A method for preparing flexible hydrogen production and storage of alcohols using wind power
By optimizing the wind power flexible hydrogen production and storage methanol system using differential evolution algorithm, the capacity configuration problem of the wind power, hydrogen production, hydrogen storage and methanol synthesis chain was solved, which improved the wind power absorption capacity and the stable and efficient methanol production, reduced construction costs and improved the project's internal rate of return.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG YUE HYDROPOWER ENERGY INVESTMENT GROUP CO LTD
- Filing Date
- 2026-03-20
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies have failed to effectively apply genetic algorithms to solve the capacity configuration problem in the wind power, hydrogen production, hydrogen storage, and methanol synthesis chain, resulting in difficulties in system optimization, complex parameters, and difficulty in achieving synergistic optimization of wind power consumption and the hydrogen and methanol industry chains.
By employing the differential evolution algorithm and combining wind power output and dynamic electricity price data, a multi-energy flow collaborative scheduling strategy is constructed to optimize the wind power flexible hydrogen production and storage system. By collecting wind power output sequences and dynamic electricity price data in the target area, a methanol production hierarchical control strategy and a multi-energy flow collaborative scheduling model are established to optimize equipment configuration and improve the system's economic performance and wind power absorption capacity.
This has improved wind power absorption capacity, ensured stable and efficient methanol production, reduced construction costs, increased the project's internal rate of return, enhanced investment value and profitability, and improved the system's operational practicality and engineering feasibility within compliance boundaries.
Smart Images
Figure CN122491980A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wind power-to-hydrogen coupled methanol production technology, and in particular to a method for preparing flexible wind power-based hydrogen production and storage methanol. Background Technology
[0002] With the rapid growth of wind power installed capacity, the problems of wind power consumption and intermittency are becoming increasingly prominent. Wind power-to-hydrogen coupled with methanol synthesis technology can convert abandoned wind into hydrogen energy for storage and further synthesize methanol, realizing cross-form energy storage and value enhancement. However, this system involves the coupling of multiple units such as wind power output, hydrogen electrolyzer, hydrogen storage, and methanol synthesis, with many parameters and complex constraints. It is urgent to adopt an efficient optimization configuration strategy. The strategy needs to be able to effectively adapt to the characteristics of wind power flexible hydrogen production system with high parameter sensitivity and high precision requirements of hydrogen storage and methanol system. At the same time, the complex coupling parameters also place extremely high demands on the global search capability, convergence speed and solution accuracy of the algorithm itself.
[0003] In such complex system optimization problems, genetic algorithms (GA) exhibit significant advantages, as detailed below:
[0004] First, genetic algorithms search based on a population and evaluate multiple solutions simultaneously through operations such as selection, crossover, and mutation. This effectively avoids getting trapped in local optima and makes it easier to determine the globally optimal or near-optimal system configuration in a solution space with multiple peaks and complex coupling relationships.
[0005] Second, genetic algorithms do not rely on problem gradient information and have no strict requirements on the continuity and differentiability of the objective function and constraints. They are suitable for mixed integer programming problems that include nonlinearity, discrete variables and multiple complex constraints.
[0006] Third, the individual algorithms in the population are evaluated independently, making them suitable for parallel computing using modern computing technology; thus accelerating the solution process and meeting the convergence speed requirements of large-scale system optimization.
[0007] Fourth, genetic algorithms guide the search through probability rules, are not sensitive to the setting of the initial solution, and their search results are less affected by the initial population. They can still maintain stable optimization performance when there is uncertainty in the system model parameters.
[0008] Fifth, the algorithm uses an encoding method to process decision variables, which can handle both continuous and discrete variables simultaneously. It also incorporates various complex constraints through penalty functions or special encoding mechanisms to meet the needs of refined system modeling.
[0009] In the current technology, there is no systematic application of genetic algorithms to solve the capacity configuration problem of a specific link such as wind power, hydrogen production, hydrogen storage and methanol synthesis. The optimization potential of genetic algorithms in this field has not been fully explored.
