Electro-hydrogen coupling system robust optimization operation method, system, equipment and medium

Abstract

The invention provides an electro-hydrogen coupling system robust optimization operation method, system, equipment and medium, belongs to the technical field of electro-hydrogen coupling comprehensive energy systems, and constructs an electro-hydrogen coupling system comprising a fan, a photovoltaic device, an electric energy storage device, an electrolytic bath, a fuel cell and a hydrogen storage device. A deterministic optimization model considering constraints such as power balance is established, an uncertainty set is constructed by analyzing wind and light output historical data, and adjustment parameters are introduced. And introducing uncertainty into an optimization model to form a robust optimization model, and iteratively solving a main problem and a sub-problem by adopting a column constraint generation algorithm to finally obtain an optimal operation scheduling scheme. According to the method, energy complementation is achieved through electricity-hydrogen coupling, renewable energy output uncertainty is effectively dealt with through robust optimization, economical efficiency and conservative property are flexibly balanced by adjusting parameters, the risk and fluctuation resisting capacity of the system in the electricity market environment is improved, and operation reliability is guaranteed.

Description

Technical Field

[0001] This invention belongs to the technical field of integrated energy systems with electro-hydrogen coupling, and in particular, it relates to a robust optimization method, system, equipment, and medium for electro-hydrogen coupling systems. Background Technology

[0002] Electro-hydrogen coupling systems have become a research hotspot and an industry frontier. Integrated wind-solar-hydrogen-storage demonstration projects encompass technologies such as hydrogen production, storage, refueling, and fuel cell power generation. Electro-hydrogen coupling systems have benefited from continuous advancements in equipment technology, with alkaline electrolyzers, proton exchange membrane electrolyzers, and fuel cells experiencing sustained improvements in efficiency, lifespan, and economics.

[0003] In related technologies, the robust optimization of the electro-hydrogen coupling system is achieved using an uncertainty method based on stochastic scenarios. This requires generating a large number of scenarios to cover fluctuation possibilities, resulting in excessively long model solution times and an inability to adapt to real-time scheduling. The energy storage system may over-discharge due to unforeseen power drops, causing the charge to fall below the safety limit and leading to battery damage; the electrolyzer may shut down due to a sudden power surge exceeding its rated power, triggering a protection mechanism that interrupts hydrogen production.

[0004] The robustness model of the relevant technology has a constant conservatism. If the conservatism is too high, the system needs to reserve a large amount of backup resources, resulting in additional electricity purchase expenditure and energy storage redundancy loss. If the conservatism is too low, it is impossible to predict that the wind turbine output will be high but the actual output will be low, resulting in insufficient power generation. It is necessary to purchase electricity from the grid urgently, which may be during peak electricity prices, significantly increasing costs and potentially affecting user reliability due to insufficient power supply.

[0005] Related technologies include two-layer robust models with mixed integer variables, and the min-max structure suffers from variable coupling, making it difficult to meet requirements. The outer layer optimizes equipment states, while the inner layer optimizes uncertainties and power variables, which are interdependent. The branch and bound method is difficult to decouple efficiently and has excessive computation time. However, power systems need to output the next day's dispatch plan in a short time, which cannot meet the practicality of engineering. Summary of the Invention

[0006] This invention provides a robust optimization operation method for an electro-hydrogen coupling system. Through a bidirectional electro-hydrogen energy conversion mechanism, it enables cross-time-period storage and scheduling of renewable energy, reducing wind and solar curtailment rates. A multi-timescale coupling model is constructed to improve the system's adaptability to fluctuations in wind and solar power output, thereby reducing power volatility.

[0007] The methods include: Step S101: Construct an electro-hydrogen coupling system; Step S102: Determine the operating parameters of each device in the electro-hydrogen coupling system; Step S103: Based on the operating parameters of each device, establish a deterministic optimization model for the electro-hydrogen coupling system. The deterministic optimization model takes minimizing the daily operating cost of the system as the objective function and includes power balance constraints, electric energy storage constraints, hydrogen storage constraints, external power grid purchase and sale constraints, and power constraints of alkaline electrolyzer and proton exchange membrane fuel cell. Step S104: Statistically analyze the historical output data of the wind turbine and photovoltaic device in the electro-hydrogen coupling system, construct the uncertainty set of the wind turbine and photovoltaic output using the wavelet transform method, and introduce uncertainty adjustment parameters to characterize the total period of the boundary of the wind turbine and photovoltaic output fluctuation interval within the scheduling cycle; Step S105: Based on the deterministic optimization model in S103 and the uncertainty set in S104, establish a robust optimization model for the electro-hydrogen coupling system. The robust optimization model aims to minimize the daily operating cost of the system under the worst-case conditions of wind turbine and photovoltaic output. The power balance constraint in the deterministic optimization model is rewritten to incorporate the uncertainty variables in S104. Step S106: Based on the uncertainty adjustment parameter in S104, set the conservative adjustment method of the robust optimization model. By changing the value of the uncertainty adjustment parameter, adjust the conservatism of the robust optimization result. Step S107: Select the column constraint generation algorithm as the solution method for the robust optimization model in S105. Decompose the robust optimization model into the main problem and sub-problems. Combine the strong binary theory to transform the sub-problems from minimax problems into maximal structure problems. Introduce auxiliary variables and parallel variables to match the constraint requirements of the maximal structure problem. Step S108: Using the column constraint generation algorithm of S107, the main problem and the transformed subproblems are solved iteratively until the iteration converges, and the optimal operation scheduling scheme of the electric-hydrogen coupling system under the worst case of wind turbine and photovoltaic output is obtained.

[0008] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the robust optimization operation method for the electro-hydrogen coupling system.

[0009] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the robust optimization operation method for the electro-hydrogen coupling system.

[0010] As can be seen from the above technical solutions, the present invention has the following advantages: The robust optimization operation method for an electro-hydrogen coupling system provided by this invention constructs a coupled system including wind turbines, photovoltaics, electric energy storage, electrolyzers, fuel cells, and hydrogen storage devices. It employs wavelet transform analysis of historical data to construct an uncertainty set containing uncertain variables, allowable fluctuation values, and adjustment parameters. Compared to stochastic scenario methods, this method can more accurately capture the fluctuation characteristics of wind turbines and photovoltaics, avoiding equipment overload or power imbalance caused by extreme fluctuations. A conservative adjustment method is set based on the uncertainty adjustment parameters, allowing for dynamic control according to actual scenarios. A column constraint generation algorithm decomposes the robust model into the main problem and sub-problems, transforming the sub-problem structure using strong binary theory and introducing auxiliary variables to reduce computational complexity and meet real-time scheduling requirements. The robust optimization model aims to minimize daily operating costs under the worst-case scenario, obtaining the optimal scheduling scheme through iterative solutions, improving economic efficiency in the electricity market environment. This invention provides a full-cycle optimization solution for electro-hydrogen coupling systems through conservative adjustability to adapt to different scenarios and efficient algorithms to meet real-time engineering requirements. Attached Figure Description

[0011] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 Flowchart of robust optimization operation method for electro-hydrogen coupling system; Figure 2 A schematic diagram of a robust optimization system for an electro-hydrogen coupling system; Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0013] The robust optimization operation method for the electro-hydrogen coupling system provided by this invention establishes an electro-hydrogen coupling system in which electricity and hydrogen interact; it models the uncertainties of wind turbine and photovoltaic output and establishes a robust optimal model; it determines the optimal operation scheduling scheme under worst-case conditions, enhancing the system's ability to withstand risks and fluctuations in the power market environment; and it can flexibly adjust the conservatism of the optimization by changing the uncertainty adjustment parameters.

