Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupled system

Abstract

The present invention relates to energy system optimization and configuration technology, and more particularly to a two-stage optimization planning method, system, and medium for a wind-solar-hydrogen-storage coupled system. The method provides a two-stage optimization of the number of electrolytic hydrogen production equipment and the capacity of hydrogen storage tanks in the wind-solar-hydrogen-storage coupled system. Furthermore, it accurately models the electrolytic hydrogen production equipment within the system, fully considering the characteristics of regional wind and photovoltaic resources. This effectively ensures the model's computational accuracy and speed, and the optimization results provide intuitive data support for planners' judgment and analysis.

Description

Technical Field

[0001] The present invention relates to energy system optimization configuration technology, and in particular to a two-stage optimization planning method, system and medium for a wind-solar-hydrogen storage coupling system. Background Art

[0002] In recent years, the global energy transition has accelerated. Against this backdrop, building a new power system based on renewable energy is crucial. Currently, distributed renewable energy generation systems, represented by photovoltaic and wind power generation, have been widely deployed. However, wind and photovoltaic power generation rely on highly volatile resources such as wind and sunlight, and cannot meet the stability requirements of energy supply systems.

[0003] Hydrogen energy, with its advantages of zero carbon, high energy density, and ultra-long energy storage, has become an important technical means to solve the problem of power supply stability in remote areas. Building a wind-solar-hydrogen-storage coupling system and optimizing the capacity of electrolytic hydrogen production equipment and hydrogen storage equipment can effectively smooth out the volatility of wind and solar power generation, thereby improving the new energy absorption capacity in a more economical way and solving the problem of power supply reliability of microgrid systems. Therefore, the wind-solar-hydrogen-storage coupling system that integrates wind power generation, photovoltaic power generation, electrolytic hydrogen production, and hydrogen storage has received widespread attention from academia and industry, and a series of demonstration application devices have been built. Among them, it should be noted that, considering multiple indicators such as technical cost, capacity limitations, and degree of commercialization, the alkaline electrolyzer (ALK) is currently the only equipment that meets the requirements for large-scale commercial hydrogen production.

[0004] Among existing capacity matching models for coupled electricity and hydrogen systems, the paper "Design and Integrated Operation Optimization of a Wind-Solar-Hydrogen Integrated Energy System" (Electricity and Energy, Vol. 43, No. 2, p. 117) constructs an optimized matching model for an integrated photovoltaic and alkaline electrolyzer electricity-hydrogen system. However, it considers distributed power sources in a relatively limited manner and does not take into account the benefits of hydrogen storage tanks, making it unsuitable for optimizing modeling of wind-solar-hydrogen-storage fusion systems. The paper "Capacity Optimal Configuration and Day-Ahead Optimal Scheduling of a Wind-Solar-Hydrogen Coupled Power Generation System" (China Electric Power, Vol. 53, No. 10, p. 80) constructs an integrated design and operation optimization model for a coupled wind-solar-hydrogen energy system. It optimizes the capacity configuration options and typical operation strategies for each device in the system. However, the model does not fully consider the operational characteristics of hydrogen production equipment, resulting in potential deviations between the optimization results and actual conditions. The document "Optimal Day-ahead Dispatch of An Alkaline Electrolyser System Concerning Thermal–Electric Properties and State-Transitional Dynamics" (Applied Energy, Vol. 37, p. 1180) takes into account factors such as operating costs, efficiency, and life cycle, and constructs a multi-objective optimization model for energy management of an integrated new energy and hydrogen energy system. However, it only considers the operating and efficiency characteristics of a single electrolyzer, and does not consider the scenario of joint operation of multiple electrolyzers. Summary of the Invention

[0005] In view of this, the first purpose of the present invention is to provide a two-stage optimization planning method for a wind-solar-hydrogen-storage coupled system. By optimizing the number of electrolysis hydrogen production equipment and the capacity of hydrogen storage tanks in the wind-solar-hydrogen-storage coupled system in two stages, accurate modeling of the wind-solar-hydrogen coupled system is achieved, fully considering the characteristics of regional wind power and photovoltaic resources, and effectively ensuring the calculation accuracy and speed of the model. The optimization results can provide the most intuitive data support for planners' judgment and analysis.

[0006] Based on the same inventive concept, the second object of the present invention is to provide a two-stage optimization planning system for a wind-solar-hydrogen storage coupling system.

[0007] Based on the same inventive concept, the third object of the present invention is to provide a storage medium.