[0010] Therefore, there is an urgent need to provide a flexible configuration method for producing and storing hydrogen and methanol from wind power. Compared with existing technologies, this method achieves synergistic optimization and improvement of system economic performance and wind power absorption capacity, and provides a configuration strategy that is both scientific and practical for the efficient conversion of wind power resources and the coordinated operation of the hydrogen and methanol industry chains. Summary of the Invention
[0011] This invention addresses the technical problems existing in the prior art and provides a method for preparing flexible hydrogen production and storage systems for wind power.
[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for preparing flexible hydrogen production and storage systems using wind power includes the following steps: S1. Collect the predicted wind power output sequence and corresponding dynamic electricity price data for the entire year in the target area, and construct a standardized sequence dataset; S2. Set system operating parameters, including basic parameters, methanol production parameters, and economic parameters; calculate the internal rate of return based on the system operating parameters. S3. Establish a graded control strategy for methanol production. By calculating the total available energy and setting a benchmark energy, the actual total methanol production is obtained by graded control based on the total available energy and the benchmark energy. S4. Based on the standardized sequence dataset constructed in step S1, establish a multi-energy flow collaborative scheduling strategy; S5. Using the internal rate of return obtained in step S2 as the objective function, and the electrolyzer power and blower scale as decision variables, construct a methanol production scale configuration optimization model. S6. Set the constraints for the methanol production scale configuration optimization model constructed in step S5, including energy balance constraints, hydrogen storage tank state recursion constraints, and energy storage battery state constraints. S7. Under the constraints of step S6, the objective function of step S5 is solved using the differential evolution algorithm to obtain the wind turbine size and electrolyzer power under the optimal IRR. S8. Based on the wind turbine scale, electrolyzer power, and corresponding annual methanol production and electricity purchase obtained in step S7, introduce detailed financial evaluation parameters, conduct a full life cycle techno-economic assessment, and output key financial indicators.
[0013] Furthermore, the methanol production staged control strategy constructed in step S3 is as follows: (1) When the total available energy / Baseline Energy When the energy level is greater than or equal to the first threshold, there is an energy surplus and the actual total output is [not specified]. Specifically, it is calculated using the following formula: ; In the above formula, Indicates the baseline output; (2) When the second threshold is less than or equal to the total available energy / Baseline Energy When the first threshold is reached, the actual total output is... Specifically, it is calculated using the following formula: ; ; In the above formula, r represents the energy availability rate. Indicates to Round the result down to the nearest integer. (3) When the total available energy of the system / Baseline Energy Production ceases when the threshold is reached. The first threshold is greater than the second threshold.
[0014] Furthermore, the total available energy of the system Specifically, it is calculated using the following formula: ; In the above formula, This represents the wind power output in the k-th time period in the future. This represents the amount of hydrogen stored at time t. This represents the battery charge at time t.
[0015] Furthermore, the method for obtaining the internal rate of return in step S2 is as follows: using basic parameters, methanol production parameters, and economic parameters, a simplified calculation is performed using a solution method combining Newton's iteration method and the bisection method. Once the algorithm converges, the optimal configuration is obtained. Then, an accurate IRR calculation is performed on the optimal configuration to obtain the internal rate of return.
[0016] Furthermore, the objective function in step S5 is expressed as: ; In the above formula, Represents the objective function value. Indicates the internal rate of return. Indicates the proportion of electricity purchased. This represents the value of the electricity purchase ratio constraint. This indicates the annual production of methanol. This represents the annual methanol production constraint value. Indicates the curtailment rate. This represents the constraint value for the power curtailment rate; , , These represent the weighting coefficients corresponding to the electricity purchase ratio, annual methanol production, and power curtailment rate, respectively.
[0017] Furthermore, the energy balance constraint in step S6 is expressed as: ; In the above formula, This represents the total electrical power consumed in methanol production at time t. This represents the direct wind power consumption at time t. This represents the discharge power of the energy storage battery at time t. This indicates the discharge efficiency of the energy storage battery. This represents the equivalent electrical power of hydrogen storage discharge at time t. This represents the power purchased by the power grid at time t. This represents the wind curtailment power at time t.
