Capacity matching method of off-grid wind-solar hydrogen production system

Abstract

The invention relates to a capacity matching method of an off-grid type wind and light hydrogen production system, which comprises the following steps: S1, determining input parameters: defining the input parameters including an initial parameter, a technical parameter, a cost parameter and a financial parameter; s2, constructing a mathematical model; s3, calling CBC optimization software in the Python through a toolkit Pulp to carry out subsequent solution of the model; s4, performing initialization, modeling, solving, verification and adjustment on the optimization target function to obtain result data; the result data comprises an optimal configuration scheme, an operation strategy and economic benefit analysis; and S5, outputting the result data. In the step S3, the optimization model calls CBC optimization software for solving, so that a user can learn and use the CBC optimization software conveniently.

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. It is the most promising large-scale hydrogen production technology. With the increasing power generation from renewable energy, hydrogen has become an ideal carrier for storing electrical energy. Specifically, through the technology of hydrogen production by water electrolysis, the electrical energy generated by renewable energy is converted into hydrogen energy for storage and subsequent use. However, under off-grid conditions, the output of renewable energy power generation is inherently random and has a large fluctuation range, which will inevitably lead to fluctuations in the amount of hydrogen produced by the downstream hydrogen production system. How to achieve optimal matching of the output of renewable energy power generation and hydrogen production equipment has become a major problem.

[0003] If the hydrogen production capacity is simply configured to be equal to the new energy power generation capacity, although the new energy can be used to the maximum extent, it will lead to the idleness and waste of hydrogen production equipment. On the contrary, if the hydrogen production capacity is too small relative to the new energy power generation capacity, it will lead to the waste of power generation installed capacity. At the same time, as a link between production and users, the capacity of hydrogen storage has an important impact on the configuration of upstream and downstream. Too much storage will increase costs and land; too little storage will cause serious waste of power generation capacity and hydrogen production capacity. The size and matching relationship of hydrogen production capacity, new energy power generation capacity, and hydrogen storage capacity have a significant impact on the cost of hydrogen production.

[0004] At present, renewable energy hydrogen (ammonia) production faces problems such as unreasonable planning of power generation, energy storage, hydrogen production, hydrogen storage, and ammonia production, and excessive production costs. The main reason for the problem is the lack of modeling of the system process, which cannot objectively and reasonably describe the actual system situation. The new energy hydrogen production system includes several modules: power generation, energy storage, hydrogen production, and hydrogen storage. There is less research on the overall system, mainly on the technical research of each sub-module. This is because the planning and operation of the new energy hydrogen production system involves multiple stakeholders, including power companies, wind turbine factories, photovoltaic module factories, hydrogen producers (chemical plants), product consumers, etc. These stakeholders may have different constraints and goals, and need to coordinate with each other to make the planning meet all needs and achieve system profitability. In addition, system planning is a complex optimization problem. Whether the optimal planning scheme can be found will affect the system benefits. The planning solution method has certain advantages and limitations. In terms of system performance evaluation, the existing technology usually uses the levelized energy cost method for the economic capacity configuration of the new energy hydrogen production system, and calculates the optimal capacity of the new energy hydrogen production system from a cost perspective, but does not consider the changes in benefits caused by hydrogen prices and the economic configuration required for 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 deficiencies in the prior art.

[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; among them,

[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 each subsystem 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 toolkit Pulp 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 S2, in order to distinguish the long-term planning of the system from 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 new energy power generation systems, energy storage systems, hydrogen production systems and hydrogen storage systems; 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 energy storage systems, 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 may be implemented using existing technologies, or the specific steps may 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, including the system capacity of each subsystem and the operation volume of each subsystem at each time point; wherein each subsystem includes 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 to ensure 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 of S2.1 and the optimization constraints of S2.2, the optimization objective function is constructed 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 used by the electrolyzer at time t (MW);

[0038] The upper and lower limits of the 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 charging and discharging 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 : The 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 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: a set of decision variables;

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

[0075] Cash flow from operating costs in year y (10,000 yuan);

[0076] The time set for the construction period;

[0077] The time set of the operation period;

[0078] r: discount rate;

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

[0080] Initial investment (ten thousand yuan);

[0081] Equipment costs in the initial investment (ten thousand yuan);

[0082] Construction and installation works in the initial investment (ten thousand 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 (ten thousand yuan);

[0088] Fixed assets recovered in the yth year of operation (ten thousand yuan);

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

[0090] Subsequent investment in replacing the electrolytic cell (ten thousand yuan);

[0091] Value-added tax in year y (ten thousand yuan);

[0092] CIT y : Corporate income tax in the yth year (ten thousand 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: Convert practical problems into mathematical models and clarify objective functions, decision variables and constraints;

[0098] S4.3 Solution: Solve by 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 prior art, 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 to ensure 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.

