Day-ahead scheduling method and system for wind-solar coupled hydrogen production system

Abstract

The invention relates to a day-ahead scheduling method and system for a wind-solar coupled hydrogen production system, and belongs to the field of wind-solar hydrogen storage and production. The method comprises the following steps: performing day-ahead prediction according to historical data and meteorological data of the wind-solar power generation system to obtain day-ahead prediction data of the wind-solar power generation system; constructing a day-ahead scheduling optimization model based on the day-ahead prediction data, wherein the day-ahead scheduling optimization model comprises an objective function and a constraint function of day-ahead scheduling optimization; the objective function comprises a minimum cost function, and the constraint function comprises a power balance constraint, a wind and light power generation operation constraint, an energy storage charge state constraint, a hydrogen production and storage operation constraint and a power grid interaction constraint; and solving the day-ahead scheduling optimization model to obtain day-ahead optimization scheduling data of the wind-solar coupled hydrogen production system. In order to solve the problem that the wind-solar coupled hydrogen production system is high in operation cost, the day-ahead scheduling optimization strategy based on the time-of-use electricity price is adopted to obtain additional benefits, so that the cost is remarkably reduced, and the economical efficiency of system operation is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wind-solar-storage hydrogen production, and particularly relates to a day-ahead scheduling method and system for a wind-solar coupling hydrogen production system. BACKGROUND

[0002] Under the background of global response to climate change, achieving the goals of carbon peak and carbon neutrality has become a major strategic task of China, which is related to the overall situation of sustainable development of economic society. Striving to explore and develop new clean energy not only can promote the rapid realization of the goals of carbon peak and carbon neutrality, but also can promote the optimization and upgrading of new energy industry structure. At present, the energy industry is accelerating the transformation towards low-carbon, green and clean direction, and renewable energy such as wind power and photovoltaic develops rapidly. At the same time, hydrogen energy, as a kind of green energy with abundant reserves, large energy density and wide sources, has become an ideal new energy and energy storage medium, which can produce hydrogen through new energy such as wind power and photovoltaic, so as to build a complete hydrogen energy industry ecology, which can not only guarantee the energy security of China and enhance the stability and autonomy of energy supply, but also can accelerate the construction of clean and low-carbon hydrogen energy supply system and promote the development of energy structure, which has great significance for the comprehensive implementation of China's sustainable development strategy.

[0003] At present, the scheduling of wind-solar coupling hydrogen production system is mainly divided into day-ahead scheduling of long time scale and day-ahead scheduling of short time scale. The day-ahead scheduling is to predict the next day's new energy output, load demand and power grid price, and reasonably arrange new energy power generation plan, energy storage device charging and discharging plan, system and power grid power interaction plan in advance, so as to improve the utilization rate of power equipment and the overall operation benefit of power system. SUMMARY

[0004] Embodiments of the application aim to provide a day-ahead scheduling method and system for a wind-solar coupling hydrogen production system, which can solve the technical problem of high operation cost of the existing wind-solar coupling hydrogen production system.

[0005] In one aspect, the application provides a day-ahead scheduling method for a wind-solar coupling hydrogen production system, wherein the wind-solar coupling hydrogen production system comprises a wind-solar power generation device, an energy storage device, a hydrogen production device, a hydrogen storage device and a power grid interaction device; the method comprises:

[0006] day-ahead prediction based on historical data and meteorological data of the wind-solar coupling hydrogen production system to obtain day-ahead prediction data of the wind-solar power generation system;

[0007] constructing a day-ahead scheduling optimization model based on the day-ahead prediction data, wherein the day-ahead scheduling optimization model comprises a target function and a constraint function of day-ahead scheduling optimization; the target function comprises a minimum cost function, and the constraint function comprises a power balance constraint, a wind-solar power generation operation constraint, an energy storage state of charge constraint, a hydrogen production and storage operation constraint and a power grid interaction constraint;

[0008] The day-ahead scheduling optimization model is solved by using a mixed integer linear programming improved algorithm based on variable aggregation to obtain day-ahead optimization scheduling data of the wind-solar coupling hydrogen production system.

[0009] In another aspect, the application provides a wind-solar coupling hydrogen production system day-ahead scheduling system, comprising:

[0010] A data prediction module is configured to perform day-ahead prediction according to historical data and meteorological data of the wind-solar power generation system to obtain day-ahead prediction data of the wind-solar power generation system.

[0011] A model construction module is configured to construct a day-ahead scheduling optimization model based on the day-ahead prediction data, wherein the day-ahead scheduling optimization model comprises a target function and a constraint function of day-ahead scheduling optimization; the target function comprises a minimum cost function, and the constraint function comprises power balance constraints, wind-solar power generation operation constraints, energy storage state of charge constraints, hydrogen production and storage operation constraints and grid interaction constraints.

[0012] An optimization solving module is configured to solve the day-ahead scheduling optimization model by using a mixed integer linear programming improved algorithm based on variable aggregation to obtain day-ahead optimization scheduling data of the wind-solar coupling hydrogen production system.

[0013] Compared with the prior art, the embodiments of the application can at least achieve one of the following beneficial effects:

[0014] The embodiments of the application adopt a system and grid power interaction strategy based on time-of-use price fluctuation, utilize the time-of-use price difference of the grid, and combine the power state and operation condition of the wind-solar coupling hydrogen production system to determine a power coordination operation scheme in each time period of low valley, flat section and peak, thereby effectively reducing the operation cost of the wind-solar coupling hydrogen production system.

[0015] The day-ahead scheduling optimization model constructed in the embodiments of the application takes the operation and maintenance cost of wind power, photovoltaic, energy storage and hydrogen production device, the cost of system and grid interaction and the hydrogen production purchase electricity benefit as a target function, and comprehensively considers power balance, safe operation, subsystem and grid interaction in a constraint function, thereby ensuring the reliable operation of the wind-solar coupling hydrogen production system, effectively reducing the calculation amount and improving the calculation accuracy.

[0016] The embodiments of the application solve the model by using a mixed integer linear programming improved method based on variable aggregation, which can effectively improve the solving efficiency of the day-ahead scheduling optimization model while maintaining the feasibility and effectiveness of the operation cost of the wind-solar coupling hydrogen production system.

[0017] The embodiments of the application can effectively improve the service life and operation safety of the hydrogen production device and reduce investment by using the array shift optimization control strategy of the hydrogen production device.