[0018] Furthermore, the recursive constraint condition for the hydrogen storage tank state in step S6 is expressed as follows: ; In the above formula, This represents the mass of hydrogen in the hydrogen storage tank at time t. Indicates the time step. express Hydrogen mass flow rate at any given time, express The mass flow rate of hydrogen release at any given moment.
[0019] Furthermore, the energy storage battery state constraints in step S6 are expressed as follows: ; In the above formula, This represents the state of charge of the battery at time t. express Battery charging power at all times This indicates the charging efficiency of the energy storage battery. express Battery discharge power at all times This indicates the discharge efficiency of the energy storage battery.
[0020] Furthermore, S7 includes the following steps: S71. Initialize the population, specifically using the following formula: ; In the above formula, This represents the initial solution vector for the i-th individual; This indicates the lower bound of the decision variable. Indicates the upper limit of the decision variable. Indicates population size, Represents a uniformly distributed random number; S72. Perform the mutation operation, specifically using the following formula: ; In the above formula, Let represent the mutation vector of the i-th individual in the g-th generation. This represents a randomly selected baseline individual. This represents the scaling factor, which is 0.8. , This represents randomly selected differential individuals; S73. Perform a crossover operation, specifically a binomial crossover, using the following formula: ; In the above formula, Let i represent the test vector of the i-th individual in the g-th generation. Let i represent the i-th individual in the g-th generation, and j represent the dimension index of the decision variable, j∈{1,2}; Indicates the crossover probability. Indicates a random dimension index; S74. Perform the selection operation, specifically using the following formula: ; In the above formula, Indicates the first For the i-th individual, , Both represent the objective function value, g takes values from 1 to G, and G represents the maximum number of iterations.
[0021] Furthermore, the key financial indicators output by step S8 include investment indicators, cost-profit indicators, and financial analysis indicators.
[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention improves the wind power absorption capacity by using differential evolution algorithm to optimize multivariate co-operation, while ensuring the stability and efficiency of methanol production, and achieving precise matching of energy cross-form conversion.
[0023] (2) This invention takes IRR as the core objective, combines total cost and benefit analysis, optimizes equipment configuration, reduces construction costs, improves project financial internal rate of return and other indicators, and enhances project investment value and profitability.
[0024] (3) The present invention effectively handles constraints such as electricity purchase ratio and equipment scale, ensuring that the system operates within compliant and feasible boundaries, and improving the practicality of the strategy and the feasibility of the project.
[0025] (4) The wind power flexible hydrogen production and storage configuration strategy based on differential evolution algorithm of this invention collects wind power output and dynamic electricity price data of the target area, constructs an optimization model with the project financial internal rate of return as the objective function, and adopts methanol production graded control formula and multi-energy flow collaborative scheduling strategy to complete the optimized configuration of wind power hydrogen production and storage, and achieves the best overall performance. Attached Figure Description
[0026] Figure 1 This is a flowchart of the method of the present invention.
[0027] Figure 2 This is a diagram of the energy management strategy in this invention. Detailed Implementation
[0028] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention. It should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.
[0029] like Figure 1 , Figure 2 As shown, the present invention provides a method for preparing flexible hydrogen production and storage for wind power, comprising the following steps: S1. Collect the predicted wind power output sequence and corresponding dynamic electricity price data for the entire year in the target area, and construct a standardized sequence dataset. The preprocessing includes determining the typical scale of wind power project development and standardizing the predicted wind power output sequence.
[0030] The wind power output sequence is used to simulate the actual power generation capacity of wind turbines at different times. This is mainly used to support the subsequent energy balance simulation of the system and to determine whether it is necessary to purchase electricity, abandon electricity, or charge and discharge energy storage. Dynamic electricity price data is used to consider the cost of purchased electricity. During periods of high electricity price, priority is given to using energy storage or hydrogen storage discharge to reduce the purchase of electricity. During periods of low electricity price, electricity purchase is considered.
[0031] Specifically, in this embodiment, the downtime caused by regular maintenance and sudden fault repair in actual operation is considered. The annual data is calculated as 8400 hours, which is the actual annual utilization hours of the wind turbine. The typical scale of wind power project development is determined to be 750MW. The predicted wind power output data is normalized to decouple the optimization model from the specific scale and realize a configuration scheme that can evaluate any wind power scale.
