A capacity matching method for off-grid wind-solar hydrogen production system

Abstract

The present disclosure relates to a capacity matching method for an off-grid wind-solar hydrogen production system, comprising the following steps: S1: Determine input parameters: Define the input parameters, including initial parameters, technical parameters, cost parameters, and financial parameters; S2: Construct a mathematical model; S3: In Python, invoke CBC optimization software via the Pulp toolkit to subsequently solve the model; S4: Initialize, model, solve, verify, and adjust the optimization objective function to obtain result data; the result data includes the optimal configuration plan, operation strategy, and economic benefit analysis; S5: Output the result data. In S3, the optimization model invokes CBC optimization software for solution, making it easier for users to learn and use.

Description

Technical Field

[0001] The present disclosure relates to the field of power generation, and in particular to a capacity matching method for an off-grid wind-solar hydrogen production system between wind-solar new energy power generation and water electrolysis hydrogen production loads. Background Art

[0002] Hydrogen production by water electrolysis is an efficient and clean hydrogen production technology with a simple process and high product purity, making it the most promising large-scale hydrogen production technology. With the growing use of renewable energy for power generation, hydrogen has become an ideal energy storage medium. Specifically, through water electrolysis, electricity generated by renewable energy is converted into hydrogen energy for storage and subsequent use. However, in off-grid conditions, the output of renewable energy power generation is inherently subject to random fluctuations, with large fluctuations, which inevitably lead to fluctuations in hydrogen production in downstream hydrogen production systems. Optimizing the output matching between renewable energy power generation and hydrogen production equipment has become a major challenge.

[0003] Simply allocating hydrogen production capacity to renewable energy generation capacity will maximize renewable energy utilization, but it will lead to idle hydrogen production equipment and waste. Conversely, if hydrogen production capacity is too small relative to renewable energy generation capacity, it will result in a waste of installed power generation capacity. Furthermore, hydrogen storage, as the link between production and users, has a significant impact on upstream and downstream configurations. Excessive storage capacity increases costs and space requirements; too little storage capacity results in significant waste of both power generation and hydrogen production capacity. The size and matching of hydrogen production capacity, renewable energy generation capacity, and hydrogen storage capacity significantly impacts hydrogen production costs.

[0004] Current renewable energy hydrogen (ammonia) production faces challenges such as irrational planning for power generation, energy storage, hydrogen production, storage, and ammonia production, as well as excessively high production costs. The primary reason for these issues is a lack of system process modeling, which fails to objectively and reasonably describe the actual system conditions. New energy hydrogen production systems encompass power generation, energy storage, hydrogen production, and storage. However, limited research has focused on the overall system, focusing primarily on the technical aspects of each submodule. This is because the planning and operation of new energy hydrogen production systems involve multiple stakeholders, including power companies, wind turbine manufacturers, photovoltaic module manufacturers, hydrogen producers (chemical plants), and product consumers. These stakeholders may have varying constraints and objectives, requiring coordination to ensure that the planning meets all requirements and achieves system profitability. Furthermore, system planning is a complex optimization problem, and finding the optimal planning solution impacts system profitability. Planning solutions have both advantages and limitations. Existing technologies typically use the levelized cost of energy (LCE) approach to economic capacity allocation for new energy hydrogen production systems. This approach calculates the optimal capacity of new energy hydrogen production systems from a cost perspective, but fails to consider revenue fluctuations caused by hydrogen prices and the economic configuration required for a stable hydrogen supply. New energy hydrogen production systems have a long life cycle, including construction and operation periods, but most existing technologies ignore the impact of time factors on system economics. Summary of the Invention

[0005] In response to the above technical problems in the related art, the present disclosure proposes a method for optimizing the configuration of the capacity of an off-grid wind-solar coupled hydrogen production system, which can overcome the above-mentioned shortcomings of the existing technology.

