Photovoltaic prediction-based day-ahead / intra-day scheduling optimization method for photovoltaic hydrogen production system

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; the predicted input power of the photovoltaic power generation subsystem in a future period of time is utilized, an energy management strategy of the photovoltaic hydrogen production system is established, and the fluctuation of the hydrogen production power is inhibited; the method comprises the following steps: determining the lowest daily unit hydrogen production cost and the highest daily total hydrogen production amount under the optimal condition by taking the system daily operation cost and the photovoltaic absorption rate as target parameters, firstly constructing a day-ahead long-term scheduling optimization model of the photovoltaic hydrogen production system, and then constructing an intra-day short-term scheduling optimization model of the photovoltaic hydrogen production system; and setting constraint conditions of the day-ahead long-term scheduling optimization model and the intra-day short-term scheduling optimization model, and solving the optimization models by using a particle swarm optimization algorithm to obtain a current-day optimal power distribution scheme of each electrolytic cell. The current-day optimal power distribution scheme of all the electrolytic cells is completed, real-time regulation and control of the power of the electrolytic cells are achieved, and the current-day operation cost is reduced.

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 day-ahead / intra-day scheduling of 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 the focus of international attention. As one of the most abundant and cleanest renewable energy sources, solar photovoltaic power generation technology has made significant progress in recent years and has been widely used around the world. However, the intermittent and volatile nature of photovoltaic power generation has brought challenges to the stable operation of the power grid, and also limited its direct application in areas that require a stable energy supply, such as large-scale industrial production and energy storage.

[0003] Photovoltaic hydrogen storage can effectively deal with the above problems. It converts the electricity generated by photovoltaic power generation into hydrogen energy, realizing the indirect storage and efficient utilization of solar energy. As a clean and efficient energy carrier, hydrogen energy has a wide range of application prospects, including transportation, industrial raw materials, distributed energy systems and other fields. Therefore, the optimized operation of photovoltaic hydrogen production systems is of great significance to promoting energy transformation and achieving green and low-carbon development.

[0004] In the operation of photovoltaic hydrogen production system, day-ahead / day-intraday scheduling optimization is a key link. Through reasonable scheduling strategy, the charging and discharging plan of energy storage device is considered to balance the energy supply and demand in the system and improve the overall economy and reliability of the system.

[0005] At present, the existing day-ahead / intra-day scheduling optimization methods have the following problems:

[0006] 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; less consideration is given to actively adjusting the operating power of the electrolyzer based on this, which will affect the service life of the electrolyzer in the long run.

[0007] Second, the impact of dynamic electricity prices on the economic operation of the system 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 view of the shortcomings of the prior art, the present invention proposes a day-ahead / intra-day scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction, which can make full use of 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 object, the present invention designs a method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction, which is particularly characterized in that it includes 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 respectively connected to a DC bus;

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

[0013] 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

[0014]

[0015] In the formula,

[0016] P pv,st is the target value of the output power of the photovoltaic power generation system 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] w 1 ~w 8 All are weight coefficients;

[0020] 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;

[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 operation cost and the photovoltaic absorption rate as the target parameters, the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the best conditions are determined, and the day-ahead long-term scheduling optimization model of the photovoltaic hydrogen production system is first constructed, and then the intraday short-term scheduling optimization model of the photovoltaic hydrogen production system is constructed;

[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] In the formula,

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

[0028] C sys is 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 hydrogen production volume at time t,

[0031] ∑ is the sum,

[0032] C pv is the daily cost of a photovoltaic module, 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 are the start-up and shutdown costs, cold standby costs, and hot standby costs 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 individual electrolyzers.

[0040] S5) Under the constraint conditions in step S4), use the particle swarm optimization algorithm to solve the long-term day-ahead scheduling optimization model and the short-term intra-day scheduling optimization model in step S3) to 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 adjustment 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] Even further, in S23), if the predicted light power for the next day is sufficient, the system operates on the first energy management strategy on the current day; if the predicted light power for the next day is insufficient or severely insufficient, the system operates on the second energy management strategy on the current day.

[0048] Even further, in S23), in the first energy management strategy, 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 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 generation 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 for supplementary 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 for supplementary power regulation until K_fluc = K fm .