[0032] S2. Set system operating parameters, including basic parameters, methanol production parameters, and economic parameters; calculate the internal rate of return based on the system operating parameters.
[0033] Specifically, the wind power-to-hydrogen-storage system includes an off-grid wind power section and a chemical section (methanol section and hydrogen production section). In this embodiment, the basic parameters are shown in Table 1, the methanol production parameters are shown in Table 2, and the economic parameters of each part are shown in Tables 3 and 4. The specific data can be adjusted according to the project requirements.
[0034] Using basic parameters, methanol production parameters, and economic parameters, a simplified calculation is performed using a solution method combining Newton's iteration method and the bisection method. Once the algorithm converges, the optimal configuration is obtained. An accurate IRR calculation is then performed on the optimal configuration to obtain the internal rate of return.
[0035] Table 1 Basic Parameters
[0036] Table 2 Methanol Production Parameters
[0037] Table 3 Investment Costs of Each Equipment
[0038] Table 4 Operation and Maintenance Costs
[0039] S3. Establish a methanol production graded control strategy. This strategy dynamically adjusts methanol production based on the total available energy in the next 8 hours. The production grade is controlled according to the ratio of the total available energy to the benchmark value. Based on the principle of uniform distribution, the total production in 8 hours is evenly distributed to each time period.
[0040] The methanol production tiered control strategy is as follows: (1) When the total available energy / Baseline Energy When the energy level is greater than or equal to the first threshold, there is an energy surplus, resulting in a total output over 8 hours. Adjusted to baseline production 110%; the first threshold is preferably 1.1. Specifically, it is calculated using the following formula: .
[0041] (2) When the second threshold ≤ total available energy / Baseline Energy At the first threshold, the actual total output over 8 hours The adjustment mechanism is as follows: based on the total available energy of the system. Reference energy The ratio is rounded down in increments of 0.025, and the resulting multiple is used as the adjustment coefficient and the benchmark output. Multiplying them together gives the actual total output over 8 hours. The second threshold is preferably 0.15. Specifically, it is calculated using the following formula: ; ; In the above formula, r represents the energy availability rate, which is the total available energy. Reference energy The ratio of .
[0042] (3) When the total available energy of the system / Baseline Energy When the energy level is below the first threshold, production is severely insufficient and production ceases.
[0043] Total available energy of the system Specifically, it is calculated using the following formula: ; In the above formula, This represents the wind power output in the k-th time period in the future; The total wind power output in the next 8 hours, because the methanol graded control strategy has an 8-hour production cycle, the formula can be understood as the decision being made only at the beginning of each 8-hour production cycle; This represents the amount of hydrogen stored at time t. This represents the battery charge at time t.
[0044] Based on the data in Tables 1 and 2, the baseline energy can be calculated to be 2905 MWh. However, since the system may enter a high-production mode when the wind turbine output is generally low and a small amount of electricity needs to be purchased, the electricity purchase cost will increase and the overall economic benefits will be reduced. Therefore, it is considered to increase the value of the baseline energy. Thus, in this embodiment, the value of the baseline energy is increased by 4500 MWh.
[0045] S4. Based on the standardized sequence dataset constructed in step S1, establish a multi-energy flow collaborative scheduling strategy. Based on the difference between the real-time output of wind power and the total electricity demand for methanol production within a single scheduling period, distinguish between three operating conditions: energy surplus, energy shortage but able to meet the electricity demand for hydrogen production, and severe energy shortage, and execute the corresponding energy supply, storage, and external purchase scheduling respectively.
[0046] Specifically, it requires the actual wind turbine output for each time period in the S1 step dataset (corresponding to...). Figure 2The data (output input of the top-level fan) is compared with the total electricity demand for methanol production in the same scheduling period to distinguish the three operating conditions.
[0047] The multi-energy retention collaborative scheduling strategy is as follows: (1) When wind power generation can meet the total electricity demand of production, it shall be given priority to production. Wind power is directly supplied to the electrolysis hydrogen production and methanol synthesis unit. Energy storage batteries are selected as the first storage source and hydrogen storage tanks are selected as the second storage source. If there is still electricity left, it shall be abandoned.