[0006] To achieve the above technical objectives, the technical solution of the present disclosure is implemented as follows:

[0007] The present disclosure provides a method for optimizing the configuration of the capacity of an off-grid wind-solar coupled hydrogen production system, comprising the following steps:

[0008] S1 Determine input parameters: define various input parameters, including initial parameters, technical parameters, cost parameters, and financial parameters;

[0009] Initial parameters include installed capacity, annual power generation output curve, etc.

[0010] Technical parameters include system capacity (output), conversion efficiency, etc.;

[0011] Cost parameters include initial investment cost, operation and maintenance cost, etc.

[0012] Financial parameters include taxes, capital costs, etc.;

[0013] S2 constructs a mathematical model: model each subsystem separately, constructs a mathematical model of the off-grid wind-solar coupled hydrogen production system, and connects the subsystems with the flow of electricity and hydrogen. Each subsystem includes a new energy power generation system, a hydrogen production system, an energy storage system, and a hydrogen storage system; wherein the new energy power generation system can directly supply power to the hydrogen production system, or store excess electricity in the energy storage system when the new energy power is sufficient; after the energy storage system is full, the excess new energy power is treated as abandoned power; the hydrogen produced by the hydrogen production system can be directly output, or stored in the hydrogen storage system when the hydrogen is sufficient, and then output by the hydrogen storage system; in order to achieve the purpose of stable hydrogen production, it is stipulated that the output of hydrogen is the same within a certain time interval;

[0014] S3: In Python, the optimization software CBC is called through the Pulp toolkit to solve the subsequent model;

[0015] S4: Initializing, modeling, solving, verifying, and adjusting the optimization objective function to obtain result data; the result data includes the optimal configuration plan, operation strategy, and economic benefit analysis;

[0016] S5: Output the result data.

[0017] Preferably, in said S2, in order to distinguish between the long-term planning of the system and the daily operation of the system, the system can be divided into a planning layer model, an operation layer model and a financial model; wherein, the planning layer model focuses on the design and long-term strategic planning of the system, including resource assessment, equipment selection and configuration, with an emphasis on the design and determination of the scale and capacity of the new energy power generation system, energy storage system, hydrogen production system and hydrogen storage system; the planning layer takes into account the internal characteristics, economic benefits, safety benefits and overall sustainability of each system; the operation layer model focuses on daily operation and maintenance, including energy management and real-time scheduling. Optimization is performed in each operating node of the system, including determining power generation, charge and discharge management of the energy storage system, load demand response, etc.; the financial model focuses on calculating the economic benefits of the system over its entire life cycle after considering economic factors such as the initial investment cost of the project, operation and maintenance costs, taxes, and capital costs, and provides indicators such as net present value, internal rate of return, and levelized ton cost to provide support for system decision-making.

[0018] Preferably, in S2, constructing the mathematical model can be implemented using existing technologies, or the specific steps can be:

[0019] S2.1: Identify the subsystems of the off-grid wind-solar coupled hydrogen production system and define optimization decision variables for each subsystem. The optimization decision variables include the system capacity of each subsystem and the operating volume of each subsystem at each time point. The subsystems include a new energy power generation system, a hydrogen production system, a hydrogen storage system, and an energy storage system.

[0020] S2.2: Define optimization constraint functions, which include: subsystem operation constraint function, hydrogen output stability constraint function, overall system relationship constraint function, and financial accounting constraint function; among them,

[0021] The subsystem operation constraint function includes the operation restriction condition function of each subsystem;

[0022] The hydrogen output stability constraint function is a conditional function that ensures that the off-grid wind-solar coupled hydrogen production system can stably produce hydrogen in an off-grid state;

[0023] The total system relationship constraint function is the mutual relationship function between each subsystem;

[0024] Financial accounting constraint functions include calculation functions for capital costs and taxes;

[0025] S2.3: Based on the optimization decision variables in S2.1 and the optimization constraints in S2.2, construct the optimization objective function to ensure that the net present value (NPV) is maximized throughout the system life cycle.

[0026] Preferably, in S2, the total system relationship constraint function is expressed by the following formula:

[0027]

[0028] In the above formula: t: represents the specific time;

[0029] Power of renewable energy power generation system (MW);

[0030] Abandoned power (MW);

[0031] Energy storage system power (MW);

[0032] Hydrogen production system power (MW);

[0033] The above formula 1 is the mutual relationship function between the subsystems, that is, the overall system relationship constraint function.

