrogen production system and achieves the best overall performance results.

[0187] 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 considered as equivalent replacement methods and are included in the scope of protection 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 an energy management strategy for the photovoltaic hydrogen production system; S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, the output power of the photovoltaic power generation subsystem at the current moment is adjusted to reduce the overall power volatility of the photovoltaic power generation subsystem; the output power of the photovoltaic power generation subsystem at the current moment is adjusted by the following formula ； Where, is the target value of the photovoltaic power generation system output power at time t, is the output power of the photovoltaic power generation system at time t, ~ are the predicted input power of the photovoltaic power generation system at time t+1 to t+8, All are weight coefficients; S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem by adjusting the target value of the output power of the photovoltaic power generation subsystem; S23) Set the hydrogen production power fluctuation rate limit ,when Real-time fluctuation rate of photovoltaic power generation subsystem 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; S3) Based on the total hydrogen production volume, unit hydrogen production cost, and hydrogen production power fluctuation rate limit 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: ； Where, M is the objective function value, For the lowest unit hydrogen production cost, is the unit hydrogen production cost, is the total hydrogen production volume, is the maximum total hydrogen production volume, is the volatility limit, ~ are weight coefficients respectively; S4) setting constraints for 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 particle swarm algorithm is used to solve the objective function of step S3) to obtain the optimal hydrogen production power fluctuation rate limit, the optimal electrolyzer capacity, and the 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: In S22), the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula: ； Where, is the real-time fluctuation rate of the photovoltaic power generation system, is the target value of the photovoltaic power generation system output power at time t, is the output power of the photovoltaic power generation system at time t.

3. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 2 is characterized in that: In S23), when 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.

4. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on light power prediction according to claim 3 is characterized in that: S23) when If the battery capacity and power are sufficient, the battery will be charged until If the battery capacity and power are insufficient, the excess photovoltaic power will be abandoned until .

5. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 4 is characterized in that: S23) when If the battery capacity and power are sufficient, the battery will be discharged until If the battery capacity and power are insufficient, the battery will be discharged or the grid will purchase electricity to supplement the power regulation until .

6. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on optical power prediction according to claim 1, characterized in that: S3) Unit hydrogen production cost Solve by the following formula ； ； ； ； Where, is the unit hydrogen production cost, is the total discounted cost of the system during the project operation period, ∑ is the sum, is the volume of hydrogen produced at time t, is the equipment investment cost in year i, is the operation and maintenance cost in year i, r is the discount rate, is the ordinal number of the year of capital investment, is the investment cost of the equipment, is the investment cost of photovoltaic modules, is the investment cost of the electrolytic cell, is the investment cost of the battery, is the investment cost of the compressor, The operation and maintenance cost of the equipment, is the operation and maintenance cost of photovoltaic modules, is the operation and maintenance cost of the electrolytic cell, is the operation and maintenance cost of the battery, The electricity cost and maintenance cost of the compressor are consumed. The water consumption cost of the system operation is The cost of purchasing electricity to supplement power to the grid.

7. The photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 1 is characterized in that: In S4), the grid power constraint condition is: ； Where, is the grid power, is the maximum grid power.

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

9. The method for optimizing capacity configuration of a photovoltaic hydrogen production system based on optical power prediction according to claim 8, characterized in that: S4), the battery capacity constraint; ； Where, SOC is the battery capacity.

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