[0048] (2) When wind power generation can meet the electricity demand of the electrolysis hydrogen production unit but cannot meet the total electricity demand of production, energy storage batteries are selected as the first supplementary source and hydrogen storage tanks are selected as the second supplementary source. If there is still an electricity demand, electricity from the grid is purchased.
[0049] (3) When wind power generation cannot meet the power demand of the electrolysis hydrogen production device, energy storage battery is selected as the first supplementary source and hydrogen storage tank is selected as the second supplementary source. If there is still a power demand, the power grid power is purchased.
[0050] S5. Using the internal rate of return obtained in step S2 as the objective function, and the power of the electrolyzer and the scale of the blower as decision variables, construct an optimization model for the configuration of methanol production scale.
[0051] The objective function is expressed as: ; In the above formula, Represents the objective function value. Indicates the internal rate of return. Indicates the proportion of electricity purchased. This represents the value of the electricity purchase ratio constraint. This indicates the annual production of methanol. This represents the annual methanol production constraint value. Indicates the curtailment rate. This represents the constraint value for the power curtailment rate; , , These represent the weighting coefficients for the electricity purchase ratio, annual methanol production, and curtailment rate, respectively, with values of 10, 5, and 10.
[0052] The preferred value for the electricity purchase ratio constraint is 3%, the preferred value for the annual methanol production constraint is 152,000-168,000 tons, and the preferred value for the curtailment rate constraint is 15%.
[0053] S6. Set the constraints for the methanol production scale configuration optimization model constructed in step S5, including energy balance constraints, hydrogen storage tank state recursion constraints, and energy storage battery state constraints.
[0054] The energy balance constraint is expressed as: ; In the above formula, This represents the total electrical power consumed in methanol production at time t. This represents the direct wind power consumption at time t. This represents the discharge power of the energy storage battery at time t. This indicates the discharge efficiency of the energy storage battery. This represents the equivalent electrical power of hydrogen storage discharge at time t. This represents the power purchased by the power grid at time t. This represents the wind curtailment power at time t.
[0055] The recursive constraint condition for the state of the hydrogen storage tank is expressed as follows: ; In the above formula, This represents the mass of hydrogen in the hydrogen storage tank at time t. Indicates the time step. express Hydrogen mass flow rate at any given time, express The mass flow rate of hydrogen release at any given moment.
[0056] The state constraints of the energy storage battery are expressed as follows: ; In the above formula, This represents the state of charge of the battery at time t. express Battery charging power at all times This indicates the charging efficiency of the energy storage battery. express Battery discharge power at all times This indicates the discharge efficiency of the energy storage battery.
[0057] S7. Under the constraints of step S6, the objective function of step S5 is solved using the differential evolution algorithm to obtain the optimal wind turbine size and electrolyzer power under the optimal IRR. Specifically, this includes the following steps: S71. Initialize the population, specifically using the following formula: ; In the above formula, This represents the initial solution vector for the i-th individual; The lower limit of the decision variables is represented here as [600, 300], which means that the lower limit of the optimization search for wind turbine scale is 600MW and the upper limit of the optimization search for electrolyzer power is 300MW. The upper limit of the decision variables is represented here as [800, 600], which means that the upper limit of the optimization search for wind turbine scale is 800MW and the upper limit of the optimization search for electrolyzer power is 600MW. Indicates population size, with a typical value of 50; Represents a uniformly distributed random number.
[0058] S72. Perform the mutation operation, specifically using the following formula: ; In the above formula, Let represent the mutation vector of the i-th individual in the g-th generation. This represents a randomly selected baseline individual. This represents the scaling factor, which is 0.8. , This represents randomly selected differential individuals.
[0059] S73. Perform a crossover operation, specifically a binomial crossover, using the following formula: ; In the above formula, Let i represent the test vector of the i-th individual in the g-th generation. Let i represent the i-th individual in the g-th generation, and j represent the dimension index of the decision variable, j∈{1,2}; This represents the crossover probability, set to 0.9; This indicates a random dimension index.