[0014] The robust optimization operation method of the electro-hydrogen coupling system involved in this application will be described in detail below. Specific details such as particular system structures and technologies are proposed for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0015] It should be understood that, when used in this application specification, the term "including" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "including," "comprise," "have," and variations thereof all mean "including, but not limited to," unless otherwise specifically emphasized.

[0016] The phrases "one embodiment" or "some embodiments" described in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments, unless otherwise specifically emphasized."

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 The diagram shows a flowchart of a robust optimization operation method for an electro-hydrogen coupling system in a specific embodiment. The method includes: Step S101: Construct an electro-hydrogen coupling system, which includes a wind turbine, a photovoltaic device, an electric energy storage system, an alkaline electrolyzer, a proton exchange membrane fuel cell, a hydrogen storage device, and an interface for connecting to an external power grid to achieve the interaction between electricity and hydrogen.

[0019] In some embodiments, the constructed electro-hydrogen coupling system comprises wind turbines and photovoltaic devices as renewable energy power generation units, converting wind and solar energy into electricity, respectively; an energy storage system for short-term energy storage, capable of rapid charging and discharging to mitigate short-term power fluctuations; an alkaline electrolyzer converts electricity into hydrogen through water electrolysis, its operation dependent on stable power input; a proton exchange membrane fuel cell converts hydrogen into electricity through a hydrogen-oxygen reaction, serving as a backup power source for the system; and a hydrogen storage device for long-term hydrogen storage, balancing the temporal and spatial differences between hydrogen production by the electrolyzer and hydrogen consumption by the fuel cell. The interface connecting to the external power grid supports bidirectional power exchange, allowing for the sale of electricity when there is a system power surplus and the purchase of electricity when there is a shortage. This achieves multi-pathway consumption of renewable energy and reduces wind and solar curtailment.

[0020] Step S102: Determine the operating parameters of each device in the electro-hydrogen coupling system described in S101.

[0021] The specific operating parameters include: the wind speed at which the wind turbine cuts in, its rated wind speed, and its cut-off wind speed; the maximum output power, power temperature coefficient, and reference temperature of the photovoltaic device; the initial capacity, charging efficiency, discharging efficiency, maximum charging and discharging power, and upper and lower limits of the energy storage system; the operating temperature and ohmic resistance parameters of the alkaline electrolyzer; the total number of single cells, current density, and stack activation area of ​​the proton exchange membrane fuel cell; the upper and lower limits of the capacity of the hydrogen storage device; and the purchase price, sales price, and upper and lower limits of the purchased and sold power from the external power grid.

[0022] In some embodiments, equipment operating parameters are quantitative descriptions of system characteristics, determining the equipment's output capacity, efficiency, and safety boundaries under different operating conditions. For example, the output power of a wind turbine varies with the actual wind speed and the cut-in / rated / cut-off wind speeds; photovoltaic output is affected by temperature, radiation intensity, and the power temperature coefficient; and the charging and discharging power of energy storage cannot exceed the maximum limit to avoid equipment damage. These parameters ensure that the model output matches the actual operating capabilities of the equipment. Parameters covering all operating conditions ensure the model's applicability in different scenarios and improve model accuracy.

[0023] Step S103: Based on the operating parameters of each device determined in S102, establish a deterministic optimization model for the electro-hydrogen coupling system. The deterministic optimization model takes minimizing the daily operating cost of the system as the objective function and includes power balance constraints, electrical energy storage constraints, hydrogen storage constraints, external power grid purchase and sale constraints, and power constraints of the alkaline electrolyzer and proton exchange membrane fuel cell.

[0024] In some embodiments, the objective function of the deterministic optimization model is to minimize the daily operating cost of the system.

[0025] The method for minimizing the daily operating cost of the system in this embodiment is to include the power consumption cost of the alkaline electrolyzer, the power generation cost of the proton exchange membrane fuel cell, and the cost of purchasing and selling electricity from the external power grid.

[0026] In this embodiment, the power balance constraint requires that at any given time, the sum of the system's power generation and purchased power equals the sum of the power consumption, the power sold, and the energy storage charging and discharging. The energy storage constraint includes the current power level, upper and lower limits of power level, upper and lower limits of charging and discharging power, and mutually exclusive states.

[0027] Hydrogen storage constraints limit the hydrogen storage capacity to a safe range; external power grid purchase and sale constraints limit the upper and lower limits of power purchase and sale and prevent simultaneous power purchase and sale. Electrolyzer and fuel cell power constraints limit their output power between minimum and maximum limits to ensure economic efficiency under ideal predictable conditions; multi-dimensional constraints ensure the safe and stable operation of the system and avoid equipment overload or power imbalance caused by pursuing the lowest cost.

[0028] Step S104: Perform statistical analysis on the historical output data of the wind turbine and photovoltaic device in the S101 electro-hydrogen coupling system. Use wavelet transform to construct the uncertainty set of the wind turbine and photovoltaic output. The uncertainty set includes the uncertainty variables of the wind turbine output, the uncertainty variables of the photovoltaic output, and the corresponding allowable fluctuation values. Introduce the uncertainty adjustment parameter, which takes the value from 0 to an integer from the scheduling period T, to characterize the total period of the boundary of the fluctuation range of the wind turbine and photovoltaic output within the scheduling period.

[0029] In some embodiments, statistical analysis is performed on historical output data of wind turbines and photovoltaics, and the data must include the output power of different seasons, weather types and intraday periods.

[0030] Optionally, in this embodiment, when using wavelet transform, the db4 wavelet basis function is selected to decompose the power data into four layers, where the low-frequency approximate component reflects the intraday power variation trend, and the high-frequency detail component reflects random fluctuations. Measurement noise in the high-frequency component is removed by soft thresholding, while valid fluctuations are retained.

[0031] This embodiment statistically analyzes the fluctuation amplitude of high-frequency detail components, taking the 90th percentile as the allowable fluctuation value to cover the vast majority of normal fluctuations. The constructed uncertainty set includes: wind turbine output uncertainty variables and photovoltaic output uncertainty variables; and an uncertainty adjustment parameter Γ (0≤Γ≤24) is introduced to control the total number of times the boundary is reached within the scheduling cycle. Statistical analysis determines a reasonable fluctuation range to avoid interference from extreme outliers; the uncertainty set quantifies the possible range of fluctuations, and the Γ parameter controls the fluctuation scenario, providing controllable uncertainty input for robust optimization.

