Optimal Operation Decision-Making Method for Renewable Energy Hydrogen Production Stations Based on Information Gap Decision Theory

Abstract

This invention relates to the field of renewable energy operation decision-making methods, and in particular to an optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory. The method comprises the following steps: Step 1, establishing a deterministic model for the operation decision of the renewable energy hydrogen production plant; Step 2, establishing an optimal decision-making model for the renewable energy hydrogen production plant based on information gap decision theory; Step 3, obtaining the contracted electricity volume, spot market electricity volume, and hydrogen production of the renewable energy hydrogen production plant based on the decision-making model. This invention establishes a deterministic optimal decision-making model for renewable energy hydrogen production plants. This application can help renewable energy hydrogen production plants, when facing multiple uncertainties, make decisions that control the total cost below the acceptable maximum cost, thereby ensuring their basic economic benefits, and can provide a valuable reference for the operation decision-making of renewable energy hydrogen production plants.

Description

Technical Field

[0001] This invention relates to the field of renewable energy operation decision-making methods, and in particular to an optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory. Background Technology

[0002] Hydrogen energy is not only one of the most important and promising clean energy sources, but using renewable energy to produce hydrogen can also significantly reduce hydrogen production costs while ensuring the full utilization of renewable energy and reducing wind and solar power curtailment. Therefore, renewable energy-based hydrogen production has gradually become an important model for large-scale hydrogen production. With the gradual advancement of my country's power market reform, renewable energy hydrogen production plants will also gradually participate in the power market. However, in the current power market environment, renewable energy output and electricity market prices face multiple uncertainties. Traditional optimized operation methods for renewable energy hydrogen production plants do not consider these uncertainties, resulting in unreliable profitability and an inability to effectively improve the returns of renewable energy hydrogen production plants. Therefore, an optimal operation decision-making method for renewable energy hydrogen production plants is needed. Summary of the Invention

[0003] The technical problem this invention aims to solve is as follows: To address the technical problems described in the background art, this invention provides an optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory. This application comprehensively considers typical scenarios of renewable energy output and the prediction deviation of electricity spot market prices, establishing a deterministic optimal decision-making model for renewable energy hydrogen production plants. This application establishes an optimal operation decision-making model for renewable energy hydrogen production plants based on information gap decision theory, taking into account the uncertainty of spot prices. The decisions given by this model can ensure that the total cost of the renewable energy hydrogen production plant does not exceed its maximum acceptable value. This application can help renewable energy hydrogen production plants, when facing multiple uncertainties, to make decisions that control the total cost below the maximum acceptable cost, thereby ensuring their basic economic benefits and providing a valuable reference for the operation decision-making of renewable energy hydrogen production plants.

[0004] The technical solution adopted by this invention to solve its technical problem is:

[0005] An optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory, comprising the following steps:

[0006] Step 1: Establish a deterministic model for the operation of renewable energy hydrogen production stations, and determine the internal physical constraints of the hydrogen production station, the external constraints of the hydrogen production station's participation in the electricity market, and the objective function for minimizing the operating costs of the hydrogen production station;

[0007] Step 2: Establish an optimal decision-making model for renewable energy hydrogen production stations based on information gap decision theory, generate typical renewable energy output scenarios based on stochastic scenarios, and combine the uncertainty of electricity spot market prices, the deterministic model of renewable energy hydrogen production station operation decisions, and information gap decision theory to determine the optimal decision-making model for renewable energy hydrogen production stations that considers multiple uncertainties.

[0008] Step 3: Based on the decision-making model for renewable energy hydrogen production stations, obtain the contracted electricity volume, spot market electricity volume, and hydrogen production of renewable energy hydrogen production stations.

[0009] Specifically, the optimal decision model includes an objective function and constraints.

[0010] Specifically, in step one, the physical constraints of the internal operation of the hydrogen production station include an electrolyzer model, a hydrogen compressor model, a hydrogen storage tank model, and an electrical energy storage model.

[0011] The constraints on the external participation of hydrogen production stations in the electricity market include monthly contract power allocation constraints, power balance constraints, wind power output dispatch constraints, and hydrogen production plan constraints.

[0012] The objective function for minimizing the operating cost of the hydrogen production station takes into account both the cost of purchasing electricity from the spot market and the operation and maintenance costs of the hydrogen production equipment.