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

[0055]

[0056] In the formula,

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

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

[0059] r 1 ,r 2 are the electrolyte ohmic resistance parameters,

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

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

[0062] s is the overvoltage,

[0063] t 1 , t 2 , t 3 are the overvoltage coefficients,

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

[0065] U rev0 is the reversible voltage under standard conditions, where T 0 =25℃,

[0066] P 0 =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] In the formula,

[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 cell terminal voltage,

[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] In the formula,

[0082] M is the objective function value,

[0083] C h2,minis the minimum 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] w 1 ~w 2 are weight coefficients respectively.

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

[0089]

[0090] In the formula,

[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] In the formula,

[0097] P grid is the grid power,

[0098] is the maximum grid power;

[0099] The battery power constraint condition is:

[0100]

[0101] In the formula,

[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] In the formula,

[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] In the formula,

[0112] P i,j is the input power of the jth 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 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;

[0119] 2. The present invention combines the energy management strategy of the system with the day-ahead / intraday optimization scheduling method of the system. The day-ahead scheduling is a long-term hourly-scale scheduling made 24 hours in advance, taking into account multiple energy flexibility constraints, and generating an hourly-level day-ahead operation plan for each equipment unit for the next day; the intraday scheduling is a short-term 15-minute-scale scheduling made 2 hours in advance, which provides the system with flexible operating margins, stabilizes power fluctuations by adjusting the output of each device, and plays a key role in daily schedules at work;

[0120] The day-ahead / intraday scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction 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 achieving real-time control of the electrolyzer power and reducing the daily operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 is a 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 in conjunction with the accompanying drawings and specific embodiments.

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

[0126] 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, which are respectively connected to a DC bus.

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

[0128] 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

[0129]

[0130] In the formula,

[0131] P pv,st is the target value of the output power of the photovoltaic power generation system 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] w 1 ~w 8 are all weight coefficients.

[0135] P pv,t+1 ~P pv,t+8 is 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 can fully meet the requirements. 1 ~w 8 They 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 through 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] In the formula,

[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 output power of the photovoltaic power generation system 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 is often insufficient during sunrise and sunset, and fully charged at noon, resulting in the waste of surplus light power at noon.

[0145] Based on the comprehensive consideration of light power prediction and hydrogen load, we propose energy management strategies for two scenarios. If the predicted light power for the next day is sufficient, the system adopts the first energy management strategy for operation on the current day; if the predicted light power for the next day is insufficient or severely insufficient, the system adopts the second energy management strategy for operation on the current day.

[0146] As Figure 1 shown, in the first energy management strategy, 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 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;

[0147] 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 curtailment power until K fluc = K fm ;

[0148] 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 for supplementary power regulation until K_fluc = -K fm .

[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 battery discharges or power is purchased from the grid for supplementary power regulation until K_fluc = K fm ;

[0150] 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 curtailment power until K fluc = K fm ;

[0151] 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 for supplementary power regulation until K_fluc = K fm .

[0152] S3) Taking the system daily operating cost and photovoltaic consumption rate as target parameters, determine the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the optimal conditions, first build a day-ahead long-term scheduling optimization model for the photovoltaic hydrogen production system, and then build an intraday short-term scheduling optimization model for the photovoltaic hydrogen production system.

[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] In the formula,

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

[0158] C sys is 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 hydrogen production volume at time t,

[0161] ∑ is the sum,

[0162] C pv is the daily cost of a photovoltaic module, 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 stare the start-up and shutdown costs, cold standby costs, and hot standby costs 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 grid The cost of purchasing electricity from the grid.

[0169] In this embodiment, the rated power of the electrolytic cell is 40kW, and the cost of cold standby and hot standby is 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 a dynamic efficiency electrolyzer mathematical model and an electrolyzer hydrogen production efficiency model. The dynamic efficiency electrolyzer mathematical model formula is as follows:

[0171]

[0172] In the formula,

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

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

[0175] r 1 ,r 2 are the electrolyte ohmic resistance parameters,

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

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

[0178] s is the overvoltage,

[0179] t 1 , t 2 , t 3 are the overvoltage coefficients,

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

[0181] U rev0 is the reversible voltage under standard conditions, where T 0 =25℃, P 0 =1.01325bar, value 1.229V,

[0182] R is the gas constant,

[0183] z is the number of electron transfers 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] u 0 =1.22V, z=2, r 1 =8.05×10 -5 Ωm 2

[0188] r 2 =-2.5×10 -7 Ωm 2 ℃,u 1 =0.185V,

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

[0190] t 3 =247.3A -1 m 2 ℃ 2 , T elz =80℃.