[0034] Preferably, in S2, the constraint function of the system variables and the operating amount of the hydrogen production system is expressed by the following formula:

[0035]

[0036] In the above formula: t: represents the specific time;

[0037] The power consumption of the electrolyzer at time t (MW);

[0038] Upper and lower limits of electrolytic cell load power ratio;

[0039] Energy conversion coefficient of electrolyzer (kWh / Nm 3 );

[0040] The hydrogen flow rate (Nm) entering the hydrogen storage tank at time t 3 / h);

[0041] The hydrogen flow rate (Nm) directly supplied to the downstream hydrogen system at time t 3 / h);

[0042] Rated power of the smallest operating unit of the electrolyzer (MW);

[0043] N AE : The number of electrolytic cells.

[0044] Preferably, in S2, the constraint function of the system variables and the operating amount of the energy storage system is expressed by the following formula:

[0045]

[0046] In the above formula: t: represents the specific time;

[0047] ES (t) : The remaining energy of the energy storage system at time t (MWh);

[0048] The charging and discharging power of the energy storage system at time t (MW);

[0049] η ES 、 The upper and lower limits of the charge and discharge power ratio of the energy storage system;

[0050] ΔT: time step (hours);

[0051] C ES : Rated power of energy storage system (MW);

[0052] ES min , ES max : Upper and lower limits of energy storage system power.

[0053] Preferably, in S2, the constraint function of the system variables and the operating amount of the hydrogen storage system is expressed by the following formula:

[0054]

[0055] In the above formula: t: represents the specific time;

[0056] η sto,0 : hydrogen inventory ratio in the buffer tank in the initial state;

[0057] η ES 、 The upper and lower limits of hydrogen inventory ratio in hydrogen storage tanks;

[0058] Hydrogen inventory in the buffer tank (Nm 3 );

[0059] The hydrogen production rate of the hydrogen production system (Nm 3 / h);

[0060] Hydrogen rate directly from the hydrogen production system to the downstream (Nm 3 / h);

[0061] Outflow rate related to downstream hydrogen consumption (Nm 3 / h);

[0062] Downstream target hydrogen consumption (Nm 3 / h).

[0063] Preferably, in S2, the hydrogen release stability constraint function is expressed by the following formula:

[0064]

[0065] In the above formula: t: represents the specific time;

[0066] Real-time hydrogen consumption at downstream at time t (Nm 3 );

[0067] The hydrogen flow rate (Nm) directly supplied to the downstream hydrogen system at time t 3 / h);

[0068] The amount of hydrogen flowing downstream from the hydrogen storage unit at time t (Nm 3 / h).

[0069] Preferably, in S2, the financial accounting constraint function is expressed by the following formula:

[0070]

[0071]

[0072] In the above formula: t: represents the specific time;

[0073] x: set of decision variables;

[0074] Construction cost cash flow in year y (10,000 yuan);

[0075] Operating cost cash flow in year y (10,000 yuan);

[0076] Time collection during the construction period;

[0077] Time collection of the operation period;

[0078] r: discount rate;

[0079] Y1: end year of the construction period;

[0080] Initial investment (10,000 yuan);

[0081] Equipment costs in the initial investment (10,000 yuan);

[0082] Construction and installation engineering in the initial investment (10,000 yuan);

[0083] Unit cost of energy storage system (10,000 yuan / MWh);

[0084] Unit cost of hydrogen production system (10,000 yuan / MW);

[0085] Unit cost of hydrogen storage system (10,000 yuan / Nm 3 );

[0086] Unit cost of new energy power generation system (10,000 yuan / MW);

[0087] Income withtax : Income including tax (10,000 yuan);

[0088] Fixed assets recovered during the yth year of operation (10,000 yuan);

[0089] Total operating costs in year y (10,000 yuan);

[0090] Subsequent investment in replacing the electrolytic cell (10,000 yuan);

[0091] Value-added tax in year y (10,000 yuan);

[0092] CIT y : Corporate income tax in year y (10,000 yuan);

[0093] BT y : Business tax in the yth year (10,000 yuan).