[0013] Specifically, in step two, the objective function is to maximize the acceptable electricity price prediction error;

[0014] The constraints include acceptable cost constraints, prediction error constraints, and deterministic model constraints.

[0015] Specifically, the electrolytic cell model is as follows:

[0016]

[0017] in, The amount of hydrogen produced by the electrolyzer during time period t; The electrical power input to the electrolytic cell during time period t; The hydrogen production efficiency of the electrolyzer;

[0018] The range of electrical power input to the electrolytic cell:

[0019]

[0020] in, This indicates the maximum electrical power input to the electrolytic cell;

[0021] The power ramp-up range of the input electrolytic cell:

[0022]

[0023] in, This is the maximum ramping power of the electrolytic cell;

[0024] The hydrogen compressor model is as follows:

[0025]

[0026] in, Let t be the electrical power input to the hydrogen compressor; The specific heat capacity constant of hydrogen; Let T be the mass flow rate of hydrogen compressed by the compressor at time t; in The temperature at which hydrogen is input to the compressor; η com κ represents the compressor's operating efficiency; R represents the isentropic exponent of hydrogen. comp This represents the compression ratio of hydrogen.

[0027] Input power range for the compressor:

[0028]

[0029] in, This is the maximum electrical power input to the hydrogen compressor;

[0030] The hydrogen storage tank model is as follows:

[0031]

[0032] in, T represents the internal gas pressure of the hydrogen storage tank during time period t; s V represents the temperature of the hydrogen gas inside the hydrogen storage tank. s This refers to the volume of the hydrogen storage tank; The amount of hydrogen used during time period t;

[0033] Internal pressure range of hydrogen storage tank:

[0034]

[0035] in, This indicates the minimum gas pressure inside the hydrogen storage tank; This indicates the maximum gas pressure inside the hydrogen storage tank;

[0036] The energy storage charging and discharging power range of the energy storage model is as follows:

[0037]

[0038] in, It is a 0-1 indicator variable; when At time t, the energy storage device is in a charging state; when At that time, the energy storage device is in a discharging state during the time period t; The charging power during time period t; Let be the discharge power during time period t; This indicates the maximum charging power of the energy storage device; This indicates the minimum charging power of the energy storage device.

[0039] Specifically, the monthly contract electricity volume decomposition constraint is as follows:

[0040]

[0041] Where T represents the number of time periods in a month; Q represents the electricity volume allocated from medium- and long-term contracts to time period t; C This indicates the total monthly electricity volume under medium- and long-term contracts for renewable energy hydrogen production plants.

[0042] The power balance constraint is:

[0043]

[0044] in, This represents the amount of electricity purchased by renewable energy power plants from the spot market during time period t; w t This indicates the dispatch output of the wind turbine units during time period t;

[0045] The wind power output scheduling constraint is:

[0046]

[0047] in, This represents the predicted output of the wind turbine during time period t;

[0048] The hydrogen production plan constraints are as follows:

[0049]

[0050] in, This represents the amount of hydrogen produced during time period t; This indicates the total amount of hydrogen to be delivered as agreed in the hydrogen order.

[0051] Specifically, the electricity purchase cost in the electricity spot market is C. P ,

[0052]

[0053] in, The electricity spot price for period t;

[0054] The operation and maintenance cost of the hydrogen production equipment is C. M

[0055]

[0056] Among them, C EL Indicates the operating and maintenance cost of the electrolytic cell; C ES This represents the operation and maintenance cost of electrical energy storage; β EL This represents the operating and maintenance cost coefficient of the electrolytic cell; β ES This represents the operation and maintenance cost coefficient of electrical energy storage;

[0057] The objective function for minimizing the operating cost of a hydrogen production facility is C. ∑ ,

[0058] C Σ =min(C P +C M ).

[0059] Specifically, the maximum acceptable electricity price prediction error is:

[0060]

[0061] Where S represents the set of renewable energy unit output scenarios; S = {s1, s2, ..., s...} i ,...s N}; The number of scenarios is N, where s1, s2, si, sN represent different output scenarios; ρ s,t Prob represents the error between the spot market clearing price and the predicted price in scenario s. s Let be the probability of scenario s.

