Day-ahead / intraday scheduling optimization method for photovoltaic hydrogen production system based on photovoltaic forecast

Abstract

The present invention discloses a method for optimizing the capacity configuration of a photovoltaic hydrogen production system based on light power prediction. It includes the following steps: building a photovoltaic hydrogen production system; using the predicted input power of the photovoltaic power generation system for a period of time in the future, and establishing an energy management strategy for the photovoltaic hydrogen production system to suppress the volatility of the hydrogen production power; using the system's daily operating cost and the photovoltaic absorption rate as target parameters, determining the lowest daily unit hydrogen production cost and the highest daily total hydrogen production under optimal conditions, first constructing a long-term scheduling optimization model for the photovoltaic hydrogen production system before constructing a short-term scheduling optimization model for the photovoltaic hydrogen production system; setting constraints for the long-term scheduling optimization model and the short-term scheduling optimization model, using a particle swarm algorithm to solve the optimization model, and obtaining the optimal power allocation plan for each electrolyzer on the day. The present invention completes the optimal power allocation plan for each electrolyzer on the day, realizes real-time control of the electrolyzer power, and reduces the operating cost on the day.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy hydrogen production, and specifically to a day-ahead / intraday scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction. Background Art

[0002] With the transformation of the global energy structure and the severe challenges of climate change, the development and utilization of renewable energy has become a focus of international attention. As one of the most abundant and cleanest renewable energy sources, solar photovoltaic (PV) technology has made significant progress in recent years and has been widely adopted worldwide. However, the intermittent and fluctuating nature of PV power generation poses challenges to the stable operation of the power grid and limits its direct application in areas requiring a stable energy supply, such as large-scale industrial production and energy storage.

[0003] Photovoltaic hydrogen storage can effectively address these issues. It converts photovoltaic electricity into hydrogen, enabling indirect storage and efficient utilization of solar energy. As a clean and efficient energy carrier, hydrogen has broad application prospects, including in transportation, industrial raw materials, and distributed energy systems. Therefore, the optimized operation of photovoltaic hydrogen production systems is of great significance for promoting energy transformation and achieving green and low-carbon development.

[0004] In the operation of photovoltaic hydrogen production systems, day-ahead and intraday scheduling optimization is a key step. Through reasonable scheduling strategies and consideration of the charge and discharge schedules of energy storage devices, the energy supply and demand within the system can be balanced, improving the overall economics and reliability of the system.

[0005] Currently, existing day-ahead / intraday scheduling optimization methods have the following problems:

[0006] First, there is less use of photovoltaic prediction data. In most cases, the energy management strategy is to adjust the hydrogen production power to the safe operating range of the electrolyzer. There is less consideration of actively adjusting the operating power of the electrolyzer based on this data. In the long run, this will affect the service life of the electrolyzer.

[0007] Second, the impact of dynamic electricity prices on the economic efficiency of system operation is rarely considered.

[0008] Third, the impact of the dynamic efficiency of the electrolyzer on the economic operation of the system is rarely considered. Summary of the Invention

[0009] In response to the shortcomings of the existing technology, the present invention proposes a day-ahead / intra-day scheduling optimization method for photovoltaic hydrogen production systems based on photovoltaic prediction, which can fully utilize the prediction information of photovoltaic power generation, optimize the operating status and power distribution of the electrolyzer, and ensure the efficient and stable operation of the system under different lighting conditions.