[0060] S74. Perform the selection operation, specifically using the following formula: ; In the above formula, Indicates the first For the i-th individual, , Both represent the objective function value, g takes values from 1 to G, and G represents the maximum number of iterations, which is 200.
[0061] S8. Based on the wind turbine scale, electrolyzer power, and corresponding annual methanol production and electricity purchase obtained in step S7, introduce detailed financial evaluation parameters, conduct a full life cycle techno-economic assessment, and output key financial indicators.
[0062] The optimal configuration obtained according to step S7 is shown in Table 5.
[0063] Table 5 Optimal Configuration
[0064] In this example, the production period and construction period are set to 2 years and 20 years, respectively.
[0065] In this example, the discount rate, net residual value rate, and VAT rate are set to 9.5%, 4%, and 13%, respectively.
[0066] Specifically, the calculation method for cost and profit financial indicators can be adjusted according to project needs. In this embodiment, the key financial indicators output after the full life cycle techno-economic assessment are shown in Table 6.
[0067] Table 6 Financial Indicator Output
[0068] This invention utilizes a differential evolution algorithm for multivariate collaborative optimization to enhance wind power absorption capacity while ensuring stable and efficient methanol production, thereby achieving precise matching of energy conversion across different forms.
[0069] This invention takes IRR as its core objective, combines total cost and benefit analysis, optimizes equipment configuration, reduces construction costs, improves project financial internal rate of return and other indicators, thereby enhancing the project's investment value and profitability.
[0070] This invention effectively addresses constraints such as electricity purchase ratio and equipment scale, ensuring that the system operates within compliant and feasible boundaries, thereby improving the practicality of the strategy and the feasibility of the project.
[0071] This invention presents a wind power flexible hydrogen production and storage configuration strategy based on differential evolution algorithm. During the configuration process, wind power output and dynamic electricity price data of the target area are collected to construct an optimization model with the project's financial internal rate of return as the objective function. A methanol production graded control formula and a multi-energy flow collaborative scheduling strategy are adopted to complete the optimized configuration of wind power hydrogen production and storage, and the best overall performance result is achieved.
[0072] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.
Claims
1. A method for preparing flexible hydrogen production and storage solutions using wind power, characterized in that, Includes the following steps: S1. Collect the predicted wind power output sequence and corresponding dynamic electricity price data for the entire year in the target area, and construct a standardized sequence dataset; S2. Set system operating parameters, including basic parameters, methanol production parameters, and economic parameters; Calculate the internal rate of return based on the system operating parameters; S3. Establish a graded control strategy for methanol production. By calculating the total available energy and setting a benchmark energy, the actual total methanol production is obtained by graded control based on the total available energy and the benchmark energy. S4. Based on the standardized sequence dataset constructed in step S1, establish a multi-energy flow collaborative scheduling strategy; S5. Using the internal rate of return obtained in step S2 as the objective function, and the electrolyzer power and blower scale as decision variables, construct a methanol production scale configuration optimization model. S6. Set the constraints for the methanol production scale configuration optimization model constructed in step S5, including energy balance constraints, hydrogen storage tank state recursion constraints, and energy storage battery state constraints. S7. Under the constraints of step S6, the objective function of step S5 is solved using the differential evolution algorithm to obtain the wind turbine size and electrolyzer power under the optimal IRR. S8. Based on the wind turbine scale, electrolyzer power, and corresponding annual methanol production and electricity purchase obtained in step S7, introduce financial evaluation parameters, conduct a full life cycle techno-economic assessment, and output key financial indicators.
2. The method for preparing flexible hydrogen production and storage using wind power according to claim 1, characterized in that, The methanol production staged control strategy constructed in step S3 is as follows: (1) When the total available energy / Baseline Energy When the energy level is greater than or equal to the first threshold, there is an energy surplus and the actual total output is [not specified]. Specifically, it is calculated using the following formula: ； In the above formula, Indicates the baseline output; (2) When the second threshold is less than or equal to the total available energy / Baseline Energy When the first threshold is reached, the actual total output is... Specifically, it is calculated using the following formula: ； ； In the above formula, r represents the energy availability rate. Indicates to Round the result down to the nearest integer. (3) When the total available energy of the system / Baseline Energy Production ceases when the threshold is reached. The first threshold is greater than the second threshold.