[0062] Specifically, the acceptable cost constraint is as follows:

[0063] C ac ≤(1+β)·C Σ ;

[0064]

[0065] Where Cac represents the acceptable cost; β represents the cost acceptability coefficient; Prob s Let be the probability of scenario s; Electricity purchase cost in the spot market under scenario s; Operation and maintenance costs of energy storage in scenario s; The operating and maintenance costs of the electrolytic cell;

[0066] The prediction error constraint is:

[0067]

[0068] Where, ρ s,t This represents the error between the spot market clearing price and the predicted price in scenario s. The actual spot market electricity price under scenario s;

[0069] The constraints of the deterministic model are:

[0070] A T x s ≤b s

[0071] Where AT represents the coefficient matrix of the deterministic model; xs represents the decision variables of the deterministic model in scenario s; and bs represents the constant matrix of the deterministic model in scenario s.

[0072] The beneficial effects of this invention are as follows: This invention provides an optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory. This application comprehensively considers typical scenarios of renewable energy output and the prediction deviation of electricity spot market prices, establishing a deterministic optimal decision-making model for renewable energy hydrogen production plants. This application establishes an optimal operation decision-making model for renewable energy hydrogen production plants based on information gap decision theory and taking into account the uncertainty of spot prices. The decisions given by this model can ensure that the total cost of the renewable energy hydrogen production plant does not exceed its maximum acceptable value. This application can help renewable energy hydrogen production plants, when facing multiple uncertainties, to make decisions that control the total cost below the maximum acceptable cost, thereby ensuring their basic economic benefits and providing a valuable reference for the operation decision-making of renewable energy hydrogen production plants. Attached Figure Description

[0073] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0074] Figure 1 This is a flowchart of the present invention;

[0075] Figure 2 This is a power output scenario diagram of renewable energy according to the present invention;

[0076] Figure 3 This is the spot market forecast electricity price map of the present invention;

[0077] Figure 4 This is a diagram illustrating the optimal operation strategy for renewable energy power plants under scenario 1 of this invention.

[0078] Figure 5 This is a diagram illustrating the optimal operation strategy for renewable energy power plants under scenario 2 of this invention.

[0079] Figure 6 This is a diagram illustrating the optimal operation strategy for renewable energy power plants under scenario 3 of this invention. Detailed Implementation

[0080] Figure 1 This is a flowchart of the present invention; Figure 2This is a power output scenario diagram of renewable energy according to the present invention; Figure 3 This is the book

[0081] Invented spot market electricity price forecasting chart; Figure 4 This is a diagram illustrating the optimal operation strategy for renewable energy power plants under scenario 1 of this invention. Figure 5 This is a diagram illustrating the optimal operation strategy for renewable energy power plants under scenario 2 of this invention. Figure 6 This is the optimal operation strategy diagram for renewable energy power plants under scenario 3 of the present invention.

[0082] As attached Figure 1 As shown, an optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory is presented. The steps of this method are as follows:

[0083] Step 1: Establish a deterministic model for the operation of renewable energy hydrogen production stations, and determine the internal physical constraints of the hydrogen production station, the external constraints of the hydrogen production station's participation in the electricity market, and the objective function for minimizing the operating costs of the hydrogen production station;

[0084] The physical constraints of the internal operation of the hydrogen production station include the electrolyzer model, the hydrogen compressor model, the hydrogen storage tank model, and the electrical energy storage model.

[0085] The electrolytic cell model is as follows:

[0086]

[0087] in, The amount of hydrogen produced by the electrolyzer during time period t; The electrical power input to the electrolytic cell during time period t; The hydrogen production efficiency of the electrolyzer;

[0088] The range of electrical power input to the electrolytic cell:

[0089]

[0090] in, This indicates the maximum electrical power input to the electrolytic cell;

[0091] The power ramp-up range of the input electrolytic cell:

[0092]

[0093] in, This represents the maximum ramping power of the electrolytic cell.

[0094] The hydrogen compressor model is as follows:

[0095]

[0096] in, Let t be the electrical power input to the hydrogen compressor; The specific heat capacity constant of hydrogen; Let T be the mass flow rate of hydrogen compressed by the compressor at time t; in The temperature at which hydrogen is input to the compressor; η com κ represents the compressor's operating efficiency; R represents the isentropic exponent of hydrogen. comp This represents the compression ratio of hydrogen.