[0008] The first object of the present invention can be achieved by the following technical solutions:

[0009] A two-stage optimization planning method for a wind-solar-hydrogen storage coupled system includes the following steps:

[0010] Obtain wind and solar resource data;

[0011] Model the electrolytic hydrogen production equipment, construct a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment;

[0012] Model the hydrogen storage tank and construct a hydrogen storage tank optimization model based on time series simulation. Calculate the optimal hydrogen storage tank capacity using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation.

[0013] Output optimal capacity ratio.

[0014] Furthermore, the electrolytic hydrogen production equipment is modeled, and a wind-solar-hydrogen optimization model based on time series simulation is constructed. The wind-solar-hydrogen optimization model based on time series simulation is used to calculate the optimal installed capacity of the electrolytic hydrogen production equipment, including the following steps:

[0015] Establish a logical state model for electrolysis hydrogen production equipment;

[0016] Set the overall constraints of the wind-solar-hydrogen optimization model based on time series simulation, including overall equipment constraints of the electrolytic hydrogen production equipment, new energy output constraints, and electric power balance constraints. Among them, the overall equipment constraints of the electrolytic hydrogen production equipment include upper and lower power constraints of the electrolytic hydrogen production equipment, hydrogen production output constraints of the electrolytic hydrogen production equipment, and logical state constraints of the electrolytic hydrogen production equipment;

[0017] The first-stage objective function is set with the goal of maximizing the overall benefits of the wind-photovoltaic-hydrogen coupling system;

[0018] The optimization algorithm is used to calculate the optimal installed capacity of the electrolytic hydrogen production equipment.

[0019] Furthermore, a state model of the electrolytic hydrogen production equipment is established, including the following steps:

[0020] A logical state model for the operation of the electrolytic hydrogen production equipment is established. The operation state of the electrolytic hydrogen production equipment is divided into three states: working, standby, and idle. Binary variables L, S, and I are used to represent the working, standby, and idle states, respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable.

[0021] Establish an action logic state model for the electrolytic hydrogen production equipment, using binary variables Y and Z to represent the start-up and shutdown actions of the electrolytic hydrogen production equipment, respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the action state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the action state represented by the binary variable.

[0022] An input-output relationship model of the electrolytic hydrogen production equipment is established, with F representing the hydrogen production of the alkaline electrolyzer and U representing the power consumed by the alkaline electrolyzer.

[0023] Furthermore, the logic state constraint of the electrolytic hydrogen production equipment is expressed as:

[0024]

[0025] Among them, L, S, and I are binary variables indicating that the electrolysis hydrogen production equipment is in working, standby, and idle states, respectively; Y and Z are binary variables indicating the start-up and shutdown actions of the electrolysis hydrogen production equipment; t represents the time index, the superscript represents the moment of the electrolysis hydrogen production equipment, and the subscript n is the module index of the electrolysis hydrogen production equipment.

[0026] Furthermore, the expression of the objective function of the first stage is:

[0027]

[0028] Among them, T is the total length of simulation time, t is the time index, N is the number of electrolytic hydrogen production equipment, n is the module index of electrolytic hydrogen production equipment, P h is the hydrogen sales price, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, P hinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the integrated system to the large power grid at time t; Δt is the simulation time step.

[0029] Furthermore, the hydrogen storage tank is modeled and a hydrogen storage tank optimization model based on time series simulation is constructed. The optimized hydrogen storage tank capacity is calculated using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation:

[0030] Set the overall constraints of the hydrogen storage tank optimization model based on time series simulation, including overall equipment constraints of the hydrogen storage tank, new energy output constraints, and electric power balance constraints. Among them, the overall equipment constraints of the hydrogen storage tank include the maximum capacity constraint of the hydrogen storage tank and the dynamic capacity constraint of the hydrogen storage tank;

[0031] The second-stage objective function is set with the goal of maximizing the overall benefits of the wind-photovoltaic-hydrogen coupling system;

[0032] Use the optimization algorithm to calculate the optimal hydrogen storage tank installed capacity.

[0033] Furthermore, the maximum capacity constraint of the hydrogen storage tank is expressed as:

[0034] 0≤Q t ≤Q max

[0035] Among them, Q t is the hydrogen storage capacity of the hydrogen storage tank at time t; Q max is the maximum hydrogen storage capacity of the hydrogen storage tank;

[0036] The dynamic capacity constraint of the hydrogen storage tank is expressed as:

[0037]

[0038] in, is the external delivery volume of the hydrogen storage tank, ∑F t It represents the total hydrogen production of all electrolytic hydrogen production equipment at time t.