[0032] Step S105: Based on the deterministic optimization model in S103 and the uncertainty set in S104, establish a robust optimization model for the electro-hydrogen coupling system. The robust optimization model aims to minimize the daily operating cost of the system under the worst-case conditions of wind turbine and photovoltaic output. The power balance constraint in the deterministic optimization model is rewritten to incorporate the uncertainty variables in S104.

[0033] In some embodiments, the objective function of the robust optimization model is to minimize the daily operating cost of the system under the worst-case scenario of wind turbine and photovoltaic output. Specifically, this involves optimizing equipment operating status and power, as well as identifying uncertain variables, to find the worst-case scenario. The power balance constraints in the deterministic model are replaced with the original predicted values ​​to ensure power balance under any fluctuating scenario. The constraints of energy storage, hydrogen storage, grid power purchase and sale, and equipment power in the deterministic model are retained; these constraints do not change with uncertainty, ensuring safe equipment operation. The variable system includes the relationship system between binary variables and power, electricity, and uncertain variables.

[0034] Step S106: Based on the uncertainty adjustment parameter in S104, set the conservative adjustment method of the robust optimization model. By changing the value of the uncertainty adjustment parameter, adjust the conservatism of the robust optimization result.

[0035] In some embodiments, the uncertainty adjustment parameter Γ ranges from 0 to 24, optionally corresponding to a 24-hour scheduling cycle, with an initial value set to 12.

[0036] It should be noted that when Γ=0, neither the wind turbine nor the photovoltaic system reaches the fluctuation boundary at any given time. When Γ=24, the boundary is reached at all times. By calculating the daily operating cost of the system under different Γ values ​​and plotting the Γ-cost curve, it is verified that the cost increases monotonically with increasing Γ.

[0037] Optionally, the adjustment strategy, combined with external conditions, can involve raising Γ when electricity prices are high to reduce high-priced electricity purchases; lowering Γ when forecast accuracy is high to reduce reserve costs; and raising Γ when energy storage is low to avoid power imbalance. After adjustment, Γ should be maintained within the range of 0-24, with a single adjustment increment ≤2 to avoid sudden cost fluctuations. In this way, by adapting the Γ value to real-time conditions, costs and risks, and economic efficiency and reliability, can be balanced in different scenarios.

[0038] Step S107: Select the column constraint generation algorithm as the solution method for the robust optimization model in S105. Decompose the robust optimization model into the main problem and sub-problems, and combine the strong binary theory to transform the sub-problems from minimax problems into maximal structure problems. Introduce auxiliary variables and parallel variables to match the constraint requirements of the maximal structure problem.

[0039] In some embodiments, the column constraint generation algorithm is applicable to the integer robust model of S105, reducing the difficulty of solving the problem by iteratively decomposing the main problem and subproblems. The main problem is a minimization problem, optimizing binary variables, i.e., device states. Constraints include device operation constraints and pruning constraints based on historical solutions to subproblems.

[0040] The subproblem is a maximization problem. Based on strong binary theory, the binary variables of the current master problem are fixed, transforming the original minimax problem into a maximization problem containing only continuous variables. The optimization objective is to maximize the system cost. We define s(t) to associate Γ with boundary variables, paired variables, and concurrent variables, ensuring that the subproblem constraints fit the linear solution format. We verify the equivalence between the master-subproblem and the original model by comparing the cost and constraint satisfaction under different Γ values, ensuring the decomposition and transformation are unbiased. In this way, the highly complex robust model is transformed into a solvable iterative problem, significantly reducing computational difficulty; the collaborative iteration of the master and subproblems ensures the optimality of the solution and avoids local optima.

[0041] Step S108: Using the column constraint generation algorithm of S107, the main problem and the transformed subproblems are solved iteratively until the iteration converges, so as to obtain the optimal operation and scheduling scheme of the electric hydrogen coupling system under the worst case of wind turbine and photovoltaic output, so as to enhance the system's ability to resist risks and fluctuations in the power market environment.

[0042] In some embodiments, when using the column constraint generation algorithm for iterative solution, the initial iteration k=0 uses the solution of the S103 deterministic model as the initial solution to the main problem; in the k-th iteration, the main problem is solved to obtain the binary variable x. k Subproblems based on x k Solving for the continuous variable y k And the corresponding maximum cost.

[0043] If the difference between the cost of the main problem and the cost of the subproblems is within the convergence threshold, the iteration terminates, and x is output. k y k As the optimal solution; otherwise, solve the subproblem y. k The problem is transformed into a pruning constraint and added to the main problem, proceeding to k+1 iterations.

[0044] During the iteration process, the cost value and constraint satisfaction must be recorded each time to ensure no constraint violations. The final optimal operation scheduling scheme includes the charging and discharging power of the energy storage, the power of the electrolyzer / fuel cell, the power purchased and sold from the grid, and the hydrogen storage status at each time point, covering a 24-hour scheduling cycle. In this embodiment, as the iteration progresses, the main problem moves closer to the worst-case scenario, and the optimal state of the subproblems becomes more adaptable to the worst-case scenario until both converge to the same value, i.e., the robust optimal solution. This improves the system's ability to withstand the fluctuations and price risks of renewable energy in the electricity market.

[0045] Furthermore, as a refinement and extension of the specific implementation method of the above-mentioned robust optimization operation method for the electro-hydrogen coupling system, in order to fully explain the specific implementation process in this embodiment, the method includes the following specific contents.

[0046] In some embodiments, the wind turbine model is a predicted wind turbine output based on time t, as shown in equation (1).

[0047] (1) in, To predict wind speed. This is the rated fan output. This refers to the wind speed of the fan at its rated power, i.e., the rated wind speed. To cut into wind speed, To cut off the wind speed.

[0048] In some embodiments, the photovoltaic model is represented based on the actual photovoltaic output at time t as follows: (2) In the formula, This represents the maximum output power of the photovoltaic system. This represents the intensity of solar radiation under standard testing conditions. This represents the actual solar radiation intensity. The power temperature coefficient is -0.47% / ℃. This is a reference temperature. The temperature of the photovoltaic cell.

[0049] There is a linear relationship between the total power of photovoltaic panels at time t and the total area of ​​the panels, expressed as: (3) in, This represents the total area of ​​the photovoltaic panels. This indicates that the panel per unit area is 1kW / m² 2 The conversion factor from output power to photovoltaic panel area under solar radiation.

[0050] In some embodiments, the energy storage model selects a time scale of 24 hours per day, with an initial power of [missing information]. .

[0051] The amount of energy stored in the energy storage system at time t+1 It is expressed as follows: (4) (5) In the formula, Let be the electrical power of the energy storage system at time t. , The charging and discharging power of the energy storage system. To improve energy storage charging efficiency. This represents the discharge efficiency. The time interval is 1 hour.

[0052] In some embodiments, the alkaline electrolyzer model is based on a given temperature. The UI equation for the alkaline electrolyzer is as follows.