[0094] Preferably, in S3, the CBC optimization software is called in Python through the toolkit Pulp.

[0095] Preferably, the S4 specifically includes the following steps:

[0096] S4.1 Initialization: Set input parameters and initial conditions, define objective function and constraints;

[0097] S4.2 Modeling: Transform practical problems into mathematical models, clarifying the objective function, decision variables, and constraints;

[0098] S4.3 Solving: Solve the problem using a mathematical programming solver to obtain the optimal solution;

[0099] S4.4 Verification and adjustment: Verify the feasibility and rationality of the solution, adjust the model parameters or constraints if necessary, and solve again.

[0100] Preferably, in S4 and S5, the optimal configuration plan includes the capacity configuration of each subsystem, the operation strategy includes the optimal operation plan of each subsystem at different time points, and the economic benefit analysis is an economic indicator and includes the calculated net present value (NPV) and internal rate of return (IRR).

[0101] Beneficial effects of the present disclosure: Compared with the existing technology, the present disclosure has the following advantages: (A) Efficiency improvement: By optimizing the configuration and operation of the hydrogen production system, the design and operation efficiency of the hydrogen production system is significantly improved; (B) Stability enhancement: The model includes constraints on hydrogen output stability conditions, ensuring stable hydrogen output under different conditions; (C) Model solution advantage: The optimization software CBC is called through the toolkit Pulp in Python to solve the subsequent model, ensuring the efficiency and accuracy of the solution process.

[0102] The present disclosure solves the problem of providing the most economical (highest net present value) hydrogen production, energy storage and hydrogen storage configuration scheme in existing new energy hydrogen production projects by combining wind and solar power output curves. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0104] Figure 1 This is a logic flow chart of the capacity matching method of the off-grid wind-solar hydrogen production system described in the present disclosure. DETAILED DESCRIPTION

[0105] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present disclosure.

[0106] like Figure 1 As shown, in order to facilitate understanding of the above technical solutions of the present disclosure, the above technical solutions of the present disclosure are described in detail below through specific usage methods.

[0107] The present disclosure provides a capacity matching method for an off-grid wind-solar hydrogen production system, comprising the following steps:

[0108] S1 Determine input parameters: define various input parameters, including initial parameters, technical parameters, cost parameters, and financial parameters;

[0109] Initial parameters include installed capacity, annual power generation output curve, etc.

[0110] Technical parameters include system capacity (output), conversion efficiency, etc.;

[0111] Cost parameters include initial investment cost, operation and maintenance cost, etc.

[0112] Financial parameters include taxes, capital costs, etc.;

[0113] S2 constructs a mathematical model: model each subsystem separately, constructs a mathematical model of the off-grid wind-solar coupled hydrogen production system, and connects the subsystems with the flow of electricity and hydrogen. Each subsystem includes a new energy power generation system, a hydrogen production system, an energy storage system, and a hydrogen storage system; wherein the new energy power generation system can directly supply power to the hydrogen production system, or store excess electricity in the energy storage system when the new energy power is sufficient; after the energy storage system is full, the excess new energy power is treated as abandoned power; the hydrogen produced by the hydrogen production system can be directly output, or stored in the hydrogen storage system when the hydrogen is sufficient, and then output by the hydrogen storage system; in order to achieve the purpose of stable hydrogen production, it is stipulated that the output of hydrogen is the same within a certain time interval;

[0114] S3: In Python, the optimization software CBC is called through the Pulp toolkit to solve the subsequent model;

[0115] S4: Initializing, modeling, solving, verifying, and adjusting the optimization objective function to obtain result data; the result data includes the optimal configuration plan, operation strategy, and economic benefit analysis;

[0116] S5: Output the result data.