[0039] Furthermore, the expression of the second-stage objective function is:

[0040]

[0041] Among them, T is the total length of simulation time, t is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, n represents the module index of the electrolytic hydrogen production equipment, P h is the hydrogen sales price, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, P hinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, P sinv is the investment cost of hydrogen storage tank per unit capacity, C s is the scale of hydrogen storage tank; LT s For the entire life cycle of the hydrogen storage tank, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the wind-solar-hydrogen storage coupling system to the main power grid at time t; Δt is the simulation time step.

[0042] The second object of the present invention can be achieved by the following technical solutions:

[0043] A two-stage optimization planning system for a wind-solar-hydrogen storage coupling system, comprising:

[0044] Data acquisition module, used to obtain wind and light resource data;

[0045] The wind-solar-hydrogen optimization model module is used to model the electrolytic hydrogen production equipment, build a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment;

[0046] The hydrogen storage tank optimization model module is used to model the hydrogen storage tank, build a hydrogen storage tank optimization model based on time series simulation, and calculate the optimal hydrogen storage tank capacity using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation;

[0047] Output module, used to output the optimal capacity ratio.

[0048] The third object of the present invention can be achieved by the following technical solutions:

[0049] A storage medium stores a program, which, when executed by a processor, implements the above-mentioned two-stage optimization planning method for a wind-solar-hydrogen storage coupling system.

[0050] The present invention has the following beneficial effects compared to the prior art:

[0051] (1) The present invention optimizes the wind-solar-hydrogen storage coupling system by modeling the number of electrolysis hydrogen production equipment and the capacity of hydrogen storage tanks in two stages. Compared with the model in the prior art, the scale of variables in a single optimization process is reduced, thereby improving the calculation speed of the model.

[0052] (2) The present invention adopts a detailed modeling of the operating status of the electrolytic hydrogen production device and uses a time series simulation method for optimization planning, which improves the simulation accuracy of the wind-solar-hydrogen optimization model based on time series simulation and reduces the error caused by the electrolytic hydrogen production device in the optimization planning method.

[0053] (3) The timing simulation method designed in the present invention can fully consider the characteristics of regional new energy resources and simulate the actual operation of the power system in each time period, including the start and stop status of the alkaline electrolyzer at each time section, operating output, hydrogen production, hydrogen storage, power interaction power with the main grid, etc. It is more comprehensive when considering boundary conditions, and the calculation results are closer to the actual operation conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0055] Figure 2 is the normalized annual wind power hourly output sequence of Example 1 of the present invention;

[0056] Figure 3is the normalized annual photovoltaic hourly output sequence of Example 1 of the present invention;

[0057] Figure 4 This is a schematic diagram of the operation principle of the electrolytic hydrogen production equipment according to Example 1 of the present invention;

[0058] Figure 5 A bar chart showing the quantitative relationship between the system benefit and the number of alkaline electrolytic cells of the first-stage objective function of Example 1 of the present invention;

[0059] Figure 6 This is a line graph showing the quantitative relationship between the system benefit and the scale of the hydrogen storage tank in the second stage objective function of Example 1 of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] Example 1:

[0062] like Figure 1 As shown, this embodiment provides a method for optimizing planning of a wind-solar-hydrogen storage coupling system, including the following steps:

[0063] S100, obtaining wind and solar resource data;

[0064] In this embodiment, the wind and solar resource data are the wind and solar resource data of a certain region, including the installed capacity of wind farms, the installed capacity of photovoltaic power stations, the annual wind power hourly output sequence, and the annual photovoltaic hourly output sequence. The data is normalized to obtain the total installed capacity of the new energy stations in the region as 40MW. Among them, the installed capacity of wind farms is 23MW, the installed capacity of photovoltaic power stations is 17MW, and the normalized annual wind power hourly output sequence is as follows: Figure 2 As shown, the normalized annual photovoltaic hourly output sequence Figure 3 shown.

[0065] S200, such as Figure 4 As shown, the electrolysis hydrogen production equipment is modeled, and a wind-solar-hydrogen optimization model based on time series simulation is constructed. The wind-solar-hydrogen optimization model based on time series simulation is used to calculate the optimal installed capacity of the electrolysis hydrogen production equipment (i.e., the number of modules), including the following steps:

[0066] S210, establishing a state model of the electrolytic hydrogen production equipment, including the following steps:

[0067] S211. Establish a logical state model for the operation of the electrolytic hydrogen production equipment, and divide the operation state of the electrolytic hydrogen production equipment into three states: working, standby and idle. Use binary variables L, S and I to represent the working, standby and idle states respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable; for example, if the alkaline electrolyzer equipment is in the working state, then L=1; otherwise, L=0.