[0053] (6) in, This is the output voltage of the electrolytic cell. The temperature of the electrolytic cell is... . This represents the area of ​​the electrolysis module. t represents the output current of the electrolytic cell. r represents the ohmic resistance parameter of the electrolyte. t and s represent the electrode overvoltage factors. It is a reversible unit voltage.

[0054] Electrolyzer energy efficiency Refers to thermal neutral voltage With the voltage of the electrolytic cell The ratio.

[0055] (7) In some embodiments, the output power of the electrolytic cell is expressed as: (8) In the formula, Let t be the input power of the electrolytic cell at time t.

[0056] In some embodiments, the power consumption cost in the electrolytic cell can be expressed as a linear function. Therefore, the cost of electrical energy consumed by the electrolytic cell at time t is shown in equation (9).

[0057] (9) Both a and b are cost factors.

[0058] In some embodiments, the proton exchange membrane fuel cell model is based on the output power of the proton exchange membrane fuel cell stack as follows.

[0059] (10) in, It is the total number of individual units that are linked serially in the stack. It is the current density. It is the stack activation region.

[0060] The cost of fuel cell power generation at time t is shown in equation (11).

[0061] (11) c and d are cost factors.

[0062] In some embodiments, the hydrogen storage model can be expressed as: (12) In the formula, Let be the hydrogen storage capacity at time t. , The figures represent the DC-DC converter efficiencies of the electrolyzer and fuel cell, respectively. The time interval is 1 hour.

[0063] In some embodiments, the external grid power purchase and sale model is based on the external grid connection of the electro-hydrogen coupling system. The electricity purchased from the external grid at time t is... The purchase price of electricity is Similarly, This is electricity sold to the external power grid. The unit price of electricity sold is... .

[0064] The electricity cost of exchanging electricity between the electro-hydrogen coupling system and the external power grid is expressed as: (13) In some embodiments, the objective function in the robust optimization model incorporates the operational optimization problem of the electro-hydrogen coupling system. Investment costs, auxiliary investment costs, and equipment maintenance costs can all be considered as fixed values ​​and are therefore not considered.

[0065] The objective function of the coupled system is the optimal daily operating economy, i.e., the minimum daily operating cost, as shown below.

[0066] (14) In some embodiments, the constraints are based on a deterministic optimization model for the scheduling problem of the electro-hydrogen coupling system, which can be derived without considering the uncertainties in wind turbine and photovoltaic output. This model has the following constraints: The power balance constraint is as follows: (15) In some embodiments, energy storage constraints involve the following constraint methods: Electric energy storage constraints (16) (17) (18) (19) (20) (twenty one) in and This indicates the minimum and maximum capacity of the stored energy. , It is the maximum charge and discharge power of the energy storage. I(t) is a binary variable representing the charge and discharge conditions of the energy storage system.

[0067] Hydrogen storage constraints are (twenty two) in and These represent the minimum and maximum hydrogen storage capacities, respectively.

[0068] Since the charging and discharging conditions of the hydrogen storage tank do not affect the objective function for solving the optimal solution and the power constraint in this embodiment, if the monitoring values ​​of the hydrogen storage tank remain within a certain range, the hydrogen storage constraint is not considered.

[0069] In some embodiments, the external power grid constraint is (twenty three) (twenty four) (25) in, and This represents the maximum price at which the electro-hydrogen coupling system can purchase and sell electricity from the external power grid. I(t) is a binary variable representing the state of the system when purchasing and selling electricity from the external power grid.

[0070] In some embodiments, the electrolyzer and fuel cell are constrained as follows: (26) (27) in, and These are the maximum and minimum values ​​of the output power of the electrolytic cell, respectively. and These are the minimum and maximum output power of the fuel cell.

[0071] Given a set of wind turbine and solar power output forecasts, the above model is deterministically optimized and can be solved in the same way as a normal solution to a mixed-integer linear programming problem. The optimal solution of the model depends on the accuracy of the wind turbine and solar power output forecasts.

[0072] In some embodiments, the robust optimization model involves uncertainty models of wind turbine and photovoltaic outputs. In practical problems, the uncertainty of wind turbine and photovoltaic outputs is significant, and the outputs directly participate in the power balance of the electro-hydrogen coupling system, affecting power balance constraints. If the uncertainty of wind turbine and photovoltaic outputs is modeled using a stochastic scenario method, it can easily lead to scheduling results exceeding safety limits in actual operation, resulting in high penalty costs. To improve the robustness and resistance to fluctuation risks of the system, this invention establishes a robust optimization model for the electro-hydrogen coupling system. This model can also improve the operational economy of the electro-hydrogen coupling system.

[0073] Statistical analysis of historical data is performed to establish an uncertainty set for the wavelet transform output. A time period is selected that corresponds to the range of continuous wind turbine output across all scenarios. The uncertainty of the wind turbine output in this time period can be represented by an uncertainty set with specified constraints, i.e.: (28) in, The wind turbine output uncertainty variable introduced after considering the impact of output uncertainty can be expressed as: (29) in This represents the predicted output value of the wind turbine. The allowable fluctuation value for the wind turbine output is taken as a positive value in this invention.

[0074] Similarly, the set of uncertainties in photovoltaic output can be represented as: (30) (31) This embodiment considers the robust optimization problem of an electro-hydrogen coupling system with uncertainties in wind turbine and photovoltaic output. To configure the conservatism of the robust model, an uncertainty adjustment parameter is used. This parameter takes an integer value from time 0 to time T, representing the total number of periods at the boundaries of the fluctuation intervals of wind turbine and photovoltaic output within the scheduling period. The uncertainty adjustment parameter can be used to adjust the conservatism of the optimal solution. The uncertainty set of wind turbine and photovoltaic output can ultimately be expressed as: (32) in , and , It is a binary variable. and Both are 1, indicating that both wind turbines and photovoltaics have reached the lower bound of the uncertainty setting. and A value of 1 indicates that both wind turbines and photovoltaics have reached the upper limit of uncertainty.

[0075] In some embodiments, the purpose of the robust optimization model is to ensure that the uncertainty variables of the wind turbine and photovoltaic output satisfy equation (32), and in the worst case, the system can operate in the most economical way according to equation (33).

[0076] (33) The above equation is a robust optimization model. In the equation, x represents the binary optimization variable, and y represents the power, energy, and uncertainty variables.

[0077] (34) (35) Unlike deterministic optimization models, Equation (15) uses WT and PV predictions, while robust optimization models must be rewritten as follows.

[0078] (36) Therefore, for each set of uncertain variables, the final robust optimization model, i.e., equation (33), can be transformed into a deterministic model to solve the problem. The purpose of the maximum structure in the model is to identify the worst case.

[0079] In some embodiments, the robust optimization model solver uses a column constraint generation algorithm to transform the model into a main problem and sub-problems for solving.

[0080] (37) Equation (37) raises the main problem. and It is the solution to the subproblem after n iterations. k is the current iteration count.

[0081] Based on strong binary theory and robust optimization characteristics, the subproblem is transformed from a minimax problem into a maximal structure problem.