[0010] To achieve the above objectives, the present invention designs a method for optimizing the day-ahead / intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic forecasting, which is particularly characterized by comprising the following steps:

[0011] S1) constructing a photovoltaic hydrogen production system, the system comprising a photovoltaic power generation subsystem, a battery energy storage subsystem, an electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, each of which is connected to a DC bus;

[0012] S2) Establish an energy management strategy for the photovoltaic hydrogen production system to suppress the fluctuation of hydrogen production power;

[0013] S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, adjusting the output power of the photovoltaic power generation subsystem at the current moment 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

[0014]

[0015] Where,

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

[0017] P pv,t is the output power of the photovoltaic power generation system at time t,

[0018] 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,

[0019] w1~w8 are weight coefficients;

[0020] S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem based on the adjustment target value of the output power of the photovoltaic power generation subsystem;

[0021] S23) Setting the hydrogen production power fluctuation rate limit K fm , compare the real-time fluctuation rate of the photovoltaic power generation system with the hydrogen production power fluctuation rate limit K fm , respectively formulate the first energy management strategy and the second energy management strategy of the photovoltaic hydrogen production system to achieve the stabilization of the hydrogen production power of the electrolyzer, thereby determining the final hydrogen production power of the electrolyzer;

[0022] S3) Taking the system daily operating cost and the photovoltaic absorption rate as target parameters, determine the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the optimal conditions, first construct a day-ahead long-term scheduling optimization model for the photovoltaic hydrogen production system, and then construct an intraday short-term scheduling optimization model for the photovoltaic hydrogen production system;

[0023] The daily unit hydrogen production cost is expressed by the following formula

[0024]

[0025] C sys =∑C pv +C bat +C ele +C st +C compre +C h2o +C grid

[0026] Where,

[0027] C h2 is the daily unit hydrogen production cost,

[0028] C sys The daily cost of the system, including initial investment and operation and maintenance costs,

[0029] C o2 is the daily unit oxygen production cost,

[0030] V h2,t is the volume of hydrogen produced at time t,

[0031] ∑ is the sum,

[0032] C pv is the daily cost of photovoltaic modules, including initial investment and operation and maintenance costs,

[0033] C bat is the daily cost of the battery, including initial investment and operation and maintenance costs,

[0034] C ele is the daily cost of the electrolyzer, including initial investment and operation and maintenance costs,

[0035] C st is the start-up and shutdown cost, cold standby cost and hot standby cost of the electrolyzer,

[0036] C compre is the daily cost of the compressor, including initial investment and operation and maintenance costs,

[0037] C h20 The daily cost of water consumption for hydrogen production in the system,

[0038] C grid the cost of purchasing electricity for the grid;

[0039] S4) Set the constraint conditions for the long-term day-ahead scheduling optimization model and the short-term intra-day scheduling optimization model. The constraint conditions include system power balance constraint conditions, grid power constraint conditions, battery power constraint conditions, battery capacity constraint conditions, and input power constraint conditions for a single electrolyzer.

[0040] S5) Under the constraint conditions in step S4), use the particle swarm algorithm to solve the long-term day-ahead scheduling optimization model and the short-term intra-day scheduling optimization model in step S3), and obtain the optimal power distribution plan for each electrolyzer on the current day.

[0041] Furthermore, in S22), the real-time volatility of the photovoltaic power generation subsystem is calculated by the following formula

[0042]

[0043] In the formula,

[0044] K_fluc is the real-time volatility of the photovoltaic power generation subsystem,

[0045] P pv,st is the regulation target value of the output power of the photovoltaic power generation subsystem at time t,

[0046] P pv,t is the output power of the photovoltaic power generation subsystem at time t.

[0047] Furthermore, in S23), if the predicted light power for the next day is sufficient, the system operates on the current day using the first energy management strategy; if the predicted light power for the next day is insufficient or severely insufficient, the system operates on the current day using the second energy management strategy.

[0048] Furthermore, in S23), in the first energy management strategy, 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 electrolyzer; when the battery SOC < bat_i, the battery charges to reduce the hydrogen production power of the 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;

[0049] 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 is the curtailed power until K fluc =K fm ;

[0050] 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 .