[0117] In one embodiment, in S2, in order to distinguish between the long-term planning of the system and the daily operation of the system, the system can also be divided into a planning layer model, an operation layer model and a financial model; wherein, the planning layer model focuses on the design and long-term strategic planning of the system, including resource assessment, equipment selection and configuration, with an emphasis on the design and determination of the scale and capacity of the new energy power generation system, energy storage system, hydrogen production system and hydrogen storage system; the planning layer takes into account the internal characteristics of each system, economic benefits, safety benefits and the overall sustainability of the system; the operation layer model focuses on daily operation and maintenance, including energy management and real-time scheduling. Optimization is performed in each operating node of the system, including determining power generation, charge and discharge management of the energy storage system, load demand response, etc.; the financial model focuses on calculating the economic benefits of the system over its entire life cycle after considering economic factors such as the initial investment cost of the project, operation and maintenance costs, taxes, and capital costs, and provides indicators such as net present value, internal rate of return, and levelized ton cost to provide support for system decision-making. Existing technologies can be used here to achieve this.

[0118] Preferably, in S2, constructing the mathematical model can be implemented using existing technologies, or the specific steps can be:

[0119] S2.1: Identify the subsystems of the off-grid wind-solar coupled hydrogen production system and define optimization decision variables for each subsystem. The optimization decision variables include the system capacity of each subsystem and the operating volume of each subsystem at each time point. The subsystems include a new energy power generation system, a hydrogen production system, a hydrogen storage system, and an energy storage system.

[0120] S2.2: Define optimization constraint functions, which include: subsystem operation constraint function, hydrogen output stability constraint function, overall system relationship constraint function, and financial accounting constraint function; among them,

[0121] The subsystem operation constraint function includes the operation restriction condition function of each subsystem;

[0122] The hydrogen output stability constraint function is a conditional function that ensures that the off-grid wind-solar coupled hydrogen production system can stably produce hydrogen in an off-grid state;

[0123] The total system relationship constraint function is the mutual relationship function between each subsystem;

[0124] Financial accounting constraint functions include calculation functions for capital costs and taxes;

[0125] S2.3: Based on the optimization decision variables in S2.1 and the optimization constraints in S2.2, construct the optimization objective function to ensure that the net present value (NPV) is maximized throughout the system life cycle.

[0126] In one embodiment, in S2, the total system relationship constraint function is expressed by the following formula:

[0127]

[0128] In the above formula: t: represents the specific time;

[0129] Power of renewable energy power generation system (MW);

[0130] Abandoned power (MW);

[0131] Energy storage system power (MW);

[0132] Hydrogen production system power (MW);

[0133] The above formula 1 is the mutual relationship function between the subsystems, that is, the overall system relationship constraint function.

[0134] In one embodiment, in S2, the constraint function of the system variables and the operating amount of the hydrogen production system is expressed by the following formula:

[0135]

[0136] In the above formula: t: represents the specific time;

[0137] The power consumption of the electrolyzer at time t (MW);

[0138] Upper and lower limits of electrolytic cell load power ratio;

[0139] Energy conversion coefficient of electrolyzer (kWh / Nm 3 );

[0140] The hydrogen flow rate (Nm) entering the hydrogen storage tank at time t 3 / h);

[0141] The hydrogen flow rate (Nm) directly supplied to the downstream hydrogen system at time t 3 / h);

[0142] Rated power of the smallest operating unit of the electrolyzer (MW);

[0143] N AE : The number of electrolytic cells.

[0144] In one embodiment, in S2, the constraint function of the system variables and the operating amount of the energy storage system is expressed by the following formula:

[0145]

[0146] In the above formula: t: represents the specific time;

[0147] ES (t) : The remaining energy of the energy storage system at time t (MWh);

[0148] The charging and discharging power of the energy storage system at time t (MW);

[0149] η ES 、 The upper and lower limits of the charge and discharge power ratio of the energy storage system;

[0150] ΔT: time step (hours);

[0151] C ES : Rated power of energy storage system (MW);

[0152] ES min , ES max : Upper and lower limits of energy storage system power.