[0018] The technical solutions in the present application can be combined with each other to realize more preferred combination solutions. Other features and advantages of the present application will be described in the following description, and some advantages will become apparent from the description, or will be understood by those skilled in the art through implementation of the present application. The purposes and other advantages of the present application can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0020] Figure 1 A flow chart of a day-ahead scheduling method of a wind-solar coupling hydrogen production system according to an embodiment of the present application.

[0021] Figure 2 A structure schematic diagram of a wind-solar coupling hydrogen production system according to an embodiment of the present application.

[0022] Figure 3 A 24-hour wind power and photovoltaic power generation prediction diagram according to an embodiment of the present application.

[0023] Figure 4 A hydrogen production device predicted load diagram according to an embodiment of the present application.

[0024] Figure 5 An electrolytic cell operation condition schematic diagram according to an embodiment of the present application.

[0025] Figure 6 An electrolytic cell array circulation schematic diagram according to an embodiment of the present application.

[0026] Figure 7 A multi-condition electrolytic cell array shift strategy schematic diagram according to an embodiment of the present application.

[0027] Figure 8 A 24-hour power grid time-of-use electricity price diagram according to an embodiment of the present application.

[0028] Figure 9 A system electricity purchase and sale and energy storage charge and discharge change comparison diagram according to an embodiment of the present application.

[0029] Figure 10 An energy storage state of charge change curve diagram according to an embodiment of the present application.

[0030] Figure 11 An energy storage state of charge change curve diagram of a comparative scheme according to an embodiment of the present application.

[0031] Figure 12 A hydrogen production system operation power comparison diagram according to an embodiment of the present application.

[0032] Figure 13 A hydrogen production amount comparison chart for a low wind and solar power generation period of an embodiment of the present application.

[0033] Figure 14 A cost comparison chart for a scheduling scheme obtained in each period of an embodiment of the present application.

[0034] Figure 15 A total cost comparison chart for a scheduling scheme obtained in an embodiment of the present application.

[0035] Figure 16 An electrolyzer power change curve of an embodiment of the present application.

[0036] Figure 17 An electrolyzer power change curve of a comparison scheme of an embodiment of the present application.

[0037] Figure 18 An electrolyzer operation condition comparison chart of an embodiment of the present application.

[0038] Figure 19 A composition schematic diagram of a day-ahead scheduling system of a wind-solar coupling hydrogen production system of an embodiment of the present application. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present application will be described in detail below with reference to the drawings, which form a part of this application. The drawings and the associated descriptions are provided to illustrate the preferred embodiments of the present application and to provide a comprehensible description of the principles of the present application. Therefore, it will be apparent to those skilled in the art that the present application can be practiced without the specific details presented.

[0040] In order to solve the problem of high operation cost of a wind-solar coupling hydrogen production system, the present application proposes a day-ahead scheduling method and device for a wind-solar coupling hydrogen production system, based on a system and power grid power interaction strategy of time-of-use price fluctuation, using high peak electricity price electricity sales and low valley electricity price electricity purchase to produce hydrogen to obtain additional income, thereby effectively reducing the operation cost of the system.

[0041] Figure 1 is a flow chart of a day-ahead scheduling method for a wind-solar coupling hydrogen production system according to an embodiment of the present application. As shown in Figure 1 The day-ahead scheduling method for a wind-solar coupling hydrogen production system in the embodiment of the present application includes the following steps S101-S103.

[0042] Step S101, according to the historical data and meteorological data of the wind-solar power generation system, day-ahead prediction is performed to obtain day-ahead prediction data of the wind-solar power generation system.

[0043] Specifically, the structure of the wind-solar coupling hydrogen production system is as shown in Figure 2As shown, according to the system control link, it is divided into multiple subsystems, including wind and light power generation devices (photovoltaic units and wind turbine units), energy storage devices, hydrogen production devices, hydrogen storage devices and grid interaction devices. The system integrates the electric energy of photovoltaic power generation, wind power generation and energy storage batteries, converts into alternating current through AC / DC, DC / DC, DC / AC converters and enters the grid, and converts into direct current through DC / DC and AC / DC converters to supply power to the hydrogen production device. The hydrogen production device generates hydrogen through water electrolysis reaction and stores it in the hydrogen storage device. The whole system realizes the collaborative utilization and conversion of multiple energies, and comprehensively utilizes multiple energies.

[0044] It can be understood that day-ahead prediction is a commonly used short-term prediction method in power systems, which uses a deep learning model (such as a neural network) to combine historical data and weather data to predict the power load and / or power generation of the next day (usually the next day) in the future. Generally, it is completed one day before operation, with a time scale of 15 minutes or 1 hour, generating 96 or 24 prediction points.

[0045] Specifically, in the embodiment and some embodiments of the application, the input data of the day-ahead prediction includes the power generation data of the wind-solar coupling hydrogen production system and the 24-hour weather forecast data, and the day-ahead prediction has a prediction period of 24 hours and a time scale of 1 hour. The day-ahead prediction data obtained by prediction includes wind power prediction, photovoltaic power prediction and hydrogen production device load prediction. As shown in the detailed data of a certain wind-solar coupling hydrogen production system day-ahead 24-hour prediction Figure 3 and Figure 4 as shown.

[0046] Step S102, constructing a day-ahead scheduling optimization model based on the day-ahead prediction data, the day-ahead scheduling optimization model including a day-ahead scheduling optimization objective function and a constraint function; the objective function includes a minimum cost function, and the constraint function includes a power balance constraint, a wind and light power generation operation constraint, an energy storage state of charge constraint, a hydrogen production and storage operation constraint and a grid interaction constraint.

[0047] Specifically, in the embodiment and some embodiments of the application, the minimum cost function includes the operation and maintenance cost of the wind turbine unit, the photovoltaic unit, the energy storage device and the hydrogen production device, the system and the grid interaction cost, and the hydrogen production purchase electricity benefit, which is calculated according to the following formula:

[0048]

[0049] Wherein, F min represents the minimum value of the operation cost of the day-ahead scheduling; W dg (t) represents the operation and maintenance cost of the wind-solar coupling hydrogen production system at t period; W batt (t) represents the operation and maintenance cost of the energy storage device at t period; W ap(t) represents the operation and maintenance cost of the hydrogen production device at period t; W grid (t) represents the interaction cost of the wind-solar coupling hydrogen production system with the power grid at period t, the interaction cost includes the electricity purchase cost and the electricity sale benefit, the electricity purchase cost is positive, and the electricity sale benefit is negative; W hyd (t) represents the benefit obtained by the wind-solar coupling hydrogen production system from purchasing electricity from the power grid to produce hydrogen and selling the hydrogen produced at period t.