[0051] Furthermore, in S23), in the second energy management strategy, when -K fm ≤K_fluc≤K fm When the grid power is sufficient, the grid discharges to K_fluc=K fm If the grid power is insufficient, the battery discharges or the grid purchases electricity to supplement power regulation until K_fluc=K fm ;

[0052] 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 ;

[0053] When K_fluc<-K fm When the grid power is sufficient, the grid discharges to K_fluc=K fm If the grid power is insufficient, the battery discharges or the grid purchases electricity to supplement power regulation until K_fluc=K fm .

[0054] Furthermore, in S3), the total daily hydrogen production is obtained by a dynamic efficiency electrolyzer mathematical model and an electrolyzer hydrogen production efficiency model. The dynamic efficiency electrolyzer mathematical model formula is as follows:

[0055]

[0056] Where,

[0057] U el is the terminal voltage of the electrolysis chamber,

[0058] U rev is the reverse voltage of the electrolysis chamber,

[0059] r1 and r2 are the ohmic resistance parameters of the electrolyte,

[0060] A is the area of ​​the electrolysis module,

[0061] T is the temperature of the electrolytic cell,

[0062] s is the overvoltage,

[0063] t1, t2, t3 are overvoltage coefficients,

[0064] I el is the DC current of the electrolytic cell,

[0065] U rev0 is the reversible voltage under standard conditions, where T0 = 25°C.

[0066] P0=1.01325bar,

[0067] R is the gas constant,

[0068] z is the number of electrons transferred per reaction,

[0069] F is Faraday's constant;

[0070] The electrolyzer hydrogen production efficiency model formula is as follows

[0071]

[0072] Where,

[0073] η F is the current efficiency,

[0074] I el is the DC current of the electrolytic cell,

[0075] η v is the voltage efficiency,

[0076] U th is the thermal neutral voltage,

[0077] U el is the terminal voltage of the electrolytic cell,

[0078] η is the hydrogen production efficiency.

[0079] Furthermore, in S3), the objective functions of the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model are both expressed by the following formula:

[0080]

[0081] Where,

[0082] M is the objective function value,

[0083] C h2,min is the lowest daily unit hydrogen production cost,

[0084] C h2 is the daily unit hydrogen production cost,

[0085] V h2 is the total daily hydrogen production volume,

[0086] V h2,max is the maximum daily total hydrogen production volume,

[0087] w1~w2 are weight coefficients respectively.

[0088] Furthermore, in S4), the system power balance constraint is

[0089]

[0090] Where,

[0091] P pv is the output power of the photovoltaic power generation system,

[0092] P grid is the grid power,

[0093] P h2 (i) is the hydrogen production power of the electrolyzer hydrogen production subsystem at the i-th time point, P bat is the battery power;

[0094] The grid power constraint condition is:

[0095]

[0096] Where,

[0097] P grid is the grid power,

[0098] is the maximum grid power;

[0099] The battery power constraint condition is:

[0100]

[0101] Where,

[0102] P bat is the battery power,

[0103] is the maximum battery power;

[0104] The battery capacity constraint condition is:

[0105] 0≤SOC≤1

[0106] Where,

[0107] SOC is the battery constraint capacity;

[0108] The single electrolyzer input power constraint condition is:

[0109] P rate ≥P i,j ≥0.1P rate

[0110]

[0111] Where,

[0112] P i,j is the input power of the j-th electrolyzer at the i-th time point,

[0113] P rate is the rated power of a single electrolyzer,

[0114] ∑ is the sum,

[0115] N ele is the number of electrolytic cells,

[0116] P h2,i is the total hydrogen production power at the i-th time point.

[0117] The advantages of the present invention are:

[0118] 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;

[0119] 2. This invention combines the system's energy management strategy with its day-ahead / intraday optimization scheduling methods. Day-ahead scheduling is a long-term, hourly schedule developed 24 hours in advance. It takes into account multiple energy flexibility constraints and generates a day-ahead operation plan for each equipment unit at the hourly level for the next day. Intraday scheduling is a short-term, 15-minute schedule developed two hours in advance. It provides the system with flexible operating margins and stabilizes power fluctuations by adjusting the output of each device. This plays a key role in daily scheduling.