3. The method for preparing flexible hydrogen production and storage using wind power according to claim 2, characterized in that, Total available energy of the system Specifically, it is calculated using the following formula: ； In the above formula, This represents the wind power output in the k-th time period in the future. This represents the amount of hydrogen stored at time t. This represents the battery charge at time t.
4. The method for preparing flexible hydrogen production and storage using wind power according to claim 1, characterized in that, The method for obtaining the internal rate of return (IRR) in step S2 is as follows: using basic parameters, methanol production parameters, and economic parameters, a simplified calculation is performed using a solution method combining Newton's iteration method and the bisection method. Once the algorithm converges, the optimal configuration is obtained. Then, an accurate IRR calculation is performed on the optimal configuration to obtain the internal rate of return.
5. The method for preparing flexible hydrogen production and storage using wind power according to claim 4, characterized in that, The objective function in step S5 is expressed as: ； In the above formula, Represents the objective function value. Indicates the internal rate of return. Indicates the proportion of electricity purchased. This represents the value of the electricity purchase ratio constraint. This indicates the annual production of methanol. This represents the annual methanol production constraint value. Indicates the curtailment rate. This represents the constraint value for the power curtailment rate; , , These represent the weighting coefficients corresponding to the electricity purchase ratio, annual methanol production, and power curtailment rate, respectively.
6. The method for preparing flexible hydrogen production and storage using wind power according to claim 5, characterized in that, The energy balance constraint in step S6 is expressed as follows: ； In the above formula, This represents the total electrical power consumed in methanol production at time t. This represents the direct wind power consumption at time t. This represents the discharge power of the energy storage battery at time t. This indicates the discharge efficiency of the energy storage battery. This represents the equivalent electrical power of hydrogen storage discharge at time t. This represents the power purchased by the power grid at time t. This represents the wind curtailment power at time t.
7. The method for preparing flexible hydrogen production and storage using wind power according to claim 6, characterized in that, The recursive constraint condition for the state of the hydrogen storage tank in step S6 is expressed as follows: ； In the above formula, This represents the mass of hydrogen in the hydrogen storage tank at time t. Indicates the time step. express Hydrogen mass flow rate at any given time, express The mass flow rate of hydrogen release at any given moment.
8. The method for preparing flexible hydrogen production and storage using wind power according to claim 7, characterized in that, The energy storage battery state constraints in step S6 are expressed as follows: ； In the above formula, This represents the state of charge of the battery at time t. express Battery charging power at all times. This indicates the charging efficiency of the energy storage battery. express Battery discharge power at all times This indicates the discharge efficiency of the energy storage battery.
9. A method for preparing flexible hydrogen production and storage using wind power according to claim 8, characterized in that, S7 includes the following steps: S71. Initialize the population, specifically using the following formula: ； In the above formula, This represents the initial solution vector for the i-th individual; This indicates the lower bound of the decision variable. Indicates the upper limit of the decision variable. Indicates population size, Represents a uniformly distributed random number; S72. Perform the mutation operation, specifically using the following formula: ； In the above formula, Let represent the mutation vector of the i-th individual in the g-th generation. This represents a randomly selected baseline individual. Indicates the scaling factor. , Represents randomly selected differential individuals; S73. Perform a crossover operation, specifically a binomial crossover, using the following formula: ； In the above formula, Let i represent the test vector of the i-th individual in the g-th generation. Let i represent the i-th individual in the g-th generation, and j represent the dimension index of the decision variable, j∈{1,2}; Indicates the crossover probability. Indicates a random dimension index; S74. Perform the selection operation, specifically using the following formula: ； In the above formula, Indicates the first For the i-th individual, , Both represent the objective function value, g takes values from 1 to G, and G represents the maximum number of iterations.
10. A method for preparing flexible hydrogen production and storage using wind power according to claim 1, characterized in that, The key financial indicators output by step S8 include investment indicators, cost-profit indicators, and financial analysis indicators.