[0050] Further, in the embodiments of the present embodiment and some embodiments of the present application,

[0051]

[0052] wherein Z pv and P pv (t) respectively represent the operation and maintenance coefficient and the photovoltaic power generation power of the photovoltaic unit in the wind-solar power generation device at time t, Z wind and P wind (t) respectively represent the operation and maintenance coefficient and the wind power generation power of the wind turbine in the wind-solar power generation device at time t; Z batt and P batt (t) respectively represent the operation and maintenance coefficient and the output of the energy storage device at time t; Z ap and P hyd (t) respectively represent the operation and maintenance coefficient and the hydrogen production power of the hydrogen production device at time t; J price (t) represents the electricity price of the power grid at time t, P grid (t) represents the interaction power of the wind-solar coupling hydrogen production system with the power grid at time t, a negative number represents electricity sale, and a positive number represents electricity purchase off-grid power; J hyd (t) represents the hydrogen price at time t, S hyd (t) represents the amount of hydrogen sold at time t.

[0053] Further, the hydrogen production device includes an ALK electrolytic cell (alkaline electrolytic cell) and a PEM electrolytic cell (proton exchange membrane electrolytic cell), W ap (t) = Z ap-ALK × P ALK (t) + Z ap-PEM × P PEM (t), Z ap-ALK and P ALK (t) respectively represent the operation and maintenance coefficient and the operation power of the ALK electrolytic cell at period t; Z ap-PEM and P PEM (t) respectively represent the operation and maintenance coefficient and the operation power of the PEM electrolytic cell at period t.

[0054] Specifically, in the embodiments of the present embodiment and some embodiments of the present application, the power balance constraint is: P hyd (t) = Pwind (t) + P pv (t) - P batt (t) + P grid (t). When the hydrogen production device comprises an ALK electrolyzer and a PEM electrolyzer, P hyd (t) = P ALK (t) + P PEM (t).

[0055] Specifically, in the embodiment and some embodiments of the application, the wind-solar power generation operation constraint is:

[0056]

[0057] wherein, Pmin(t) and Pmax(t) represent the minimum and maximum values of the output power of the wind turbine, respectively; Pmin(t) and Pmax(t) represent the minimum and maximum values of the output power of the photovoltaic unit, respectively; Pmin(t) and Pmax(t) represent the minimum and maximum values of the charging power of the energy storage device, respectively; Pmin(t) and Pmax(t) represent the minimum and maximum values of the discharging power of the energy storage device, and P batt (t) < 0 when charging, and P batt (t) > 0 when discharging.

[0058] Specifically, in the embodiment and some embodiments of the application, the energy storage state of charge constraint is:

[0059]

[0060] wherein, SOC(t) and SOC(t+1) represent the state of charge of the energy storage device at time period t and t+1, respectively; η ch represents the charging and discharging efficiency of the energy storage device; E ess represents the rated capacity of the energy storage device; SOC min and SOC max represent the lower and upper limits of the state of charge of the energy storage device, respectively.

[0061] Specifically, in the embodiment and some embodiments of the application, the hydrogen production operation constraint is:

[0062]

[0063] wherein, and represent the lower and upper limits of the hydrogen storage state of the hydrogen storage device, respectively, and represent the minimum and maximum values of the operating power of the hydrogen storage device, respectively.

[0064] Furthermore, when the hydrogen production unit includes an ALK electrolyzer and a PEM electrolyzer, the hydrogen production and storage operation constraints are as follows:

[0065]

[0066] in, These represent the minimum and maximum operating power of the ALK electrolytic cell, respectively. These represent the minimum and maximum operating power of the PEM electrolyzer, respectively.

[0067] Furthermore, the hydrogen production and storage operation constraints also include the rotation optimization constraints for the hydrogen production unit array.

[0068] Before detailing the constraints of the rotation optimization for the hydrogen production unit array, some relevant definitions will be explained. Figure 5 As shown, in this embodiment of the invention, binary variables are used to represent the state of the hydrogen production unit: R represents the rated operating condition, G represents the fluctuating operating condition, M represents the standby condition, and D represents the shutdown condition; and start / stop variables are introduced: start-up Y, shutdown Z. The operating condition subscripts n and t are used to identify the hydrogen production unit n at time t, for example, R n,t This is a binary variable representing the hydrogen production unit n at time t under rated operating conditions. It takes the value 1 when it is under rated operating conditions and 0 otherwise, and so on.

[0069] The input power and corresponding state variables of the hydrogen production unit under the four operating conditions are represented as follows:

[0070]

[0071] In the formula: P represents the input power of the hydrogen production unit n at time t, in MW. r and P s These are the rated power and standby power of the hydrogen production unit, respectively. The above formula can be expressed as: P G The input power under fluctuating operating conditions is 0.2G. n,t P r <P G <G n,t P r , where R n,t M n,t and G n,t These represent the state variables of the nth hydrogen production unit under rated operating conditions, standby operating conditions, and fluctuating operating conditions, respectively. A value of 1 indicates the unit is operating under the corresponding condition, and a value of 0 indicates the unit is operating under the corresponding condition. When there are N hydrogen production units, the total input power of the N hydrogen production units operating in combination is...

[0072] In this embodiment of the invention, the hydrogen production unit array rotation optimization constraint implements power allocation based on the hydrogen production unit number, and schedules the power consumption of the corresponding numbered hydrogen production unit according to real-time wind power. The rotation cycle threshold T... min The setting is determined based on the operating characteristics of the hydrogen production unit, and its value shall not exceed the minimum value of the allowable downtime, continuous fluctuating power operation limit, and continuous sub-safe power operation duration of the hydrogen production unit.

[0073] like Figure 6 As shown, the hydrogen production units are rotated using a numbered cyclic queue mechanism. Assume that each numbered hydrogen production unit has preset operating conditions in the initial state, and when the operating time reaches a threshold T... min At that time, the corresponding number for each operating condition is incremented by 1 (number 4 is reset to 1 after rotation). For example, shutdown is 1, standby is 2, fluctuation is 3 and rated is 4. The hydrogen production unit in each state is rotated in an orderly manner according to a predetermined strategy.

[0074] For example, the rotation optimization constraint is illustrated with a group of 4 hydrogen production units, where other groups of hydrogen production units operate under the same conditions. Assume there are n hydrogen production units rotating in shifts, divided into n / 4 groups, with 4 units in each group, and each unit having a rated power of P. r The actual power is Where n is the number of the hydrogen production unit. The power required to be consumed by each hydrogen production unit array is P. elen The hydrogen production unit operates under the four conditions described above.