[0120] The present invention proposes a day-ahead / intraday scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction. It considers using ultra-short-term light power prediction technology to suppress hydrogen power fluctuations, and completes the optimal power allocation plan for each electrolyzer on the day by constructing a day-ahead long-term scheduling optimization model and an intraday short-term scheduling optimization model for the photovoltaic hydrogen production system, thereby realizing real-time regulation of the electrolyzer power and reducing the daily operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 is the first energy management strategy diagram in the present invention;

[0122] Figure 2 is a second energy management strategy diagram in the present invention;

[0123] Figure 3 This is the day-ahead / intra-day scheduling optimization process in the present invention. DETAILED DESCRIPTION

[0124] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0125] The present invention provides a method for optimizing the day-ahead / intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction, comprising the following steps:

[0126] S1) Build a photovoltaic hydrogen production system, which includes a photovoltaic power generation subsystem, a battery energy storage subsystem, an electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, each of which is connected to a DC bus.

[0127] S2) Establish an energy management strategy for the photovoltaic hydrogen production system to suppress the fluctuation of hydrogen production power. The specific steps are as follows.

[0128] S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, adjusting the output power of the photovoltaic power generation subsystem at the current moment 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

[0129]

[0130] Where,

[0131] P pv,st is the target value of the photovoltaic power generation system output power at time t,

[0132] P pv,t is the output power of the photovoltaic power generation system at time t,

[0133] 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,

[0134] w1~w8 are all weight coefficients.

[0135] P pv,t+1 ~P pv,t+8 The ultra-short-term optical power prediction data for the last eight time nodes. The ultra-short-term prediction accuracy based on cloud image information and other methods has fully met the requirements. In this embodiment, w1 to w8 are 0.23, 0.20, 0.17, 0.14, 0.11, 0.08, 0.05, and 0.02 respectively.

[0136] 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.

[0137] Specifically, the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula:

[0138]

[0139] Where,

[0140] K_fluc is the real-time fluctuation rate of the photovoltaic power generation system,

[0141] P pv,st is the target value of the photovoltaic power generation system output power at time t,

[0142] P pv,t is the output power of the photovoltaic power generation system at time t.

[0143] S23) Setting the hydrogen production power fluctuation rate limit K fm , compare the real-time fluctuation rate of the photovoltaic power generation system with the hydrogen production power fluctuation rate limit K fm , respectively formulate the first energy management strategy and the second energy management strategy of the photovoltaic hydrogen production system to achieve the stabilization of the hydrogen production power of the electrolyzer, thereby determining the final hydrogen production power of the electrolyzer.

[0144] By setting the hydrogen production power fluctuation rate limit K fm To determine the real-time hydrogen production power, in the photovoltaic hydrogen production system, battery power and grid power are the main ways to balance the hydrogen power. The battery energy storage capacity is limited, and the photovoltaic output power is weak during sunrise and sunset, and strongest at noon. Therefore, the battery often has insufficient power during sunrise and sunset, but is fully charged at noon, resulting in the waste of surplus solar power in the noon.

[0145] Based on a comprehensive consideration of solar power forecasts and hydrogen load, we propose two energy management strategies: If the predicted solar power for the next day is sufficient, the system operates using the first energy management strategy; if the predicted solar power for the next day is insufficient or severely insufficient, the system operates using the second energy management strategy.