[0153] In one embodiment, in S2, the constraint function of the system variables and the operating amount of the hydrogen storage system is expressed by the following formula:

[0154]

[0155] In the above formula: t: represents the specific time;

[0156] η sto,0 : hydrogen inventory ratio in the buffer tank in the initial state;

[0157] η HS 、 The upper and lower limits of hydrogen inventory ratio in hydrogen storage tanks;

[0158] Hydrogen inventory in the buffer tank (Nm 3 );

[0159] The hydrogen production rate of the hydrogen production system (Nm 3 / h);

[0160] Hydrogen rate directly from the hydrogen production system to the downstream (Nm 3 / h);

[0161] Outflow rate related to downstream hydrogen consumption (Nm 3 / h);

[0162] Downstream target hydrogen consumption (Nm 3 / h).

[0163] In one embodiment, in S2, the hydrogen release stability constraint function is expressed by the following formula:

[0164]

[0165] In the above formula: t: represents the specific time;

[0166] Real-time hydrogen consumption at downstream at time t;

[0167] The hydrogen flow rate (Nm) directly supplied to the downstream hydrogen system at time t 3 / h);

[0168] The amount of hydrogen flowing downstream from the hydrogen storage unit at time t (Nm 3 / h).

[0169] In one embodiment, in S2, the financial accounting constraint function is expressed by the following formula:

[0170] In the above formula: t: represents the specific time;

[0171] x: set of decision variables;

[0172] Construction cost cash flow in year y (10,000 yuan);

[0173] Operating cost cash flow in year y (10,000 yuan);

[0174] Time collection during the construction period;

[0175] Time collection of the operation period;

[0176] r: discount rate;

[0177] Y1: end year of the construction period;

[0178] Initial investment (10,000 yuan);

[0179] Equipment costs in the initial investment (10,000 yuan);

[0180] Construction and installation engineering in the initial investment (10,000 yuan);

[0181] Unit cost of energy storage system (10,000 yuan / MWh);

[0182] Unit cost of hydrogen production system (10,000 yuan / MW);

[0183] Unit cost of hydrogen storage system (10,000 yuan / Nm 3 );

[0184] Unit cost of new energy power generation system (10,000 yuan / MW);

[0185] Income withtax : Income including tax (10,000 yuan);

[0186] Fixed assets recovered during the yth year of operation (10,000 yuan);

[0187] Total operating costs in year y (10,000 yuan);

[0188] Subsequent investment in replacing the electrolytic cell (10,000 yuan);

[0189] Value-added tax in year y (10,000 yuan);

[0190] CIT y : Corporate income tax in year y (10,000 yuan);

[0191] BT y : Business tax in the yth year (10,000 yuan).

[0192] In one embodiment, in S3, the CBC optimization software is called in Python through the toolkit Pulp.

[0193] In one embodiment, the S4 specifically includes the following steps:

[0194] S4.1 Initialization: Set input parameters and initial conditions, define objective function and constraints;

[0195] S4.2 Modeling: Transform practical problems into mathematical models, clarifying the objective function, decision variables, and constraints;

[0196] S4.3 Solving: Solve the problem using a mathematical programming solver to obtain the optimal solution;

[0197] S4.4 Verification and adjustment: Verify the feasibility and rationality of the solution, adjust the model parameters or constraints if necessary, and solve again.

[0198] In a certain embodiment, in S4 and S5, the optimal configuration plan includes the capacity configuration of each subsystem, the operation strategy includes the optimal operation plan of each subsystem at different time points, and the economic benefit analysis is an economic indicator and includes the calculated net present value (NPV) and internal rate of return (IRR).

[0199] In summary, through the unique design of the present disclosure, compared with the existing technology, the present disclosure has the following advantages: (A) Efficiency improvement: by optimizing the configuration and operation of the hydrogen production system, the design and operation efficiency of the hydrogen production system is significantly improved; (B) Stability enhancement: the model includes constraints on hydrogen output stability conditions, ensuring stable hydrogen output under different conditions; (C) Model solving advantage: in Python, the optimization software CBC is called through the toolkit Pulp to solve the subsequent model, ensuring the efficiency and accuracy of the solution process. The present disclosure solves the problem of combining the wind and solar output curves in existing new energy hydrogen production projects to provide the most economical (highest net present value) hydrogen production, energy storage, and hydrogen storage configuration plan.