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

[0192]

[0193] In the formula,

[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 cell terminal voltage,

[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] In the formula,

[0204] M is the objective function value,

[0205] C h2,min is the minimum 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] w 1 ~w 2 are weight coefficients respectively.

[0210] In this example, we select the data for the entire day of January 1, 2016, and then use this data as the basis for calculation. 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 each 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. 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:

[0214] Table 2 Dynamic electricity prices for commercial use

[0215]

[0216]

[0217] S4) setting constraints for the day-ahead long-term dispatch optimization model and the intraday short-term dispatch 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] In the formula,

[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] In the formula,

[0228] P grid is the grid power,

[0229] is the maximum grid power;

[0230] The battery power constraint condition is:

[0231]

[0232] In the formula,

[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] In the formula,

[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] In the formula,

[0243] P i,j is the input power of the jth 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 calculation on the system, it is necessary to first determine the various configuration information of the system. 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 day-ahead / intraday scheduling optimization method for a photovoltaic hydrogen production system based on photovoltaic prediction 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 achieving real-time control 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 equivalent replacement methods and are included in the protection scope 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 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 electrolyzer hydrogen production subsystem, and a hydrogen compressor subsystem respectively connected to a DC bus; S2) Establish an energy management strategy for the photovoltaic hydrogen production system to suppress the volatility of hydrogen production power; 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 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 by adjusting the output power target value 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 operation cost and the photovoltaic absorption rate as the target parameters, the minimum daily unit hydrogen production cost and the maximum daily total hydrogen production under the best conditions are determined, and the day-ahead long-term scheduling optimization model of the photovoltaic hydrogen production system is first constructed, and then the intraday short-term scheduling optimization model of the photovoltaic hydrogen production system is constructed; 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 In the formula, C h2 is the daily unit hydrogen production cost, C sys is 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 hydrogen production volume at time t, ∑ is the sum, C pv is the daily cost of a photovoltaic module, 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 are the start-up and shutdown costs, cold standby costs, and hot standby costs 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 the constraint conditions of the day-ahead long-term dispatch optimization model and the intraday short-term dispatch optimization model, wherein the constraint conditions include system power balance constraint conditions, grid power constraint conditions, battery power constraint conditions, battery capacity constraint conditions, and single electrolyzer input power constraint conditions; 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 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 the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 2 is characterized in that: In S23), if the predicted next-day light power is sufficient, the system operates on that day using the first energy management strategy; if the predicted next-day light 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: 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 for supplementary 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 for supplementary power regulation until K_fluc = K fm .

6. The method for optimizing the day-ahead / day-intraday scheduling of a photovoltaic hydrogen production system based on photovoltaic prediction according to claim 1 is characterized in that: S3), the total daily hydrogen production is obtained by a dynamic efficiency electrolyzer mathematical model and an electrolyzer hydrogen production efficiency model, and the dynamic efficiency electrolyzer mathematical model formula is as follows In the formula, 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 electrolytic cell DC current, U rev0 is the reversible voltage under standard conditions, wherein the standard condition is 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 In the formula, η F is the current efficiency, I el is the electrolytic cell DC current, 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 is 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: In the formula, M is the objective function value, C h2,min is the minimum 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 photovoltaic hydrogen production system capacity configuration optimization method based on light power prediction according to claim 7 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 electrolyzer hydrogen production subsystem at the i-th time point, P bat is the battery power; The grid power constraint condition is: In the formula, P grid is the grid power, is the maximum grid power; The battery power constraint condition is: In the formula, P bat is the battery power, is the maximum battery power; The battery capacity constraint condition is 0≤SOC≤1 In the formula, SOC is the battery constraint capacity; The single electrolyzer input power constraint condition is: P rate ≥P i,j ≥0.1P rate In the formula, P i,j is the input power of the jth electrolyzer 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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