[0146] like Figure 1 As shown, in the first energy management strategy, when -K fm ≤K_fluc≤K fmWhen the specific strategy for regulating the power of the storage battery is as follows: when the SOC of the storage battery > bat_u, the storage battery discharges to increase the hydrogen production power of the electrolyzer; when the SOC of the storage battery < bat_i, the storage battery charges to reduce the hydrogen production power of the electrolyzer; when bat_i ≤ the SOC of the storage battery ≤ bat_u, no operation is performed; where bat_u is the upper limit of the power regulation of the storage battery and bat_i is the lower limit of the power regulation of the storage battery;

[0147] When K_fluc > K fm If the storage battery has sufficient capacity margin and power, the storage battery charges until K fluc = K fm ; if the storage battery has insufficient capacity margin and power, the excess photovoltaic power is curtailment power until K fluc = K fm ;

[0148] When K_fluc < -K fm If the storage battery has sufficient capacity and power, the storage battery discharges until K_fluc = -K[[ID=)19]] fm ; if the storage battery has insufficient capacity and power, the storage battery discharges or the grid purchases electricity for supplementary power regulation until K_fluc = -K fm [[ID=)22]].

[0149] As Figure 2 shown, in the second energy management strategy, when -K fm ≤ K_fluc ≤ K fm If the grid power is sufficient, the grid discharges until K_fluc = K fm ; if the grid power is insufficient, the storage battery discharges or the grid purchases electricity for supplementary power regulation until K_fluc = K fm ;

[0150] When K_fluc > K fm If the storage battery has sufficient capacity margin and power, the storage battery charges until K fluc = K fm ; if the storage battery has insufficient capacity margin and power, the excess photovoltaic power is curtailment power until K fluc = K fm ;

[0151] When K_fluc < -K fm If the grid power is sufficient, the grid discharges until K_fluc = K fm ; if the grid power is insufficient, the storage battery discharges or the grid purchases electricity for supplementary power regulation until K_fluc = K fm .

[0152] S3) Taking the system daily operating cost and photovoltaic absorption rate as target parameters, the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the optimal conditions are determined. First, a day-ahead long-term scheduling optimization model for the photovoltaic hydrogen production system is constructed, and then a day-ahead short-term scheduling optimization model for the photovoltaic hydrogen production system is constructed.

[0153] Specifically, the daily unit hydrogen production cost is expressed by the following formula:

[0154]

[0155] C sys =∑C pv +C bat +C ele +C st +C compre +C h2o +C grid

[0156] Where,

[0157] C h2 is the daily unit hydrogen production cost,

[0158] C sys The daily cost of the system, including initial investment and operation and maintenance costs,

[0159] C o2 is the daily unit oxygen production cost,

[0160] V h2,t is the volume of hydrogen produced at time t,

[0161] ∑ is the sum,

[0162] C pv is the daily cost of photovoltaic modules, including initial investment and operation and maintenance costs,

[0163] C bat is the daily cost of the battery, including initial investment and operation and maintenance costs,

[0164] C ele is the daily cost of the electrolyzer, including initial investment and operation and maintenance costs,

[0165] C st is the start-up and shutdown cost, cold standby cost and hot standby cost of the electrolyzer,

[0166] C compre is the daily cost of the compressor, including initial investment and operation and maintenance costs,

[0167] C h20 The daily cost of water consumption for hydrogen production in the system,

[0168] C gridThe cost of purchasing electricity from the grid.

[0169] In this embodiment, the rated power of the electrolytic cell is 40kW, and the cold standby and hot standby costs are tentatively set at 1.2 yuan / h and 3 yuan / h, respectively. A cold start takes 30 minutes, and the cost is tentatively set at 10 yuan / time.

[0170] Specifically, the total daily hydrogen production is obtained by the dynamic efficiency electrolyzer mathematical model and the electrolyzer hydrogen production efficiency model. The dynamic efficiency electrolyzer mathematical model formula is as follows:

[0171]

[0172] Where,

[0173] U el is the terminal voltage of the electrolysis chamber,

[0174] U rev is the reverse voltage of the electrolysis chamber,

[0175] r1 and r2 are the ohmic resistance parameters of the electrolyte,

[0176] A is the area of ​​the electrolysis module,

[0177] T is the temperature of the electrolytic cell,

[0178] s is the overvoltage,

[0179] t1, t2, t3 are overvoltage coefficients,

[0180] I el is the DC current of the electrolytic cell,

[0181] U rev0 is the reversible voltage under standard conditions, where T0 = 25°C, P0 = 1.01325 bar, and the value is 1.229 V.