[0075] Figure 7 The diagram shows the round-robin optimization constraints for the hydrogen production unit array under multiple operating conditions. The round-robin optimization constraints for the hydrogen production unit array are:

[0076] (1) Scenario 1: High power operation

[0077] Under these operating conditions, 3P r ≤P elen ≤4P r Furthermore, each group of three hydrogen production units is operating at its rated capacity, while the other unit is operating under fluctuating power conditions.

[0078]

[0079] In the formula, P represents the actual power of the hydrogen production unit numbered n in each group; elen P represents the power required to be consumed by each hydrogen production unit array. r The rated power for each electrolytic cell.

[0080] In the initial stage, hydrogen production units numbered 1-3 are configured to operate at rated power, while hydrogen production unit number 4 is responsible for absorbing fluctuating power. After a preset cycle, units numbered 4, 1, and 2 are switched to operate at rated power, while unit number 3 takes over the absorption of fluctuating power. This cyclical rotation mechanism achieves a balanced distribution of the exposure time of each hydrogen production unit to fluctuating operating conditions, thereby improving the overall service life of the system. The same applies to other operating conditions.

[0081] (2) Scenario 2: Medium power operation

[0082] Under this operating condition, 2P r ≤P elen <3P r In addition, each group has two hydrogen production units operating at rated capacity, one hydrogen production unit in shutdown condition, and the other hydrogen production unit in fluctuating power condition.

[0083]

[0084] (3) Scenario 3: Low power operation:

[0085] Under this operating condition, P r ≤P elen <2P r Furthermore, in each group, one hydrogen production unit is in rated operating condition, two are in shutdown condition, and the other is in fluctuating power condition:

[0086]

[0087] (4) Scenario 4: Ultra-low power condition

[0088] Under this condition, 0≤P elen <P r Furthermore, three hydrogen production units in each group are in shutdown mode, while the other unit is in fluctuating power mode.

[0089]

[0090] The beneficial effects achieved by using the rotation optimization constraint of the hydrogen production unit array in this embodiment of the invention are as follows: By implementing the collaborative operation strategy of the hydrogen production unit array, it is possible to effectively suppress the performance degradation caused by fluctuating operating conditions, reduce the proportion of unhealthy operating conditions, and thus achieve the comprehensive benefits of extending the life of the hydrogen production unit, improving operational safety, and reducing equipment investment, ultimately reducing the total life cycle investment cost.

[0091] Specifically, in this embodiment and some embodiments of the present invention, the power grid interaction constraints include power purchase and sales interlock constraints, power purchase power interaction constraints, power sales power interaction constraints, off-peak period power grid interaction constraints, normal period power grid interaction constraints, and peak period power grid interaction constraints;

[0092] The interlock constraints for electricity purchase and sale are as follows:

[0093] Z grid,sd (t)+Z grid,gd (t)≤1,

[0094] Among them, Z grid,sd (t) represents the electricity purchase behavior, with a value of 1 when electricity is purchased and a value of 0 when no electricity is purchased; Z grid,gd (t) represents the electricity sales activity, with a value of 1 when electricity is sold and a value of 0 when no electricity is sold;

[0095] The power purchase power interaction constraint is:

[0096]

[0097] in, and These represent the minimum and maximum values ​​of the electricity purchased from the grid by the wind-solar coupled hydrogen production system, respectively. and These represent the minimum and maximum values ​​of the electricity sold to the grid by the wind-solar coupled hydrogen production system, respectively.

[0098] The power grid interaction constraints during off-peak hours are as follows:

[0099]

[0100] In equation (1), T d This indicates that the current time-of-use electricity price is at its lowest point, and the State of Charge (SOC) is at its lowest. target This indicates the initial state of charge (SOC) of the energy storage device during off-peak hours. tend This indicates the final state of charge of the energy storage device during off-peak hours; The minimum operating power of the hydrogen production device during time period t is indicated; Equation (1) indicates that when the output of wind power and photovoltaic power is less than the minimum operating power of the hydrogen production device, the system purchases electricity from the grid to produce hydrogen, and the state of charge of the energy storage device can only increase.

[0101] and,

[0102]

[0103] Equation (2) indicates that when the output of wind power and photovoltaic power is greater than the minimum operating power of the hydrogen production device, it is not necessary to purchase electricity from the grid to produce hydrogen, and the surplus electricity will be used to charge the energy storage first.

[0104] The power grid interaction constraints during normal periods are as follows:

[0105]

[0106] In equation (3), T pThis indicates that the current grid time-of-use electricity price is in a flat period. Equation (3) indicates that when the output of wind power and photovoltaic power is less than the minimum operating power of the hydrogen production device, the insufficient power is purchased from the grid or supplemented by energy storage discharge.

[0107] and,

[0108]

[0109] Equation (4) indicates that when the output of wind power and photovoltaic power is greater than the minimum operating power of the hydrogen production unit, the surplus electrical energy will be used for energy storage charging or sold to the grid.

[0110] The peak-hour power grid interaction constraints include:

[0111]

[0112] In the formula, T g Equation (5) indicates that the current time-of-use electricity price of the power grid is in the peak period; Equation (5) indicates that when the sum of the electricity from wind power, photovoltaic power and energy storage is less than the minimum operating power of the hydrogen production device, it is prohibited to purchase electricity from the power grid.

[0113] and,

[0114]

[0115] Equation (6) indicates that when the sum of the electrical energy from wind power, photovoltaic power, and energy storage is greater than the minimum operating power of the hydrogen production device, the surplus electrical energy will be sold to the grid.

[0116] The power grid interaction constraints also include constraints for optimizing electricity sales revenue; the constraints for optimizing electricity sales revenue include:

[0117]

[0118] In equation (7), J price (t), J price (t+1) represent the time-of-use electricity prices for time periods t and t+1, respectively; W grid (t+1) represents the revenue from electricity sales in segment t+1; P grid (t+1) represents the amount of electricity sold to the grid during the t+1 time period. Equation (7) indicates that if the predicted time-of-use electricity price shows an upward trend, the electricity sale will be delayed until the next time period.

[0119] It is understood that the constraint function in the embodiments of the present invention comprehensively considers power balance, safe operation, subsystems, grid interaction, etc., and can effectively reduce the amount of calculation and improve the calculation accuracy while ensuring the reliable operation of the wind-solar coupled hydrogen production system.

[0120] Step S103: The day-ahead scheduling optimization model is solved using an improved mixed-integer linear programming algorithm based on variable aggregation to obtain the day-ahead optimized scheduling data of the wind-solar coupled hydrogen production system.