[0082] (38) Equation (38) is the transformed subproblem. Wherein, , and These are paired variables that match each constraint of the maximum-minimum structure minimization problem. Indicates inclusion , and , A set of. This involves the introduction of corresponding auxiliary variables. For the corresponding parallel variables, To find the maximum value of this concurrent variable, choose a sufficiently large positive integer.

[0083] In one embodiment of the present invention, based on step S103, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S103 specifically includes the following methods: Step S1031: Construct an objective function, which is to minimize the daily operating cost of the electro-hydrogen coupling system. The daily operating cost includes the power consumption cost of the alkaline electrolyzer, the power generation cost of the proton exchange membrane fuel cell, and the cost of exchanging electricity with the external power grid. The power consumption cost of the alkaline electrolyzer is calculated linearly based on its input power, the power generation cost of the proton exchange membrane fuel cell is calculated linearly based on its output power, and the cost of exchanging electricity with the external power grid is calculated based on the purchased power, the sold power, and the corresponding electricity price.

[0084] In some embodiments, the objective function is given by equation (14), which integrates the operating costs of the main energy conversion and storage devices in the electro-hydrogen coupling system, as well as the energy exchange costs between the system and the external power grid. By minimizing the total daily operating cost, the economic scheduling decision of the system during the scheduling cycle is optimized, enabling the system to achieve the lowest-cost operating scheme under all constraints.

[0085] Step S1032: Construct a power balance constraint, which ensures that at each scheduling moment, the sum of the wind turbine output power, photovoltaic output power, proton exchange membrane fuel cell output power, electric energy storage system discharge power and external grid power purchase power is equal to the sum of alkaline electrolyzer input power, electric energy storage system charging power and external grid power sales power.

[0086] In some embodiments, by combining the power balance constraint with equation (15), the system power inflow and outflow are forced to be equal through equality constraints, preventing power shortage or excess and ensuring stable system operation. This can maintain system power balance and improve operational reliability.

[0087] Step S1033: Construct the operating constraints of the energy storage system. The operating constraints include the energy storage system's energy state update equation, charging power upper and lower limit constraints, discharging power upper and lower limit constraints, energy upper and lower limit constraints, and mutual exclusion constraints between charging and discharging states. The energy state update is calculated based on the energy, charging power, discharging power, and efficiency of the previous moment.

[0088] In some embodiments, Equations (4), (5), (16), (17), (18), and (21) are combined to manage the energy state and power flow of the energy storage system through linear equations and integer variables, ensuring that the energy storage system operates efficiently within physical constraints.

[0089] Step S1034: Construct the operating constraints of the hydrogen storage system. The operating constraints include the capacity state update equation of the hydrogen storage system and the upper and lower limits of the hydrogen storage capacity. The capacity state update is calculated based on the capacity at the previous moment, the hydrogen production of the alkaline electrolyzer, and the hydrogen consumption of the proton exchange membrane fuel cell. The hydrogen production and hydrogen consumption are related to the input power of the electrolyzer and the output power of the fuel cell, respectively.

[0090] In some embodiments, in conjunction with equation (22), hydrogen storage constrains the dynamics of hydrogen storage. The capacity status update is based on the current capacity, the hydrogen production of the electrolyzer, and the hydrogen consumption of the fuel cell. The amount of hydrogen stored is tracked through a linear equation to ensure the balance between hydrogen supply and demand and to couple with the power system.

[0091] Step S1035: Construct external power grid purchase and sale constraints, alkaline electrolyzer power constraints, and proton exchange membrane fuel cell power constraints. The external power grid purchase and sale constraints include upper and lower limits of power purchase, upper and lower limits of power sale, and mutual exclusion constraints between power purchase and power sale states. The alkaline electrolyzer power constraints include upper and lower limits of its input power, and the proton exchange membrane fuel cell power constraints include upper and lower limits of its output power.

[0092] In some embodiments, Equations (23), (24), (25), (26), and (27) are combined to control power flow and equipment operation through box constraints and logic constraints, preventing equipment overload and grid violations.

[0093] In one embodiment of the present invention, based on step S104, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S104 specifically includes the following methods: Step S1041: Collect time series data of the output power of the wind turbine and photovoltaic device in the electro-hydrogen coupling system within a preset historical period.

[0094] In some embodiments, by collecting historical data with a sufficiently long time span and high resolution, a reliable sample is provided for subsequent analysis of the fluctuation patterns of renewable energy output.

[0095] Step S1042: Perform wavelet transform processing on the collected historical power output data of wind turbines and photovoltaics respectively, decompose the power signal into approximate components and detail components, and analyze the statistical characteristics of its output fluctuation based on the detail components.

[0096] In some embodiments, wavelet transform algorithms from signal processing are employed. For historical power data sequences of wind turbines and photovoltaic systems, multi-resolution analysis is performed using specific wavelet basis functions. Through wavelet decomposition, the original power signal is divided into sub-bands of different frequencies: a low-frequency approximate component reflects the overall trend, while several high-frequency detail components reflect fluctuations at different time scales. The statistical characteristics of the high-frequency detail components, such as standard deviation and range, are analyzed in detail to quantify their fluctuation intensity.

[0097] Step S1043: Based on the statistical characteristics, determine the allowable fluctuation values ​​of wind turbine and photovoltaic output respectively, and introduce an integer between 0 and the scheduling period T as an uncertainty adjustment parameter. This parameter is used to control the maximum number of time periods during which fluctuations are allowed within the scheduling period.

[0098] In some embodiments, the allowable fluctuation values ​​of wind turbine and photovoltaic outputs are defined as in equation (29). In the sum (31) The uncertainty adjustment parameter is defined as Γ. It is determined based on the fluctuation statistical characteristics analyzed in step S1042. The uncertainty adjustment parameter Γ is a control parameter that limits the maximum number of periods during which the actual output of wind turbines and photovoltaics can deviate from their predicted values ​​and reach the allowable fluctuation boundary within the entire scheduling period T. By adjusting the value of Γ, a trade-off is struck between the conservatism and reliability of the optimization results.

[0099] Step S1044: Based on the allowable fluctuation value and uncertainty adjustment parameter, construct the uncertainty set of wind turbine and photovoltaic output; the set is defined by the uncertainty variable of wind turbine output, the uncertainty variable of photovoltaic output and a set of binary variables, the binary variables are used to characterize whether the wind turbine and photovoltaic output take the boundary value of their uncertainty interval at each scheduling time.

[0100] In some embodiments, the uncertainty set in Equation (32) adopts the modeling idea of ​​a budget uncertainty set. Instead of assuming a specific probability distribution, a set of all possible fluctuation scenarios is defined, and the size or conservatism of this set is controlled by the budget parameter Γ.

[0101] Step S1045: Correlate the constructed set of uncertainties in wind turbine and photovoltaic outputs with the power balance constraints of the electro-hydrogen coupling system to characterize the impact of renewable energy output uncertainties on system operation.