[0182] R is the gas constant,

[0183] z is the number of electrons transferred per reaction, which is 2 for this reaction.

[0184] F is the Faraday constant, which is 96485C / mol.

[0185] The rated power of the alkaline water electrolysis cell used in this embodiment is 40k w , and the remaining parameter values ​​are as follows:

[0186] P elz，rated =40kW,N elz =280,A elz =0.25m 2 ,

[0187] u0=1.22V, z=2, r1=8.05×10 -5 Ωm 2

[0188] r2=-2.5×10 -7 Ωm 2 ℃, u1=0.185V,

[0189] t1=-1.002A -1 m 2 ,t2=8.424A -1 m 2 ℃,

[0190] t3=247.3A -1 m 2 ℃ 2 , T elz =80℃.

[0191] The electrolyzer hydrogen production efficiency model formula is as follows

[0192]

[0193] Where,

[0194] η F is the current efficiency,

[0195] I el is the DC current of the electrolytic cell

[0196] η v is the voltage efficiency,

[0197] U th It is the thermal neutral voltage, usually stable at 1.482V.

[0198] U e1 is the terminal voltage of the electrolytic cell,

[0199] η is the hydrogen production efficiency, which reflects the quality of hydrogen produced per unit input electrical energy.

[0200] After obtaining the specific hydrogen production efficiency, the hydrogen production amount is calculated based on the electrochemical theoretical formula for hydrogen production by water electrolysis, such as electron transfer.

[0201] In S3), the objective functions of the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model are both expressed by the following formula:

[0202]

[0203] Where,

[0204] M is the objective function value,

[0205] Ch2,min is the lowest daily unit hydrogen production cost,

[0206] C h2 is the daily unit hydrogen production cost,

[0207] V h2 is the total daily hydrogen production volume,

[0208] V h2,max is the maximum daily total hydrogen production volume,

[0209] w1~w2 are weight coefficients respectively.

[0210] In this example, we select the data for the entire day of January 1, 2016, and then calculate the Figure 3 The calculation scheduling optimization results shown in the process and the cost parameters of each equipment are shown in Table 1.

[0211] Table 1 Cost parameters of various equipment

[0212]

[0213] Taking into account the volatility of domestic electricity prices, we divide electricity prices into peak-time electricity prices, valley-time electricity prices, and parity prices. During valley-time and parity periods, priority is given to using the grid to supplement power shortages, while during peak and peak periods, priority is given to using battery power to supplement power shortages. The time-of-use electricity prices for commercial electricity in a certain place in China are shown in Table 2:

[0214] Table 2 Dynamic electricity prices for commercial electricity

[0215]

[0216]

[0217] S4) setting constraints for the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model, wherein the constraints include system power balance constraints, grid power constraints, battery power constraints, battery capacity constraints, and single electrolyzer input power constraints.

[0218] The system power balance constraint is:

[0219]

[0220] Where,

[0221] P pv is the output power of the photovoltaic power generation system,

[0222] P grid is the grid power,

[0223] P h2(i) is the hydrogen production power of the electrolyzer hydrogen production subsystem at the i-th time point,

[0224] P bat is the battery power;

[0225] The grid power constraint condition is:

[0226]

[0227] Where,

[0228] P grid is the grid power,

[0229] is the maximum grid power;

[0230] The battery power constraint condition is:

[0231]

[0232] Where,

[0233] P bat is the battery power,

[0234] is the maximum battery power;

[0235] The battery capacity constraint condition is:

[0236] 0≤SOC≤1

[0237] Where,

[0238] SOC is the battery constraint capacity;

[0239] The single electrolyzer input power constraint condition is:

[0240] P rate ≥P i,j ≥0.1P rate

[0241]

[0242] Where,

[0243] P i,j is the input power of the j-th electrolyzer at the i-th time point,

[0244] P rate is the rated power of a single electrolyzer,

[0245] ∑ is the sum,

[0246] N ele is the number of electrolytic cells,

[0247] P h2,i is the total hydrogen production power at the i-th time point.