[0121] As is understood, in this embodiment of the invention, the day-ahead forecast data involved in the day-ahead scheduling optimization model includes the day-ahead forecast of the photovoltaic power generation P. pv (t) and the wind power generation P wind (t); The day-ahead optimized scheduling data obtained by solving includes: the output P of the energy storage device. batt (t), the hydrogen production power P of the hydrogen production device hyd (t), the interaction power P between the wind-solar coupled hydrogen production system and the power grid grid (t).

[0122] Understandably, in the day-ahead scheduling optimization model of a wind-solar coupled hydrogen production system, decision variables include both continuous variables, such as power generation and hydrogen production at various times, and integer variables, such as the start-up and shutdown states of wind turbines and photovoltaic panels, which are usually represented by 0 or 1 integer variables, and the charging and discharging states of energy storage devices, which can be described by combining 0 or 1 integer variables with the power values ​​of continuous variables. Therefore, solving the day-ahead scheduling optimization model is a mixed-integer linear programming problem. Mixed-integer linear programming is a class of optimization problems where some decision variables are restricted to integers. Variable aggregation improves the efficiency of solving mixed-integer linear programming by reducing the number of model variables and constraint complexity. Therefore, the improved algorithm is particularly suitable for scenarios involving multiple time scales or multiple devices coordinating in wind-solar coupled hydrogen production systems.

[0123] Specifically, in this embodiment of the invention, variable aggregation reduces the dimensionality of the model by merging similar variables or reconstructing the time and spatial dimensions, while preserving the dynamic characteristics of key physical processes. Variable aggregation includes device aggregation, time-period aggregation, and spatial aggregation. Device aggregation merges multiple devices with similar characteristics or functions (such as wind turbines, photovoltaic generators, energy storage devices, hydrogen production devices, hydrogen storage devices, etc.) into an equivalent virtual device to simplify model complexity. Time-period aggregation divides the time dimension into several time periods with similar characteristics and merges or simplifies the variables within each time period to reduce complexity in the time dimension. For example, a day can be divided into three periods: daytime, evening, and nighttime. During the daytime period, the power output of photovoltaic generators may be relatively stable, and the power output during this period can be represented by an average value or a piecewise function. Spatial aggregation divides the spatial dimension into several regions with similar characteristics and merges or simplifies the variables within each region to reduce complexity in the spatial dimension.

[0124] Specifically, in this embodiment of the invention, the energy storage state of charge of a wind-solar coupled hydrogen production system is used as an example to describe the specific implementation of variable aggregation.

[0125] In day-ahead scheduling, it is necessary to process the continuous variables SOC(t) and P for 24 time periods (t = 1, 2, ..., 24). batt (t) modeling leads to a linear increase in the number of variables over time. By aggregating the variables, the 24 time periods are divided into M time period groups, i.e., M=6 groups, each group is 4 hours. The aggregated energy storage charge state variables are shown in the following formula.

[0126]

[0127] In the formula, T m N represents the set of time periods of the m-th group; m Indicates the number of time periods within a group, such as N. m =3.

[0128] The charging and discharging power constraints within each time period group are shown in the following formula:

[0129]

[0130] Then, the energy storage state of charge constraint aggregation reconstruction is in the form of inter-group recursion, as shown in the following equation:

[0131]

[0132] Where, ΔT m =4 hours, δ m ΔSOC represents the dynamic error slack variable between groups and the volatility margin within groups.

[0133] Furthermore, in this embodiment of the invention, an improved mixed-integer linear programming algorithm based on variable aggregation is employed, and a solver is invoked to solve the problem iteratively to obtain the optimal solution set for day-ahead scheduling. Each iteration evaluates the solution, checking whether it satisfies all preset constraints and whether a reasonable optimization objective has been achieved. If the result does not meet the requirements, the process returns to readjust the relevant parameters and constraints, and the solution calculation is performed again until a satisfactory result is obtained. In this embodiment of the invention, a day-ahead scheduling scheme is formulated based on the calculated results. When the process reaches the end of the iteration, the day-ahead scheduling optimization strategy solution process ends, yielding the optimal solution for day-ahead scheduling that minimizes the system's economic cost.

[0134] To verify the feasibility of the methods described in the above embodiments, this invention constructs a wind-solar coupled hydrogen production system at the simulation level. Based on this simulation, the energy scheduling interaction between different subsystems of the system under wind and solar fluctuations is obtained. By comparing the simulation results with the day-ahead scheduling optimization strategy cost function calculated in the embodiments of this invention, it is found that the day-ahead scheduling optimization strategy cost function obtained using the method of this invention meets the economic requirements and can effectively reduce the operating cost of the wind-solar coupled hydrogen production system.

[0135] Specifically, the system simulation structure diagram is as follows: Figure 2 As shown, to verify the effectiveness and superiority of the day-ahead scheduling of the wind-solar coupled hydrogen production system constructed in this embodiment of the invention, a numerical example is used to verify the day-ahead scheduling optimization strategy. Detailed parameter settings for the wind farm, solar farm, and hydrogen production unit are shown in Table 1, energy storage device parameter settings are shown in Table 2, and energy storage state-of-charge parameter settings are shown in Table 3. The total rated operating power of the hydrogen production unit is set to 300MW, the capacity ratio of the ALK electrolyzer to the PEM electrolyzer is set to 8:2, and the hydrogen price is set to 3 yuan / Nm³. 3 (Standard cubic meters). By setting these parameters in detail, the actual operating scenario of a wind-solar coupled hydrogen production system can be effectively simulated.

[0136] Table 1 Parameters of Wind Farm, Solar Farm and Hydrogen Production Unit

[0137]

[0138]

[0139] Table 2 Parameters of Energy Storage Devices

[0140]

[0141] Table 3 Energy Storage State of Charge Parameters

[0142]

[0143] Based on historical wind and solar power generation data collected from a wind-solar coupled hydrogen production site, and combined with daytime weather forecasts, the predicted wind and solar power generation and the predicted load of the hydrogen production unit for the site in the previous 24 hours were obtained. Detailed data are as follows: Figure 3 and Figure 4 As shown. And the time-of-use electricity prices for different time periods within a 24-hour period in this region were obtained, such as... Figure 8 As shown. To verify that the day-ahead scheduling strategy of this invention is superior to the traditional strategy, two schemes are constructed for comparative analysis. The details of these two schemes are as follows.