[0097] Input power range for the compressor:

[0098]

[0099] in, This represents the maximum electrical power input to the hydrogen compressor.

[0100] The hydrogen storage tank model is as follows:

[0101]

[0102] in, T represents the internal gas pressure of the hydrogen storage tank during time period t; s V represents the temperature of the hydrogen gas inside the hydrogen storage tank. s This refers to the volume of the hydrogen storage tank; The amount of hydrogen used during time period t;

[0103] Internal pressure range of hydrogen storage tank:

[0104]

[0105] in, This indicates the minimum gas pressure inside the hydrogen storage tank; This indicates the maximum gas pressure inside the hydrogen storage tank.

[0106] The energy storage charging and discharging power range of the energy storage model:

[0107]

[0108] in, It is a 0-1 indicator variable; when At time t, the energy storage device is in a charging state; when At that time, the energy storage device is in a discharging state during the time period t; The charging power during time period t; Let be the discharge power during time period t; This indicates the maximum charging power of the energy storage device; This indicates the minimum charging power of the energy storage device.

[0109] External constraints on hydrogen production facilities' participation in the electricity market include monthly contracted electricity volume allocation constraints, power balance constraints, wind power output dispatch constraints, and hydrogen production plan constraints.

[0110] The monthly contract electricity volume decomposition constraints are as follows:

[0111]

[0112] Where T represents the number of time periods in a month; Q represents the electricity volume allocated from medium- and long-term contracts to time period t; C This indicates the total monthly electricity volume under medium- and long-term contracts for renewable energy hydrogen production plants.

[0113] The power balance constraints are:

[0114]

[0115] in, This represents the amount of electricity purchased by renewable energy power plants from the spot market during time period t; w t This indicates the dispatch output of the wind turbine units during time period t.

[0116] The wind power output dispatch constraints are:

[0117]

[0118] in, This represents the predicted output of the wind turbine during time period t.

[0119] Hydrogen production planning constraints are:

[0120]

[0121] in, This represents the amount of hydrogen produced during time period t; This indicates the total amount of hydrogen to be delivered as agreed in the hydrogen order.

[0122] The objective function for minimizing the operating costs of hydrogen production plants takes into account both the cost of purchasing electricity from the spot market and the operation and maintenance costs of the hydrogen production equipment.

[0123] The electricity purchase cost in the spot market is C. P ,

[0124]

[0125] in, The electricity spot price for period t.

[0126] The operation and maintenance cost of the hydrogen production equipment is C. M ,

[0127]

[0128] Among them, C EL Indicates the operating and maintenance cost of the electrolytic cell; C ES This represents the operation and maintenance cost of electrical energy storage; β EL This represents the operating and maintenance cost coefficient of the electrolytic cell; β ES This represents the operation and maintenance cost coefficient of electrical energy storage;

[0129] The objective function for minimizing the operating cost of a hydrogen production facility is C. ∑ ,

[0130] C Σ =min(C P +C M ).

[0131] Step 2: Establish an optimal decision-making model for renewable energy hydrogen production stations based on information gap decision theory, generate typical renewable energy output scenarios based on stochastic scenarios, and combine the uncertainty of electricity spot market prices, the deterministic model of renewable energy hydrogen production station operation decisions, and information gap decision theory to determine the optimal decision-making model for renewable energy hydrogen production stations that considers multiple uncertainties.

[0132] The optimal decision model includes an objective function and constraints. The objective function is to maximize the acceptable electricity price prediction error; the constraints include acceptable cost constraints, prediction error constraints, and deterministic model constraints.

[0133] The maximum acceptable electricity price forecast error is:

[0134]

[0135] Where S represents the set of renewable energy unit output scenarios; S = {s1, s2, ..., s...} i ,...s N}; The number of scenarios is N, where s1, s2, si, sN represent different output scenarios; ρ s,t Prob represents the error between the spot market clearing price and the predicted price in scenario s. s Let be the probability of scenario s.

[0136] Acceptable cost constraints are:

[0137] C ac ≤(1+β)·C Σ ;

[0138]

[0139] Where Cac represents the acceptable cost; β represents the cost acceptability coefficient; Prob s Let be the probability of scenario s; Electricity purchase cost in the spot market under scenario s; Operation and maintenance costs of energy storage in scenario s; This refers to the operation and maintenance costs of the electrolytic cell.