[0200] The above are only preferred embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A capacity matching method for an off-grid wind-solar hydrogen production system, characterized in that: The following steps are involved: S1 Determine input parameters: define various input parameters, including initial parameters, technical parameters, cost parameters, and financial parameters; Initial parameters include installed capacity and annual power generation output curve; Technical parameters include system capacity and conversion efficiency; Cost parameters include initial investment cost, operation and maintenance cost; Financial parameters include taxes, costs of capital; S2 constructs a mathematical model: model each subsystem separately, constructs a mathematical model of the off-grid wind-solar coupled hydrogen production system, and connects the subsystems with the flow of electricity and hydrogen. Each subsystem includes a new energy power generation system, a hydrogen production system, a hydrogen storage system, and an energy storage system; wherein, the new energy power generation system can directly supply power to the hydrogen production system, or it can store excess electricity in the energy storage system when the new energy power is sufficient; after the energy storage system is full, the excess new energy power is treated as abandoned power; the hydrogen produced by the hydrogen production system can be directly output, or it can be stored in the hydrogen storage system when the hydrogen is sufficient, and then output by the hydrogen storage system; in order to achieve the purpose of stable hydrogen production, it is stipulated that the output of hydrogen is the same within a certain time interval; in S2, the system is divided into a planning layer model, an operation layer model and a financial model; wherein, the planning layer model includes resource evaluation, equipment selection and configuration; the operation layer model includes energy management and real-time scheduling; In S2, the specific steps of constructing the mathematical model are: S2.1: Identify the subsystems of the off-grid wind-solar coupled hydrogen production system and define optimization decision variables for each subsystem. The optimization decision variables include the system capacity of each subsystem and the operating volume of each subsystem at each time point. The subsystems include a new energy power generation system, a hydrogen production system, a hydrogen storage system, and an energy storage system. S2.2: Define optimization constraint functions, which include: subsystem operation constraint functions, hydrogen output stability constraint functions, overall system relationship constraint functions, and financial accounting constraint functions; among which, the subsystem operation constraint functions include the operation restriction condition functions of each subsystem; The hydrogen output stability constraint function is a conditional function that ensures that the off-grid wind-solar coupled hydrogen production system can stably produce hydrogen in an off-grid state; The total system relationship constraint function is the mutual relationship function between each subsystem; Financial accounting constraint functions include calculation functions for capital costs and taxes; S2.3: Based on the optimization decision variables in S2.1 and the optimization constraints in S2.2, we construct the optimization objective function to ensure that the net present value (NPV) is maximized throughout the system life cycle. S3: In Python, the optimization software CBC is called through the Pulp toolkit to solve the subsequent model; S4: Initialize, model, solve, verify, and adjust the optimization objective function to obtain result data; the result data includes the optimal configuration plan, operation strategy, and economic benefit analysis; S5: Output the result data.

2. The capacity matching method according to claim 1, characterized in that: In S2, the overall system relationship constraint function is expressed by the following formula: In the above formula: t: represents the specific time; Power of renewable energy power generation system, unit—MW; Abandoned power, unit: MW; Energy storage system power, unit—MW; Hydrogen production system power, unit—MW; The above formula 1 is the mutual relationship function between the subsystems, that is, the overall system relationship constraint function.

3. The capacity matching method according to claim 1, characterized in that: In S2, the constraint function of the system variables and the operating amount of the hydrogen production system is expressed by the following formula: In the above formula: t: represents the specific time; The power consumption of the electrolyzer at time t, in MW; Upper and lower limits of electrolytic cell load power ratio; Energy conversion coefficient of electrolyzer, unit—kWh / Nm 3 ; The hydrogen flow rate entering the hydrogen storage tank at time t, unit - Nm 3 / h; The hydrogen flow rate directly supplied to the downstream hydrogen system at time t, unit - Nm 3 / h; The rated power of the smallest operating unit of the electrolyzer, in MW; N AE : The number of electrolytic cells.