[0102] In some embodiments, the combination of equation (36) is a crucial step in connecting uncertainty from the model definition to the actual system operating constraints. It ensures that the optimized operating scheme can meet the real-time power balance of the system even when there are any fluctuations in wind and solar power output that belong to the set, thereby guaranteeing the robustness and feasibility of the scheme.

[0103] In one embodiment of the present invention, based on step S105, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S105 specifically includes the following methods: Step S1051: Based on the daily operating cost objective function of the deterministic optimization model in S103 and the joint uncertainty set of wind turbine and photovoltaic output constructed in S104, construct the objective function of the robust optimization model. Clarify that the objective function is to minimize the daily operating cost of the electro-hydrogen coupling system in the worst case of satisfying the uncertainty set constraints in S104. The objective function is formalized as the minimization of the optimization variables and the maximization of the uncertainty variables.

[0104] In some embodiments, combining the objective function of the deterministic optimization model with equation (14), equation (33) yields the objective function of the robust optimization model. The problem of dealing with uncertainty is transformed into a mathematical optimization problem. The uncertainty scenario that results in the highest system cost is identified, and the system's operating state is optimized for that scenario to ensure that the optimization result can withstand the most unfavorable renewable energy fluctuations.

[0105] Step S1052: The joint uncertainty set of wind turbine and photovoltaic output constructed by equation (32) is incorporated into the robust optimization model as a constraint condition. Combined with the uncertainty set of wind turbine output in equation (28) and the uncertainty set of photovoltaic output in equation (30), an uncertainty constraint system of the robust optimization model is formed to ensure that the uncertainty variables of wind turbine output and photovoltaic output in the robust optimization model are always within the fluctuation range defined in S104.

[0106] Step S1053: For the power balance constraint of the deterministic optimization model of equation (15), replace the predicted output values ​​of wind turbines and photovoltaics with the uncertain variables of wind turbine output and photovoltaics output defined in S104. Rewrite the power balance constraint in the form of equation (36) in the original technical solution, clarify the equality relationship between the system power inflow and outflow terms in the rewritten constraint, and ensure that the system power is conserved under any uncertainty scenario.

[0107] In some embodiments, based on the law of conservation of energy, uncertainties are incorporated into the power balance constraints to ensure that the total amount of electricity flowing into the system always equals the total amount flowing out, regardless of fluctuations in wind turbine and photovoltaic output, thus avoiding power imbalances caused by fluctuations in renewable energy.

[0108] Step S1054 uses the other constraints of the deterministic optimization model in S103, except for the power balance constraint, including the energy storage constraint, the external power grid purchase and sale constraint, and the power constraints of the alkaline electrolyzer and proton exchange membrane fuel cell. After confirming that the optimization variables in the above constraints are consistent with the variable system of the robust optimization model and there is no conflict, they are incorporated into the robust optimization model.

[0109] In some embodiments, constraints such as energy storage, grid power purchase and sale, and equipment power reflect the upper limit of energy storage capacity and the grid power purchase and sale quota. These constraints do not change with the uncertainty of renewable energy and can therefore be directly applied. By retaining these constraints, it is ensured that the solution of the robust optimization model can not only cope with fluctuations but also comply with the rules for safe equipment operation and external interaction.

[0110] Step S1055: According to the variable definition in equation (33), clarify the variable set of the robust optimization model; define x, which includes the binary variable I(t) of the charging and discharging state of electric energy storage and the binary variable I(t) of the power grid purchasing and selling state; define y, which includes the continuous variable of power, energy, and uncertainty variables; and limit the range of values ​​for each variable to complete the construction of the variable system of the robust optimization model.

[0111] In some embodiments, the optimization object of the robust optimization model is visualized by explicitly defining the variable types and value ranges. Binary variables control the device's operating state, while continuous variables control the device's operating parameters; these two types of variables work together to optimize system operation. Adapting to the mixed-integer characteristics of the robust optimization model provides a prerequisite for the S107 column constraint generation algorithm, ensuring that the model can be solved efficiently.

[0112] In one embodiment of the present invention, based on step S106, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S106 specifically includes the following methods: Step S1061: Set the initial value of the uncertainty adjustment parameter Γ to 0, which corresponds to the fully deterministic optimization scenario.

[0113] In some embodiments, when Γ=0, the uncertainty set U defined by equation (32) degenerates into a single scenario, and at this time, the robust optimization model (33) degenerates into the deterministic optimization model in S103.

[0114] Step S1062: Set the value of Γ to a series of discrete integer values ​​from 0 to the scheduling period T to form a sequence of robust optimization problems with different levels of conservatism.

[0115] In some embodiments, by traversing Γ from 0 to T, it is essentially a systematic expansion of the budget of the uncertainty set U, thereby generating a series of optimization problems ranging from low cost and high risk to high cost and low risk.

[0116] Step S1063: For each set Γ value, execute the robust optimization model established in S105 to solve for the optimal operation scheduling scheme and the corresponding worst-case daily operating cost under that level of conservatism.

[0117] In some embodiments, for each given Γ value, a min-max problem is solved to find the scheduling strategy that minimizes the cost in the worst case. A corresponding theoretically optimal robust scheduling scheme is calculated for each level of conservatism, ensuring optimality under different risk preferences.

[0118] Step S1064: Record and analyze the characteristics of the optimal operation scheduling scheme and its worst-case daily operating cost corresponding to different Γ values, and establish the correspondence between Γ values ​​and the economic efficiency and conservatism of system operation.

[0119] In some embodiments, data analysis and visualization reveal the quantitative relationship between the economic cost of system operation and the level of robustness.

[0120] Step S1065: Based on the analysis results of S1064, and according to the specific requirements of the system operator for risk tolerance, select the most suitable Γ value and its corresponding optimal operation scheduling scheme from a series of scheduling schemes obtained from the solution as the final implementation scheme.

[0121] In some embodiments, the human-computer interaction decision-making process is based on quantitative analysis. The optimization model provides various possible options and their consequences, and ultimately, human experts make the choice based on higher-level management objectives.

[0122] In one embodiment of the present invention, based on step S107, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S107 specifically includes the following methods: Step S1071: Decompose the robust optimization model established in S105 into a main problem and a subproblem. The main problem contains all binary optimization variables and system operation constraints, and the subproblem is used to identify the uncertainty scenario in the worst case.

[0123] Step S1072: Reconstruct the subproblem from the original minimax problem into a single maximization problem by introducing dual and auxiliary variables corresponding to each constraint.

[0124] In some embodiments, the strong duality theorem in mathematics is used to transform the difficult two-layer structure into a single-layer optimization problem, thereby reducing computational complexity.

[0125] Step S1073: Introduce auxiliary variables and corresponding constraints into the main problem to record and integrate the worst-case uncertainty scenarios returned by the subproblems in each iteration and their impact on the objective function.

[0126] In some embodiments, after the subproblems are solved in each iteration, a worst-case scenario and its corresponding cost are obtained. By dynamically adding constraints, important uncertainty information is gradually incorporated into the main problem, making the solution to the main problem increasingly robust.