[0140] The prediction error constraint is:

[0141]

[0142] Where, ρ s,t This represents the error between the spot market clearing price and the predicted price in scenario s. The actual spot market electricity price under scenario s.

[0143] The constraints of the deterministic model are:

[0144] A T x s ≤b s

[0145] Where AT represents the coefficient matrix of the deterministic model; xs represents the decision variables of the deterministic model in scenario s; and bs represents the constant matrix of the deterministic model in scenario s.

[0146] Step 3: Based on the decision-making model for renewable energy hydrogen production stations, obtain the contracted electricity volume, spot market electricity volume, and hydrogen production of renewable energy hydrogen production stations.

[0147] By incorporating the uncertainties of renewable energy output and electricity market prices into the optimal decision-making model for hydrogen production plants, and based on the information gap decision theory, the optimal operation decision-making model for renewable energy hydrogen production plants is solved, yielding the contracted electricity output, spot market electricity purchases, and hydrogen production of renewable energy hydrogen production plants.

[0148] First, as attached Figure 2 As shown, three typical wind power output scenarios are selected. Secondly, as attached... Figure 3 As shown, the spot market prices are projected for different periods within the month. Finally, refer to the attached table. Figure 4 Appendix Figure 5 Appendix Figure 6 As shown, the optimal operating strategy for renewable energy hydrogen production stations is determined under various scenarios, including the contracted electricity output, spot market electricity purchases, and hydrogen production.

[0149] The optimal operation strategy for renewable energy hydrogen production plants proposed in this application, considering multiple uncertainties, involves a long decision-making period, totaling 30 days (720 hours). To more clearly illustrate the results, this embodiment only shows the monthly electricity contract breakdown, spot market purchases, and hydrogen production at different times under different renewable energy output scenarios on a typical day (day 15). Typically, during peak electricity load periods (11:00-14:00), spot market prices are high, leading to reduced spot market purchases and increased monthly contract breakdown for renewable energy hydrogen production plants. Simultaneously, the increased spot market price raises hydrogen production costs, resulting in lower hydrogen production during this period. From 14:00-16:00, hydrogen production gradually increases as spot prices decrease and wind power output increases. From 16:00-19:00, although spot market prices are low and spot market purchases gradually increase, hydrogen production gradually decreases due to a significant reduction in wind turbine output. After 7 PM, hydrogen production gradually increases as wind turbine output recovers and spot market electricity purchases increase. This patent application, by comprehensively considering the impact of multiple uncertainties, enables the solution obtained from the proposed optimal operation decision model for renewable energy hydrogen production plants to provide reasonable decision-making for controlling total costs, compared to traditional methods, while ensuring that the predicted and actual spot market prices are within the maximum permissible error range.