[0144] Option 1: Day-ahead scheduling of the wind-solar coupled hydrogen production system according to this embodiment of the invention. First, a day-ahead scheduling optimization strategy objective function is constructed with the goal of minimizing system operating costs. Then, day-ahead scheduling power balance constraints, safe operation constraints, hydrogen production unit characteristic constraints, energy storage unit state of charge constraints, and hydrogen storage unit constraints are constructed. Furthermore, a system-grid power interaction strategy based on time-of-use electricity price fluctuations is adopted to formulate power coordination schemes for different electricity price periods (off-peak, flat, and peak), and the benefits are incorporated into the objective function for minimizing operating costs. Finally, an improved mixed-integer linear programming algorithm based on variable aggregation is used to solve the problem, yielding the day-ahead scheduling scheme for the wind-solar coupled hydrogen production system with the minimum operating cost.

[0145] Option 2: Day-ahead scheduling of the wind-solar coupled hydrogen production system not adopted in the embodiments of this invention. First, a day-ahead scheduling objective function is constructed to minimize the system operating cost, which only includes the operation and maintenance cost of the wind-solar coupled hydrogen production system. Then, day-ahead scheduling constraints are established for power balance, safe operation, hydrogen production unit characteristics, energy storage unit state of charge, and hydrogen storage unit. Finally, a mixed-integer linear programming algorithm is used to solve for the day-ahead scheduling scheme of the system.

[0146] (1) Analysis of the results of power purchase and sale and energy storage charging and discharging in the system

[0147] Combining wind and solar power generation forecasts, hydrogen production unit load forecasts, and grid time-of-use pricing, for Figure 9 The changes in energy storage charging and discharging of the two schemes and the time-sharing analysis of the power purchase and sale of Scheme 1 are compared.

[0148] During periods 1-6, total wind and solar power generation is relatively low, and the power grid operates during off-peak electricity pricing. Option 1 involves energy storage discharge and hydrogen production via electricity purchase during this period. This ensures the energy storage's state of charge remains within a reasonable range while generating additional revenue through hydrogen production from purchased low-priced electricity, effectively utilizing off-peak electricity prices and reducing operating costs. In contrast, Option 2 involves a larger discharge capacity and cannot fully utilize off-peak electricity prices for hydrogen production, increasing potential electricity costs.

[0149] During the 7-9 period, total wind and solar power generation increased but did not reach its peak, while grid electricity prices remained flat. Option 1 prioritizes charging energy storage, selling excess electricity after meeting storage needs, fully utilizing the growth of wind and solar power generation, improving energy efficiency, and generating revenue during periods of flat electricity prices. Option 2 only charges energy storage during this period, failing to utilize excess wind and solar power, resulting in lower energy efficiency.

[0150] Between 10:00 and 17:00, total wind and solar power generation first reaches its peak and then gradually decreases, while the power grid experiences peak electricity price periods. Option 1 utilizes relatively high power sales during peak electricity price periods to fully leverage peak electricity prices and generate revenue. Option 2, however, primarily focuses on energy storage charging and discharging, thus wasting surplus wind and solar power generation.

[0151] During the period from 6 PM to 9 PM, total wind and solar power generation is at a low level, while the power grid is in its peak electricity price period. Due to the high time-of-use electricity price, in order to meet the overall economic requirements of the system, both Scheme 1 and Scheme 2 utilize energy storage discharge to ensure the normal operation of the hydrogen production unit.

[0152] During the 22-24 period, total wind and solar power generation saw a slight increase, while the grid's time-of-use electricity price returned to its off-peak period. Option 1 primarily involves energy storage charging and purchasing electricity from the grid to produce hydrogen, with relatively stable charging power. It fully utilizes off-peak electricity prices to reserve energy for the next day's system operation, also generating additional revenue from hydrogen sales and reducing operating costs. Option 2 only involves energy storage discharge during this period, with a large discharge power, and cannot fully utilize off-peak electricity price periods for effective energy storage, increasing overall operating costs and resulting in lower energy utilization efficiency.

[0153] In summary, based on the predicted wind and solar power generation and the time-of-use electricity price of the grid, in the comparative analysis of 24 time periods, Scheme 1, compared with Scheme 2, can rationally plan the purchase and sale of electricity and the charging and discharging strategy of energy storage according to the changes in wind and solar power generation and time-of-use electricity price, making more effective use of wind and solar energy, reducing unnecessary electricity purchase costs, improving energy utilization efficiency and economic benefits, and demonstrating that the strategy of Scheme 1 in the embodiment of the present invention has better economic efficiency.

[0154] (2) Analysis of Energy Storage State of Charge Results

[0155] The changes in the state of charge of energy storage in Scheme 1 and Scheme 2 are as follows: Figure 10 , Figure 11 As shown, the strategy of Scheme 1 in this embodiment of the invention can closely integrate wind and solar power generation, time-of-use electricity price changes, and the system's own electricity purchase and sale and charging and discharging needs to reasonably control energy storage. This allows the energy storage state of charge to remain at a relatively stable and reasonable level in different time periods, fully leveraging the role of energy storage in energy allocation and improving the system's energy utilization efficiency. In contrast, Scheme 2 exhibits greater fluctuations in the energy storage state of charge, and during the 6-hour and 24-hour periods, the energy storage state of charge approaches the lower limit, increasing the loss of the energy storage battery and hindering the stable operation of the system.

[0156] (3) Analysis of the operating efficiency of the hydrogen production unit

[0157] The operating power of the hydrogen production units in Scheme 1 and Scheme 2 is as follows: Figure 12As shown, the operating power of the hydrogen production unit under two schemes is presented for each of the 24 time periods. During periods 5-6 and 22-24, wind and solar power generation cannot meet the normal operation of the hydrogen production unit, and time-of-use electricity prices are mainly at their lowest. Therefore, Scheme 1 purchases electricity to supplement the power shortage of the hydrogen production unit. Scheme 2 relies solely on energy storage during these periods, but the energy storage's state of charge is already close to its lower limit, and it cannot provide sufficient power. The total hydrogen production of Scheme 1 and Scheme 2 during these 5 time periods is as follows: Figure 13 As shown, the total hydrogen production capacity of Scheme 1 is 381,000 Nm³. 3 Scheme 2 has a total hydrogen production capacity of 342,000 Nm³. 3 Therefore, the strategy of Scheme 1 in this embodiment of the invention makes the hydrogen production device more efficient.

[0158] (4) Analysis of operating cost results

[0159] The operating cost composition and changes of Option 1 and Option 2 over 24 time periods are as follows: Figure 14 As shown, Scheme 1 combines the time-of-use electricity price fluctuation characteristics with the forecast of total wind and solar power generation. Low-cost electricity purchase for hydrogen production is implemented during the 1-6h and 22-24h hours, with the revenue from hydrogen sales reducing operating costs. During the 8-16h hours, revenue is generated by selling electricity to the grid, significantly reducing operating costs during this period. The total operating cost is compared to... Figure 15 As shown, the total cost of Scheme 1 is significantly lower than that of Scheme 2. In summary, the strategy of Scheme 1 in this embodiment of the invention provides a more balanced response to various cost factors, can better adapt to different operating conditions at different times, and its lower overall operating cost and good cost stability are more suitable for the economical operation of wind-solar coupled hydrogen production systems.