[0068] S212. Establish a logic state model for the electrolytic hydrogen production equipment. Use binary variables Y and Z to represent the startup and shutdown actions of the electrolytic hydrogen production equipment, respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the operational state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the operational state represented by the binary variable. For example, when the alkaline electrolytic cell switches from the standby state to the operating state, Y = 1 and Z = 0.

[0069] S213. Establish an input-output relationship model for the electrolytic hydrogen production equipment, using F to represent the hydrogen production of the alkaline electrolytic cell and U to represent the power consumed by the alkaline electrolytic cell.

[0070] S220. Setting overall constraints for the wind-solar-hydrogen optimization model based on time series simulation, including overall equipment constraints for the electrolytic hydrogen production equipment, new energy output constraints, and electric power balance constraints. The overall equipment constraints for the electrolytic hydrogen production equipment include upper and lower power constraints for the electrolytic hydrogen production equipment, hydrogen production output constraints for the electrolytic hydrogen production equipment, and logical state constraints for the electrolytic hydrogen production equipment.

[0071] In this embodiment, the expression of the new energy output constraint is:

[0072]

[0073]

[0074] in, is the output of the wind farm at time t, is the theoretical output of the wind farm at time t; is the output of the photovoltaic power station at time t, The theoretical output of the photovoltaic power station at time t.

[0075] In this embodiment, the expression of the electric power balance constraint is:

[0076]

[0077] in, represents the operating power of the nth electrolytic hydrogen production equipment at time t, It is the power provided by the integrated system to the large power grid at time t.

[0078] In this embodiment, the expression for the upper and lower power limits of the electrolytic hydrogen production equipment is:

[0079]

[0080] in, Indicates the operating power of the nth electrolytic hydrogen production equipment when it is in standby state, Indicates the minimum operating power when the nth electrolytic hydrogen production equipment is in working state, Indicates the maximum operating power of the nth electrolytic hydrogen production equipment when it is in working state. Indicates the current power of the nth electrolytic hydrogen production equipment, Indicates the standby state of the nth electrolytic hydrogen production equipment at time t; Indicates the working status of the nth electrolytic hydrogen production equipment at time t.

[0081] In this embodiment, the expression for the hydrogen production output constraint of the electrolytic hydrogen production equipment is:

[0082]

[0083] in, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, where a and b are constant parameters.

[0084] In this embodiment, the expression of the logic state constraint of the electrolytic hydrogen production equipment is:

[0085]

[0086] Among them, L, S, and I are binary variables indicating that the electrolysis hydrogen production equipment is in working, standby, and idle states, respectively; Y and Z are binary variables indicating the start-up and shutdown actions of the electrolysis hydrogen production equipment; t represents the time index, the superscript represents the moment of the electrolysis hydrogen production equipment, and the subscript n is the module index of the electrolysis hydrogen production equipment.

[0087] S230. Set the first-stage objective function with the goal of maximizing the overall benefit of the wind-solar-hydrogen coupling system;

[0088] In this embodiment, the expression of the objective function of the first stage is:

[0089]

[0090] Among them, T is the total length of simulation time, t is the time index, N is the number of electrolytic hydrogen production equipment, n is the module index of electrolytic hydrogen production equipment, P h is the selling price of hydrogen, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, Phinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the integrated system to the large power grid at time t; Δt is the simulation time step.

[0091] S240. Use the optimization algorithm to calculate the optimal installed capacity of the electrolytic hydrogen production equipment.

[0092] In this embodiment, the electrolytic hydrogen production equipment is an alkaline electrolyzer, and its module parameters are shown in Table 1:

[0093] Table 1 Alkaline electrolyzer module parameters

[0094]

[0095] The electricity prices for different periods in this area are shown in Table 2:

[0096] Table 2 Electricity prices for different periods

[0097]

[0098] In this embodiment, the simulation time step is 1 hour, and the simulation results of step S200 are as follows: Figure 5 shown.