[0150] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for optimal operation decision-making of renewable energy hydrogen production plants based on information gap decision theory, characterized in that, The steps of this method are as follows: Step 1: Establish a deterministic model for the operation of renewable energy hydrogen production stations, and determine the internal physical constraints of the hydrogen production station, the external constraints of the hydrogen production station's participation in the electricity market, and the objective function for minimizing the operating costs of the hydrogen production station; Step 2: Establish an optimal decision-making model for renewable energy hydrogen production stations based on information gap decision theory, generate typical renewable energy output scenarios based on stochastic scenarios, and combine the uncertainty of electricity spot market prices, the deterministic model of renewable energy hydrogen production station operation decisions, and information gap decision theory to determine the optimal decision-making model for renewable energy hydrogen production stations that considers multiple uncertainties. Step 3: Based on the decision-making model for renewable energy hydrogen production stations, obtain the contracted electricity volume, spot market electricity volume, and hydrogen production of renewable energy hydrogen production stations; The optimal decision model includes an objective function and constraints; In step one, the physical constraints of the internal operation of the hydrogen production station include the electrolyzer model, the hydrogen compressor model, the hydrogen storage tank model, and the electrical energy storage model. The constraints on the external participation of hydrogen production stations in the electricity market include monthly contract power allocation constraints, power balance constraints, wind power output dispatch constraints, and hydrogen production plan constraints. The objective function for minimizing the operating cost of the hydrogen production station comprehensively considers the electricity purchase cost in the spot market and the operation and maintenance cost of the hydrogen production equipment. In step two, the objective function is to maximize the acceptable electricity price prediction error; The constraints include acceptable cost constraints, prediction error constraints, and deterministic model constraints. The electrolytic cell model is as follows: ； in, The amount of hydrogen produced by the electrolyzer during time period t; The electrical power input to the electrolytic cell during time period t; The hydrogen production efficiency of the electrolyzer; Input power range for the electrolytic cell: ； in, This indicates the maximum electrical power input to the electrolytic cell; Input power ramp-up range for the electrolytic cell: ； in, This is the maximum ramping power of the electrolytic cell; The hydrogen compressor model is as follows: ； in, for The electrical power of the hydrogen compressor must be constantly input; The specific heat capacity constant of hydrogen; for The mass flow rate of hydrogen compressed by the compressor at all times; The temperature at which hydrogen is input to the compressor; To improve the compressor's operating efficiency; The isentropic index of hydrogen; This represents the compression ratio of hydrogen. Input power range for the compressor: ； in, This is the maximum electrical power input to the hydrogen compressor; The hydrogen storage tank model is as follows: ； in, This indicates the internal gas pressure of the hydrogen storage tank during time period t; The temperature of the hydrogen gas inside the hydrogen storage tank; This refers to the volume of the hydrogen storage tank; The amount of hydrogen used during time period t; Internal pressure range of hydrogen storage tank: ； in, This indicates the minimum gas pressure inside the hydrogen storage tank; This indicates the maximum gas pressure inside the hydrogen storage tank; The energy storage charging and discharging power range of the energy storage model is as follows: ； in, It is a 0-1 indicator variable; when At time t, the energy storage device is in a charging state; when At that time, the energy storage device is in a discharging state during the time period t; The charging power during time period t; Let be the discharge power during time period t; This indicates the maximum charging power of the energy storage device; Indicates the minimum charging power of the energy storage device; The maximum acceptable electricity price prediction error is: ； Where S represents the set of scenarios for renewable energy unit output; The number of scenarios is N, where s1, s2, si, and sN represent different power output scenarios. This represents the error between the spot market clearing price and the predicted price in scenario s. Let be the probability of scenario s.

2. The optimal operation decision-making method for renewable energy hydrogen production plants based on information gap decision theory according to claim 1, characterized in that: The monthly contract electricity volume decomposition constraint is as follows: ； Where T represents the number of time periods in a month; This represents the electricity volume under medium- and long-term contracts allocated to time period t. This indicates the total monthly electricity volume under medium- and long-term contracts for renewable energy hydrogen production plants. The power balance constraint is: ； in, Indicates renewable energy plants Electricity purchased from the spot market during a given period; The dispatch output of wind turbine units during time period t; The wind power output scheduling constraint is: ； in, This represents the predicted output of the wind turbine during time period t; The hydrogen production plan constraints are as follows: ； in, express Hydrogen produced during that period; This indicates the total amount of hydrogen to be delivered as agreed in the hydrogen order.

3. The optimal operation of renewable energy hydrogen production stations based on information gap decision theory as described in claim 1. The decision-making method is characterized by: The electricity purchase cost in the electricity spot market is , ； in, The electricity spot price for period t; The operation and maintenance cost of the hydrogen production equipment is : ； in, This indicates the operating and maintenance costs of the electrolytic cell; This indicates the operation and maintenance costs of electrical energy storage; This represents the operating and maintenance cost coefficient of the electrolytic cell; This represents the operation and maintenance cost coefficient of electrical energy storage; The objective function for minimizing the operating cost of hydrogen production facilities is: , 。 4. The optimal operation of renewable energy hydrogen production stations based on information gap decision theory as described in claim 1. The decision-making method is characterized by: The acceptable cost constraint is as follows: ； ； Cac represents the acceptable cost; Represents the cost acceptance coefficient; Let be the probability of scenario s; The cost of purchasing electricity in the spot market under scenario s; Operation and maintenance costs of energy storage in scenario s; The operation and maintenance costs of electrolytic cells; The prediction error constraint is: ； in, This represents the error between the spot market clearing price and the predicted price in scenario s. The actual spot market electricity price under scenario s; The constraints of the deterministic model are: ； Where AT represents the coefficient matrix of the deterministic model; xs represents the decision variables of the deterministic model in scenario s; and bs represents the constant matrix of the deterministic model in scenario s.

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