4. The capacity matching method according to claim 1, characterized in that: In S2, the constraint function of the system variables and the operating amount of the energy storage system is expressed by the following formula: In the above formula: t: represents the specific time; ES (t) : The remaining energy of the energy storage system at time t, unit - MWh; The charging and discharging power of the energy storage system at time t, in MW; η E S 、 The upper and lower limits of the charge and discharge power ratio of the energy storage system; ΔT: time step, unit—hour; C ES : Rated power of the energy storage system, unit—MW; ES min , ES max : Upper and lower limits of energy storage system power.

5. The capacity matching method according to claim 1, characterized in that: In S2, the constraint function of the system variables and the operating amount of the hydrogen storage system is expressed by the following formula: In the above formula: t: represents the specific time; η sto,0 : hydrogen inventory ratio in the buffer tank in the initial state; η H S 、 The upper and lower limits of hydrogen inventory ratio in hydrogen storage tanks; Hydrogen inventory in the buffer tank, unit—Nm 3 ; The hydrogen production rate of the hydrogen production system, unit - Nm 3 / h; The hydrogen rate directly from the hydrogen production system to the downstream, unit - Nm 3 / h; The outflow rate related to the downstream hydrogen consumption, unit - Nm 3 / h; Downstream target hydrogen consumption, unit—Nm 3 / h.

6. The capacity matching method according to claim 1, characterized in that: In S2, the hydrogen release stability constraint function is expressed by the following formula: In the above formula: t: represents the specific time; Real-time hydrogen consumption of downstream at time t, unit—Nm 3 ; The hydrogen flow rate directly supplied to the downstream hydrogen system at time t, unit - Nm 3 / h; The amount of hydrogen flowing downstream from the hydrogen storage unit at time t, unit - Nm 3 / h.

7. The capacity matching method according to claim 1, characterized in that: In S2, the financial accounting constraint function is expressed by the following formula: Income withtax =J AEHS,withtax (Formula 16); In the above formula: t: represents the specific time; x: set of decision variables; Construction cost cash flow in year y, in ten thousand yuan; Operating cost cash flow in year y, unit—10,000 yuan; Time collection during the construction period; Time collection of the operation period; r: discount rate; Y1: end year of the construction period; Initial investment, unit—10,000 yuan; Equipment costs in the initial investment, in ten thousand yuan; Construction and installation projects in the initial investment, unit—ten thousand yuan; Unit cost of energy storage system, unit—10,000 yuan / MWh; Unit cost of hydrogen production system, unit—10,000 yuan / MW; Unit cost of hydrogen storage system, unit—10,000 yuan / Nm 3 ; Unit cost of new energy power generation system, unit—10,000 yuan / MW; Income withtax :Income including tax, unit—ten thousand yuan; Fixed assets recovered during the yth year of operation, in ten thousand yuan; Total operating costs in year y, in ten thousand yuan; Subsequent investment in replacing the electrolytic cell, in ten thousand yuan; Value-added tax in year y, unit: 10,000 yuan; CIT y : Corporate income tax for the yth year, unit—ten thousand yuan; BT y : Business tax for the yth year, unit—ten thousand yuan.

8. The capacity matching method according to claim 1, characterized in that: The S4 specifically includes the following steps: S4.1 Initialization: Set input parameters and initial conditions, define objective function and constraints; S4.2 Modeling: Transform practical problems into mathematical models, clarifying the objective function, decision variables, and constraints; S4.3 Solving: Solve the problem using a mathematical programming solver to obtain the optimal solution; S4.4 Verification and adjustment: Verify the feasibility and rationality of the solution, adjust the model parameters or constraints if necessary, and solve again.

9. The capacity matching method according to claim 8, characterized in that: In S4 and S5, the optimal configuration plan includes the capacity configuration of each subsystem, the operation strategy includes the optimal operation plan of each subsystem at different time points, and the economic benefit analysis is an economic indicator and includes the calculated net present value NPV and internal rate of return IRR.

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