[0102] The present invention solves the problem of combining wind and solar power 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 scheme. 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying any creative work.

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

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

[0106] like Figure 1 As shown, in order to facilitate the 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; among them,

[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 each subsystem 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 toolkit Pulp 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 a certain embodiment, in S2, in order to distinguish the long-term planning of the system from 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 new energy power generation systems, energy storage systems, hydrogen production systems, and hydrogen storage systems; 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.

[0118] Preferably, in S2, constructing the mathematical model may be implemented using existing technologies, or the specific steps may 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, including the system capacity of each subsystem and the operation volume of each subsystem at each time point; wherein each subsystem includes 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 to ensure 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 of S2.1 and the optimization constraints of S2.2, the optimization objective function is constructed 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 used by the electrolyzer at time t (MW);

[0138] The upper and lower limits of the 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 charging and discharging 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 : The 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 output 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: a set of decision variables;

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

[0173] Cash flow from operating costs in year y (10,000 yuan);

[0174] The time set for the construction period;

[0175] The time set of the operation period;

[0176] r: discount rate;

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

[0178] Initial investment (ten thousand yuan);

[0179] Equipment costs in the initial investment (ten thousand yuan);

[0180] Construction and installation works in the initial investment (ten thousand 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 (ten thousand yuan);

[0186] Fixed assets recovered in the yth year of operation (ten thousand yuan);

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

[0188] Subsequent investment in replacing the electrolytic cell (ten thousand yuan);

[0189] Value-added tax in year y (ten thousand yuan);

[0190] CIT y : Corporate income tax in the yth year (ten thousand 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: Convert practical problems into mathematical models and clarify objective functions, decision variables and constraints;

[0196] S4.3 Solution: Solve by 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 prior art, 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 solutions.

[0200] The above are only preferred embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope 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; among them, The initial parameters include installed capacity and annual power output curve; Technical parameters include system capacity and conversion efficiency; Cost parameters include initial investment cost, operation and maintenance cost; Financial parameters include taxes, capital costs; 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 each subsystem 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 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; S3: In Python, the optimization software CBC is called through the toolkit Pulp to solve the subsequent model; 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; S5: Output the result data.

2. The capacity matching method according to claim 1, characterized in that: 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, including the system capacity of each subsystem and the operation volume of each subsystem at each time point; wherein each subsystem includes 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 to ensure 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 of S2.1 and the optimization constraints of S2.2, the optimization objective function is constructed to ensure that the net present value (NPV) is maximized throughout the system life cycle.

3. The capacity matching method according to claim 1, characterized in that: In S2, the total 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; Power of hydrogen production system, unit—MW; The above formula 1 is the mutual relationship function between the subsystems, that is, the overall system relationship constraint function.

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 hydrogen production system is expressed by the following formula: In the above formula: t: represents the specific time; The power used by the electrolyzer at time t, in MW; The upper and lower limits of the 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.

5. The capacity matching method according to claim 1, characterized in that: In S2, the constraint function of the system variables and the operating quantity 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; The upper and lower limits of the charging and discharging 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.

6. 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 : The hydrogen inventory ratio in the buffer tank in the initial state; 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 from the hydrogen production system directly 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.

7. 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 at 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.

8. The capacity matching method according to claim 1, characterized in that: In S2, the financial accounting constraint function is expressed by the following formula: In the above formula: t: represents the specific time; x: a set of decision variables; The cash flow of construction cost in year y, in ten thousand yuan; The operating cost cash flow in the yth year, unit—ten thousand yuan; The time set for the construction period; The time set of the operation period; r: discount rate; Y1: end year of the construction period; Initial investment, unit—ten thousand yuan; Equipment cost in the initial investment, unit—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—ten thousand yuan / MW; Unit cost of hydrogen storage system, unit—ten thousand yuan / Nm 3 ; Unit cost of new energy power generation system, unit—ten thousand yuan / MW; Income withtax :Income including tax, unit—ten thousand yuan; Fixed assets recovered in the yth year of operation, unit—ten thousand yuan; Total operating costs in year y, in ten thousand yuan; Subsequent investment in replacing the electrolytic cell, unit—ten thousand yuan; Value-added tax in the yth year, unit—ten thousand yuan; CIT y : Enterprise income tax in the yth year, unit—ten thousand yuan; BT y : Business tax for the yth year, unit—ten thousand yuan.

9. The capacity matching method according to claim 1, characterized in that: The S4 specifically comprises the following steps: S4.1 Initialization: Set input parameters and initial conditions, define objective function and constraints; S4.2 Modeling: Convert practical problems into mathematical models and clarify objective functions, decision variables and constraints; S4.3 Solution: Solve by 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.

10. The capacity matching method according to claim 9, 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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