[0248] Before performing scheduling optimization calculations on the system, it is necessary to first determine the various system configuration information. In this embodiment, the system configuration is set as shown in Table 3, and the volatility limit is set to 0.25.

[0249] Table 3 System configuration

[0250] Photovoltaic installed capacity 240kW Battery installed capacity 5.1kWh Electrolyzer installed capacity 200kW

[0251] S5) Under the constraints of step S4), the particle swarm algorithm is used to solve the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model of step S3) to obtain the optimal power allocation plan for each electrolyzer on that day.

[0252] Figure 3 This is the day-ahead / intra-day scheduling optimization process in the present invention.

[0253] The results obtained after model optimization calculation in this embodiment are shown in Table 4.

[0254] Table 4 Scheduling optimization calculation results

[0255]

[0256]

[0257] The present invention proposes a day-ahead / intraday scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction. It considers using ultra-short-term light power prediction technology to suppress hydrogen power fluctuations, and completes the optimal power allocation plan for each electrolyzer on the day by constructing a day-ahead long-term scheduling optimization model and an intraday short-term scheduling optimization model for the photovoltaic hydrogen production system, thereby realizing real-time regulation of the electrolyzer power and reducing the daily operating cost.

[0258] 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 the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic forecasting, 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 electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem, each of which is connected to a DC bus; S2) Establish an energy management strategy for the photovoltaic hydrogen production system to suppress the fluctuation of hydrogen production power; S21) Using the predicted input power of the photovoltaic power generation subsystem in the future, adjusting the output power of the photovoltaic power generation subsystem at the current moment 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, P pv,st is the target value of the photovoltaic power generation system output power at time t, P pv,t is the output power of the photovoltaic power generation system at time t, 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, w1~w8 are weight coefficients; S22) constructing a real-time fluctuation rate of the photovoltaic power generation subsystem based on the adjustment target value of the output power of the photovoltaic power generation subsystem; S23) Setting the hydrogen production power fluctuation rate limit K fm , compare the real-time fluctuation rate of the photovoltaic power generation system with the hydrogen production power fluctuation rate limit K fm , respectively formulate the first energy management strategy and the second energy management strategy of the photovoltaic hydrogen production system to achieve the stabilization of the hydrogen production power of the electrolyzer, thereby determining the final hydrogen production power of the electrolyzer; S3) Taking the system daily operating cost and the photovoltaic absorption rate as target parameters, determine the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the optimal conditions, first construct a day-ahead long-term scheduling optimization model for the photovoltaic hydrogen production system, and then construct an intraday short-term scheduling optimization model for the photovoltaic hydrogen production system; The daily unit hydrogen production cost is expressed by the following formula C sys =∑C pv +C bat +C ele +C st +C compre +C h2o +C grid Where, C h2 is the daily unit hydrogen production cost, C sys The daily cost of the system, including initial investment and operation and maintenance costs, C o2 is the daily unit oxygen production cost, V h2,t is the volume of hydrogen produced at time t, ∑ is the sum, C pv is the daily cost of photovoltaic modules, including initial investment and operation and maintenance costs, C bat is the daily cost of the battery, including initial investment and operation and maintenance costs, C ele is the daily cost of the electrolyzer, including initial investment and operation and maintenance costs, C st is the start-up and shutdown cost, cold standby cost and hot standby cost of the electrolyzer, C compre is the daily cost of the compressor, including initial investment and operation and maintenance costs, C h20 The daily cost of water consumption for hydrogen production in the system, C grid the cost of purchasing electricity for the grid; S4) setting constraints for the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model, wherein the constraints include system power balance constraints, grid power constraints, battery power constraints, battery capacity constraints, and single electrolyzer input power constraints; S5) Under the constraints of step S4), the particle swarm algorithm is used to solve the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model of step S3) to obtain the optimal power allocation plan for each electrolyzer on that day.

2. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 1, characterized in that: In S22), the real-time fluctuation rate of the photovoltaic power generation system is calculated by the following formula: Where, K_fluc is the real-time fluctuation rate of the photovoltaic power generation system, P pv,st is the target value of the photovoltaic power generation system output power at time t, P pv,t is the output power of the photovoltaic power generation system at time t.

3. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 2, characterized in that: In S23), if the predicted next-day solar power is sufficient, the system operates on that day using the first energy management strategy; if the predicted next-day solar power is insufficient or seriously insufficient, the system operates on that day using the second energy management strategy.

4. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 3 is characterized in that: In S23), in the first energy management strategy, 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 electrolyzer; when the battery SOC < bat_i, the battery charges to reduce the hydrogen production power of the 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; 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 ; 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 .

5. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 3 is characterized in that: In S23), in the second energy management strategy, when -K fm ≤K_fluc≤K fm When the grid power is sufficient, the grid discharges to K_fluc=K fm If the grid power is insufficient, the battery discharges or the grid purchases electricity to supplement power regulation until K_fluc=K fm ; 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 ; When K_fluc<-K fm When the grid power is sufficient, the grid discharges to K_fluc=K fm If the grid power is insufficient, the battery discharges or the grid purchases electricity to supplement power regulation until K_fluc=K fm .

6. The method for optimizing the day-ahead / intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 1, characterized in that: In S3), the total daily hydrogen production is obtained by the dynamic efficiency electrolyzer mathematical model and the electrolyzer hydrogen production efficiency model. The dynamic efficiency electrolyzer mathematical model formula is as follows: Where, U el is the terminal voltage of the electrolysis chamber, U rev is the reverse voltage of the electrolysis chamber, r1 and r2 are the ohmic resistance parameters of the electrolyte, A is the area of ​​the electrolysis module, T is the temperature of the electrolytic cell, s is the overvoltage, t1, t2, t3 are overvoltage coefficients, I el is the DC current of the electrolytic cell, U rev0 is the reversible voltage under standard conditions, where T0 = 25°C. P0=1.01325bar, R is the gas constant, z is the number of electrons transferred per reaction, F is the Faraday constant; The electrolyzer hydrogen production efficiency model formula is as follows Where, η F is the current efficiency, I el is the DC current of the electrolytic cell, η v is the voltage efficiency, U th is the thermal neutral voltage, U el is the terminal voltage of the electrolyzer, and η is the hydrogen production efficiency.

7. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 6, characterized in that: In S3), the objective functions of the day-ahead long-term scheduling optimization model and the intraday short-term scheduling optimization model are both expressed by the following formula: Where, M is the objective function value, C h2,min is the lowest daily unit hydrogen production cost, C h2 is the daily unit hydrogen production cost, V h2 is the total daily hydrogen production volume, V h2,max is the maximum daily total hydrogen production volume, w1~w2 are weight coefficients respectively.

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 system power balance constraint is Where, 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 electrolyzer hydrogen production subsystem at the i-th time point, P bat is the battery power; The grid power constraint condition is: Where, P grid is the grid power, is the maximum grid power; The battery power constraint condition is: Where, P bat is the battery power, is the maximum battery power; The battery capacity constraint condition is 0≤SOC≤1 Where, SOC is the battery constraint capacity; The single electrolyzer input power constraint condition is: P rate ≥P i,j ≥0.1P rate Where, P i,j is the input power of the jth electrolytic cell at the i-th time point, P rate is the rated power of a single electrolyzer, ∑ is the sum, N ele is the number of electrolytic cells, P h2,i is the total hydrogen production power at the i-th time point.

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