[0160] (5) Analysis of the operating status of the electrolytic cell group

[0161] The duration of power fluctuation exposure in hydrogen production units significantly affects their service life. To verify the life protection effect of the modular collaborative control strategy, an alkaline electrolyzer was used in the hydrogen production unit. Scheme 1 and Scheme 2 were selected for comparative analysis of the alkaline electrolyzer array. Figures 16-17 The electrolyzer power-time curves for the two hydrogen production schemes are presented respectively. Figure 18 The time-series distribution of each unit's operating conditions is displayed using chromatographic identification: green / red / yellow dots represent rated operating conditions, fluctuating operating conditions, and standby operating conditions, respectively, while blue squares indicate the shutdown status.

[0162] Compared to Scheme 2, Scheme 1 enables multi-condition coordinated scheduling and flexible combination switching of four electrolytic cells. (Overall) Figure 16 and Figure 18Analysis shows that the proportion of time spent in rated operation under this mode is significantly higher than that under Scheme 2, and at most a single electrolytic cell undertakes power fluctuation regulation per time period. It is noteworthy that electrolytic cells 1, 2, and 3 operate at rated conditions for extended periods with significant downtime, while electrolytic cell 4 remains operational throughout the 24-hour cycle with a slightly increased period of fluctuating operation, continuously performing the core regulation function. In summary... Figure 17 and Figure 18 Analysis shows that in Scheme 2 mode, the electrolytic cell array needs to be continuously started throughout the day to achieve balanced operation. Due to the characteristics of wind, solar and load fluctuations, the electrolytic cells are mainly forced to operate under fluctuating conditions, and can only maintain rated operation briefly during peak wind and solar periods (1, 10-15, 23-24 hours).

[0163] In summary, the day-ahead scheduling method for wind-solar coupled hydrogen production systems established in this invention can rationally utilize the characteristics of time-of-use electricity price fluctuations, combined with the analysis of the power status and operation of the wind-solar coupled hydrogen production system, to accurately formulate power coordination schemes during off-peak, flat, and peak electricity price periods. This invention can not only effectively reduce the operating costs of wind-solar coupled hydrogen production systems, but also significantly improve the utilization efficiency of renewable energy.

[0164] Example 2:

[0165] This embodiment discloses a day-ahead scheduling system for a wind-solar coupled hydrogen production system, used to implement the day-ahead scheduling method for the wind-solar coupled hydrogen production system in Embodiment 1. The specific implementation of each module is described in the corresponding section of Embodiment 1.

[0166] like Figure 19 As shown, the system includes a data prediction module, a model building module, and an optimization solution module.

[0167] The data prediction module is used to perform day-ahead prediction based on the historical data and meteorological data of the wind and solar power generation system, and obtain the day-ahead prediction data of the wind and solar power generation system.

[0168] The model building module is used to build a day-ahead scheduling optimization model based on the day-ahead forecast data. The day-ahead scheduling optimization model includes an objective function and constraint functions for day-ahead scheduling optimization. The objective function includes a minimum cost function, and the constraint functions include power balance constraints, wind and solar power generation operation constraints, energy storage state of charge constraints, hydrogen production and storage operation constraints, and grid interaction constraints.

[0169] The optimization solution module is used to solve the day-ahead scheduling optimization model using an improved mixed-integer linear programming algorithm based on variable aggregation, to obtain the day-ahead optimized scheduling data of the wind-solar coupled hydrogen production system. Since the system in this embodiment and the method in Embodiment 1 are related and can be referenced each other, this description is redundant and will not be repeated here. Because the principle of this system embodiment is the same as the above method embodiment, this system embodiment also has the corresponding technical effects of the above method embodiment.

[0170] The embodiments of the present invention adopt a system and grid power interaction strategy based on time-of-use electricity price fluctuations. By utilizing the time-of-use electricity price difference of the grid and combining it with the power status and operation of the wind-solar coupled hydrogen production system, it is possible to determine the power coordination operation scheme during the low-price, flat-price, and peak periods, thereby effectively reducing the operating cost of the wind-solar coupled hydrogen production system.

[0171] The day-ahead scheduling optimization model constructed in this embodiment of the invention uses the operation and maintenance costs of wind power, photovoltaic, energy storage, and hydrogen production devices, the cost of system interaction with the grid, and the revenue from purchasing electricity to produce hydrogen as the objective function. The constraint function comprehensively considers power balance, safe operation, subsystems, grid interaction, etc., which can effectively reduce the amount of calculation and improve the calculation accuracy while ensuring the reliable operation of the wind-solar coupled hydrogen production system.

[0172] The embodiments of the present invention employ an improved mixed-integer linear programming method based on variable aggregation to solve the model, which can effectively improve the solution efficiency of the day-ahead scheduling optimization model while maintaining the feasibility and effectiveness of the wind-solar coupled hydrogen production system in terms of operating costs.

[0173] The embodiments of the present invention utilize a rotation optimization control strategy for hydrogen production unit arrays to effectively improve the service life and operational safety of hydrogen production units while reducing investment.

[0174] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0175] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A day-ahead scheduling method for a wind-solar coupled hydrogen production system, characterized in that, The wind-solar coupled hydrogen production system includes a wind-solar power generation device, an energy storage device, a hydrogen production device, a hydrogen storage device, and a grid interconnection device; the method includes: Based on historical data and meteorological data of the wind-solar coupled hydrogen production system, day-ahead forecasts are made to obtain day-ahead forecast data of the wind-solar power generation system. A day-ahead scheduling optimization model is constructed based on the day-ahead forecast data. The day-ahead scheduling optimization model includes an objective function and constraint functions for day-ahead scheduling optimization. The objective function includes a minimum cost function, and the constraint functions include power balance constraints, wind and solar power generation operation constraints, energy storage state of charge constraints, hydrogen production and storage operation constraints, and grid interaction constraints. An improved mixed-integer linear programming algorithm based on variable aggregation is used to solve the day-ahead scheduling optimization model to obtain the day-ahead optimized scheduling data of the wind-solar coupled hydrogen production system.