[0127] Step S1074: Set the iterative convergence criterion. By calculating the difference between the objective function value of the main problem and the objective function value of the subproblem, determine whether the algorithm has met the convergence requirements.

[0128] In some embodiments, the progress of the algorithm is monitored by continuously tightening the upper and lower bounds. When the two are close enough, it indicates that a robust solution that is close enough to the optimal solution has been found.

[0129] Step S1075: Establish an iterative solution mechanism between the main problem and the sub-problems, and gradually approach the optimal solution of the robust optimization problem through information interaction between the two.

[0130] In some embodiments, through information exchange between the main problem and sub-problems, that is, the main problem provides the current scheduling scheme to the sub-problems, and the sub-problems provide feedback on the worst scenario to the main problem, a progressively improving collaborative solution process is formed to ensure that the algorithm can effectively converge to the robust optimal solution.

[0131] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0132] The following are embodiments of the robust optimization operation system for the electro-hydrogen coupling system provided in this disclosure. This system and the robust optimization operation method for the electro-hydrogen coupling system in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the robust optimization operation system for the electro-hydrogen coupling system, please refer to the embodiments of the robust optimization operation method for the electro-hydrogen coupling system described above.

[0133] like Figure 2 As shown, the system includes: System architecture building module 201 is used to build an electro-hydrogen coupling system; Parameter determination module 202 is used to determine the operating parameters of each device in the electro-hydrogen coupling system; The deterministic modeling module 203 establishes a deterministic optimization model for the electro-hydrogen coupling system based on the operating parameters of each device. The deterministic optimization model takes minimizing the daily operating cost of the system as the objective function and includes power balance constraints, electrical energy storage constraints, hydrogen storage constraints, external power grid purchase and sale constraints, and power constraints of the alkaline electrolyzer and proton exchange membrane fuel cell.

[0134] Uncertainty modeling module 204 is used to perform statistical analysis on the historical output data of wind turbines and photovoltaic devices in the electro-hydrogen coupling system. It uses wavelet transform to construct the uncertainty set of wind turbine and photovoltaic output and introduces uncertainty adjustment parameters to characterize the total period of the boundary of the fluctuation range of wind turbine and photovoltaic output within the scheduling cycle.

[0135] The robust optimization modeling module 205 establishes a robust optimization model for the electro-hydrogen coupling system based on a deterministic optimization model and an uncertainty set. The robust optimization model aims to minimize the daily operating cost of the system under the worst-case scenario of wind turbine and photovoltaic output, and rewrites the power balance constraints in the deterministic optimization model to incorporate uncertainty variables.

[0136] The conservative adjustment module 206 sets the conservative adjustment method of the robust optimization model based on the uncertainty adjustment parameter. By changing the value of the uncertainty adjustment parameter, the conservatism of the robust optimization result is adjusted.

[0137] The algorithm selection module 207 is used to select a column constraint generation algorithm as the solution method for the robust optimization model. It decomposes the robust optimization model into a main problem and sub-problems, and combines strong binary theory to transform the sub-problems from minimax problems into maximal structure problems. It introduces auxiliary variables and parallel variables to match the constraint requirements of the maximal structure problem.

[0138] The solution module 208 is used to obtain the optimal operation scheduling scheme of the electric-hydrogen coupling system under the worst-case conditions of wind turbine and photovoltaic output by using a column constraint generation algorithm to iteratively solve the main problem and the transformed subproblems until the iteration converges.

[0139] like Figure 3 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of a robust optimization operation method for an electro-hydrogen coupling system.

[0140] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0141] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0142] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0143] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0144] The present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the robust optimization operation method for the electro-hydrogen coupling system.

[0145] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] The storage medium stores a program product capable of implementing the methods described above in this specification. In some possible implementations, various aspects of this disclosure may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Exemplary Methods" section above.

[0147] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A robust optimization method for an electro-hydrogen coupling system, characterized in that, The methods include: Step S101: Construct an electro-hydrogen coupling system; Step S102: Determine the operating parameters of each device in the electro-hydrogen coupling system; Step S103: Based on the operating parameters of each device, establish a deterministic optimization model for the electro-hydrogen coupling system. The deterministic optimization model takes minimizing the daily operating cost of the system as the objective function and includes power balance constraints, electric energy storage constraints, hydrogen storage constraints, external power grid purchase and sale constraints, and power constraints of alkaline electrolyzer and proton exchange membrane fuel cell. Step S104: Statistically analyze the historical output data of the wind turbine and photovoltaic device in the electro-hydrogen coupling system, construct the uncertainty set of the wind turbine and photovoltaic output using the wavelet transform method, and introduce uncertainty adjustment parameters to characterize the total period of the boundary of the wind turbine and photovoltaic output fluctuation interval within the scheduling cycle; Step S105: Based on the deterministic optimization model in S103 and the uncertainty set in S104, establish a robust optimization model for the electro-hydrogen coupling system. The robust optimization model aims to minimize the daily operating cost of the system under the worst-case conditions of wind turbine and photovoltaic output. The power balance constraint in the deterministic optimization model is rewritten to incorporate the uncertainty variables in S104. Step S106: Based on the uncertainty adjustment parameter in S104, set the conservative adjustment method of the robust optimization model. By changing the value of the uncertainty adjustment parameter, adjust the conservatism of the robust optimization result. Step S107: Select the column constraint generation algorithm as the solution method for the robust optimization model in S105. Decompose the robust optimization model into the main problem and sub-problems. Combine the strong binary theory to transform the sub-problems from minimax problems into maximal structure problems. Introduce auxiliary variables and parallel variables to match the constraint requirements of the maximal structure problem. Step S108: Using the column constraint generation algorithm of S107, the main problem and the transformed subproblems are solved iteratively until the iteration converges, and the optimal operation scheduling scheme of the electric-hydrogen coupling system under the worst case of wind turbine and photovoltaic output is obtained.

2. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, The electro-hydrogen coupling system includes a wind turbine, a photovoltaic device, an electric energy storage system, an alkaline electrolyzer, a proton exchange membrane fuel cell, a hydrogen storage device, and an interface for connecting to an external power grid to achieve the interaction between electricity and hydrogen. The specific operating parameters of each device in the electro-hydrogen coupling system include: the cut-in wind speed, rated wind speed, and cut-off wind speed of the fan; the maximum output power, power temperature coefficient, and reference temperature of the photovoltaic device; the initial energy capacity, charging efficiency, discharging efficiency, maximum charge and discharge power, and upper and lower limits of the energy capacity of the electric energy storage system; the operating temperature and ohmic resistance parameters of the alkaline electrolyzer; the total number of single cells, current density, and stack activation area of ​​the proton exchange membrane fuel cell; the upper and lower limits of the capacity of the hydrogen storage device; and the purchase price, sales price, and upper and lower limits of the purchased and sold power from the external power grid.

3. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, Step S103 specifically includes the following methods: S1031: Construct an objective function that aims to minimize the daily operating cost of the system; S1032: Establish power balance constraints to ensure that the sum of the output power of the wind turbine, the output power of the photovoltaic system, the output power of the proton exchange membrane fuel cell, the discharge power of the energy storage system and the power purchased from the external grid are equal to the sum of the input power of the alkaline electrolyzer, the charging power of the energy storage system and the power sold from the external grid. S1033: Set the operating constraints of the energy storage system, including the energy state update equation, charging power upper and lower limit constraints, discharging power upper and lower limit constraints, energy upper and lower limit constraints, and mutual exclusion constraints between charging and discharging states. S1034: Set the operating constraints of the hydrogen storage system, including the hydrogen storage capacity state update equation and the upper and lower limits of the hydrogen storage capacity, wherein the capacity state is related to the hydrogen production of the alkaline electrolyzer and the hydrogen consumption of the proton exchange membrane fuel cell. S1035: Set constraints for external power grid purchase and sale, alkaline electrolyzer power, and proton exchange membrane fuel cell power, including corresponding upper and lower power limits and mutual exclusion constraints for power purchase and sale states.

4. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, Step S104 specifically includes the following methods: S1041: Collect time series data of the output power of the wind turbine and photovoltaic device in the electro-hydrogen coupling system within a preset historical period; S1042: Perform wavelet transform processing on the collected historical power output data of wind turbines and photovoltaics to decompose the power signal and analyze the statistical characteristics of its output fluctuations. S1043: Determine the allowable fluctuation values ​​of wind turbine and photovoltaic output based on statistical characteristics, and introduce integer values ​​between 0 and the scheduling period T as uncertainty adjustment parameters; S1044: Based on the allowable fluctuation value and uncertainty adjustment parameters, construct an uncertainty set including the uncertainty variables of wind turbine output, photovoltaic output, and corresponding binary variables; S1045: Associate the set of uncertainties in wind turbine and photovoltaic outputs with the power balance constraints of the electro-hydrogen coupling system.

5. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, Step S105 specifically includes the following methods: S1051: Based on the objective function of the deterministic optimization model and the uncertainty set of wind turbine and photovoltaic output, construct a robust optimization model objective function with the goal of minimizing the daily operating cost of the system in the worst case. S1052: Incorporate the output uncertainty sets of wind turbines and photovoltaics as constraints into the robust optimization model to ensure that the output uncertainty variables of wind turbines and photovoltaics are within the defined fluctuation range; S1053: Replace the predicted wind turbine output and photovoltaic output values ​​in the power balance constraints of the deterministic optimization model with the corresponding uncertainties in wind turbine output and photovoltaic output, and rewrite the power balance constraints; S1054: Incorporate the constraints of energy storage, external power grid purchase and sale, alkaline electrolyzer power, and proton exchange membrane fuel cell power from the deterministic optimization model into the robust optimization model; S1055: Define the set of variables for the robust optimization model, including binary optimization variables and continuous optimization variables, and limit the range of values ​​for each variable.

6. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, Step S106 specifically includes the following methods: The initial value of the uncertainty adjustment parameter Γ is set to 0, which corresponds to the completely deterministic optimization scenario; The value of Γ is set to a series of discrete integer values ​​from 0 to the scheduling period T, forming a sequence of robust optimization problems with different levels of conservatism; For each set Γ value, execute the robust optimization model established in S105 to solve for the optimal operation scheduling scheme under the conservative level and the corresponding worst-case daily operating cost; Record and analyze the characteristics of the optimal operation scheduling scheme and its worst-case daily operating cost corresponding to different Γ values, and establish the correspondence between Γ values ​​and the economic and conservative aspects of system operation; Based on the analysis results and according to the specific requirements of the system operator for risk tolerance, the most suitable Γ value and its corresponding optimal operation scheduling scheme are selected from a series of scheduling schemes obtained from the solution as the final implementation scheme.

7. The robust optimization operation method for the electro-hydrogen coupling system according to claim 1, characterized in that, Step S107 specifically includes the following methods: The robust optimization model established in S105 is decomposed into a main problem and a sub-problem; The subproblems are reconstructed from the original minimax problem into a single maximization problem by introducing dual and auxiliary variables corresponding to each constraint. Auxiliary variables and corresponding constraints are introduced into the main problem to record and integrate the worst-case uncertainty scenarios returned by the subproblems in each iteration and their impact on the objective function; Set an iterative convergence criterion and determine whether the algorithm has met the convergence requirements by calculating the difference between the objective function value of the main problem and the objective function value of the subproblems; An iterative solution mechanism is established between the main problem and the subproblems, and the optimal solution of the robust optimization problem is gradually approached through information interaction between the two.

8. A robust optimized operating system for an electro-hydrogen coupling system, characterized in that, The system is used to implement the robust optimization operation method for the electro-hydrogen coupling system as described in any one of claims 1 to 7; The system includes: The system architecture building module is used to construct an electro-hydrogen coupling system; The parameter determination module is used to determine the operating parameters of each device in the electro-hydrogen coupling system; The deterministic modeling module establishes a deterministic optimization model for the electro-hydrogen coupling system based on the operating parameters of each device. The deterministic optimization model takes minimizing the daily operating cost of the system as the objective function and includes power balance constraints, electrical energy storage constraints, hydrogen storage constraints, external power grid purchase and sale constraints, and power constraints of alkaline electrolyzer and proton exchange membrane fuel cell. The uncertainty modeling module is used to perform statistical analysis on the historical output data of wind turbines and photovoltaic devices in the electro-hydrogen coupling system. It uses wavelet transform to construct the uncertainty set of wind turbine and photovoltaic output and introduces uncertainty adjustment parameters to characterize the total period of the boundary of the fluctuation range of wind turbine and photovoltaic output within the scheduling cycle. The robust optimization modeling module establishes a robust optimization model for the electro-hydrogen coupling system based on a deterministic optimization model and an uncertainty set. The robust optimization model aims to minimize the daily operating cost of the system under the worst-case conditions of wind turbine and photovoltaic output, and rewrites the power balance constraints in the deterministic optimization model to incorporate uncertainty variables. The conservative adjustment module sets the conservative adjustment method of the robust optimization model based on the uncertainty adjustment parameter. By changing the value of the uncertainty adjustment parameter, the conservatism of the robust optimization result is adjusted. The algorithm selection module is used to select the column constraint generation algorithm as the solution method for the robust optimization model. It decomposes the robust optimization model into the main problem and sub-problems, and combines strong binary theory to transform the sub-problems from minimax problems into maximal structure problems. It introduces auxiliary variables and parallel variables to match the constraint requirements of the maximal structure problem. The solution-solving module is used to generate a column constraint algorithm to iteratively solve the main problem and the transformed subproblems until the iteration converges, thereby obtaining the optimal operation and scheduling scheme of the electro-hydrogen coupling system under the worst-case conditions of wind turbine and photovoltaic output.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the robust optimization operation method for the electro-hydrogen coupling system as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the robust optimization operation method for the electro-hydrogen coupling system as described in any one of claims 1 to 7.

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