2. The method according to claim 1, characterized in that, The objective function is the minimum cost function F of the wind-solar coupled hydrogen production system. min And calculate according to the following formula: Among them, F min W represents the minimum operating cost of day-ahead scheduling; dg (t) represents the operation and maintenance cost of the wind and solar power generation system during time period t; W batt (t) represents the operation and maintenance cost of the energy storage device during time period t; W ap (t) represents the operation and maintenance cost of the hydrogen production unit during time period t; W grid (t) represents the interaction cost between the wind-solar coupled hydrogen production system and the power grid during time period t. This interaction cost includes the cost of purchasing electricity and the revenue from selling electricity; the cost of purchasing electricity is positive, and the revenue from selling electricity is negative. hyd (t) represents the revenue obtained by the wind-solar coupled hydrogen production system during time period t from purchasing electricity from the grid to produce hydrogen and selling the produced hydrogen.

3. The method according to claim 2, characterized in that, Furthermore, Among them, Z pv and P pv (t) represent the operation and maintenance coefficient and photovoltaic power generation of the photovoltaic unit in the wind and solar power generation device at time t, respectively. wind and P wind (t) represent the operation and maintenance coefficient and wind power generation of the wind turbine in the wind and solar power generation system at time t, respectively; Z batt and P batt (t) represent the operation and maintenance coefficient and output of the energy storage device at time t, respectively; Z ap and P hyd (t) represent the operation and maintenance coefficient and hydrogen production power of the hydrogen production unit at time t, respectively; J price (t) represents the grid electricity price at time t, P grid (t) represents the interaction power between the wind-solar coupled hydrogen production system and the power grid at time t, with negative numbers indicating electricity sold and positive numbers indicating electricity purchased and delivered to the grid; J hyd (t) represents the price of hydrogen at time t, S hyd (t) represents the amount of hydrogen sold at time t.

4. The method according to claim 3, characterized in that, The power balance constraint is: P hyd (t)=P wind (t)+P pv (t)-P batt (t)+P grid (t).

5. The method according to claim 4, characterized in that, The operational constraints for wind and solar power generation are as follows: in, These represent the minimum and maximum output power of the wind turbine, respectively. These represent the maximum and minimum output power of the photovoltaic unit, respectively. These represent the minimum and maximum charging power of the energy storage device, respectively. Let P represent the minimum and maximum discharge power of the energy storage battery, respectively, and let P be the discharge power during charging. batt (t) < 0, P during discharge batt (t)>0.

6. The method according to claim 4, characterized in that, The energy storage state of charge constraint is: In the formula, SOC(t) and SOC(t+1) represent the state of charge of the energy storage device during time period t and time period t+1, respectively; η ch E represents the charge / discharge efficiency of the energy storage device. ess The SOC indicates the rated capacity of the energy storage device. min SOC max These represent the lower and upper limits of the charged state of the energy storage device, respectively.

7. The method according to claim 4, characterized in that, The operational constraints for hydrogen production and storage are as follows: in, and These represent the lower and upper limits of the hydrogen storage state of the hydrogen storage device, respectively. and These represent the minimum and maximum operating power of the hydrogen storage device, respectively.

8. The method according to claim 1, characterized in that, The power grid interaction constraints include power purchase and sales interlock constraints, power purchase power interaction constraints, power sales power interaction constraints, power grid interaction constraints during off-peak hours, power grid interaction constraints during normal hours, and power grid interaction constraints during peak hours. The interlock constraints for electricity purchase and sale are as follows: Z grid,sd (t)+Z grid,gd (t)≤1, Among them, Z grid,sd (t) represents the electricity purchase behavior, with a value of 1 when electricity is purchased and a value of 0 when no electricity is purchased; Z grid,gd (t) represents the electricity sales activity, with a value of 1 when electricity is sold and a value of 0 when no electricity is sold; The power purchase power interaction constraint is: in, and These represent the minimum and maximum values ​​of the electricity purchased from the grid by the wind-solar coupled hydrogen production system, respectively. and These represent the minimum and maximum values ​​of the electricity sold to the grid by the wind-solar coupled hydrogen production system, respectively. The power grid interaction constraints during off-peak hours are as follows: and In the formula, T d This indicates that the current time-of-use electricity price is at its lowest point, and the State of Charge (SOC) is at its lowest. target This indicates the initial state of charge (SOC) of the energy storage device during off-peak hours. tend This indicates the final state of charge of the energy storage device during off-peak hours; This represents the minimum operating power of the hydrogen production unit during time period t; and, and The power grid interaction constraints during normal periods are as follows: and In the formula, T p This indicates that the current time-of-use electricity price is in a flat period. and, and The peak-hour power grid interaction constraints include: and In the formula, T g This indicates that the current time-of-use electricity price is in peak hours; and, and The power grid interaction constraints also include constraints for optimizing electricity sales revenue; the constraints for optimizing electricity sales revenue include: When J price (t)<J price At (t+1); In the formula, J price (t), J price (t+1) represent the time-of-use electricity prices for time periods t and t+1, respectively; W grid (t+1) represents the revenue from electricity sales in segment t+1; P grid (t+1) represents the amount of electricity sold to the power grid during the time period t+1.

9. The method according to claim 8, characterized in that, The aforementioned forecast data includes: the previously predicted photovoltaic power generation P. pv (t) and the wind power generation P wind (t); The day-ahead optimized scheduling data of the wind-solar coupled hydrogen production system includes: the output P of the energy storage device. batt (t), the hydrogen production power P of the hydrogen production device hyd (t), the interaction power P between the wind-solar coupled hydrogen production system and the power grid grid (t); An improved mixed-integer linear programming algorithm based on variable aggregation is used to solve the day-ahead scheduling optimization model. The variable aggregation includes unit aggregation, time period aggregation, and spatial aggregation.

10. A day-ahead dispatching system for a wind-solar coupled hydrogen production system, characterized in that, include: The data prediction module is used to perform day-ahead prediction based on the historical data and meteorological data of the wind and solar power generation system, and obtain the day-ahead prediction data of the wind and solar power generation system. The model building module is used to build a day-ahead scheduling optimization model based on the day-ahead forecast data. The day-ahead scheduling optimization model includes an objective function and constraint functions for day-ahead scheduling optimization. The objective function includes a minimum cost function, and the constraint functions include power balance constraints, wind and solar power generation operation constraints, energy storage state of charge constraints, hydrogen production and storage operation constraints, and grid interaction constraints. The optimization solution module is used to solve the day-ahead scheduling optimization model using an improved mixed-integer linear programming algorithm based on variable aggregation, so as to obtain the day-ahead optimized scheduling data of the wind-solar coupled hydrogen production system.

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