[0099] S300, modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time series simulation, and calculating the optimized hydrogen storage tank capacity using the optimized electrolysis hydrogen production equipment installed capacity and the hydrogen storage tank optimization model based on time series simulation, including the following steps:

[0100] S310, setting the overall constraints of the hydrogen storage tank optimization model based on time series simulation, including the overall equipment constraints of the hydrogen storage tank, the new energy output constraints, and the electric power balance constraints, wherein the overall equipment constraints of the hydrogen storage tank include the maximum capacity constraints of the hydrogen storage tank and the dynamic capacity constraints of the hydrogen storage tank;

[0101] In this embodiment, the maximum capacity constraint of the hydrogen storage tank is expressed as:

[0102] 0≤Q t ≤Q max

[0103] Among them, Q t is the hydrogen storage capacity of the hydrogen storage tank at time t; Q max is the maximum hydrogen storage capacity of the hydrogen storage tank;

[0104] In this embodiment, the dynamic capacity constraint of the hydrogen storage tank is expressed as:

[0105]

[0106] in, is the external delivery volume of the hydrogen storage tank, ∑F t It represents the total hydrogen production of all electrolytic hydrogen production equipment at time t.

[0107] The new energy output constraint and the electric power balance constraint in step S310 of this embodiment have the same expressions as the corresponding constraints in step S210 and are not described again here.

[0108] S320. Set the second-stage objective function with the goal of maximizing the overall benefit of the wind-solar-hydrogen coupling system;

[0109] In this embodiment, the expression of the second stage objective function is:

[0110]

[0111] Among them, T is the total length of simulation time, t is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, n represents the module index of the electrolytic hydrogen production equipment, P h is the hydrogen sales price, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, P hinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, P sinv is the investment cost of hydrogen storage tank per unit capacity, C s is the scale of hydrogen storage tank; LT s For the entire life cycle of the hydrogen storage tank, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the wind-solar-hydrogen storage coupling system to the main power grid at time t; Δt is the simulation time step.

[0112] S330. Calculate the optimal installed capacity of the hydrogen storage tank using an optimization algorithm.

[0113] In this embodiment, the simulation time step is 1 hour, and the simulation results of step S300 are as follows: Figure 6 shown.

[0114] S400: Output optimal capacity ratio.

[0115] To summarize, this embodiment optimizes the wind-solar-hydrogen storage coupling system in two stages, namely, the number of electrolysis hydrogen production equipment and the capacity of hydrogen storage tanks. Compared with the models in the prior art, the scale of variables in a single optimization process is reduced, thereby improving the calculation speed of the model. This embodiment adopts a detailed modeling of the operating status of the electrolysis hydrogen production device, and adopts a timing simulation method for optimization planning, thereby improving the simulation accuracy of the wind-solar-hydrogen optimization model based on timing simulation, and reducing the error caused by the electrolysis hydrogen production device in the optimization planning method. The timing simulation method designed in this embodiment can fully consider the characteristics of regional new energy resources, and simulate the actual operation of the power system in each time period, including the start and stop status of the alkaline electrolyzer at each time section, operating output, hydrogen production, hydrogen storage, power interaction power with the main grid, etc. It is more comprehensive when considering boundary conditions, and the calculation results are closer to the actual operation conditions.

[0116] Example 2:

[0117] This embodiment provides a two-stage optimization planning system for a wind-solar-hydrogen storage coupled system, including:

[0118] Data acquisition module, used to obtain wind and light resource data;

[0119] The wind-solar-hydrogen optimization model module is used to model the electrolytic hydrogen production equipment, build a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment;

[0120] The hydrogen storage tank optimization model module is used to model the hydrogen storage tank, build a hydrogen storage tank optimization model based on time series simulation, and calculate the optimal hydrogen storage tank capacity using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation;

[0121] Output module, used to output the optimal capacity ratio.

[0122] That is, among the modules described above in this embodiment, the data acquisition module is used to implement step S100 of embodiment 1 of the present invention; the wind-solar-hydrogen optimization model module is used to implement step S200 of embodiment 1; the hydrogen storage tank optimization model module is used to implement step S300 of embodiment 1; and the output module is used to implement step S400 of embodiment 1. Since steps S100-S400 have been described in detail in embodiment 1, for the sake of brevity, the detailed implementation process of the modules described above in this embodiment refers to embodiment 1 and will not be repeated here.

[0123] Example 3:

[0124] This embodiment provides a storage medium storing a program. When the program is executed by a processor, the two-stage optimization planning method for a wind-solar-hydrogen storage coupled system according to embodiment 1 of the present invention is implemented, including the following steps:

[0125] A two-stage optimization planning method for a wind-solar-hydrogen storage coupled system includes the following steps:

[0126] Obtain wind and solar resource data;

[0127] Model the electrolytic hydrogen production equipment, construct a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment;

[0128] Model the hydrogen storage tank and construct a hydrogen storage tank optimization model based on time series simulation. Calculate the optimal hydrogen storage tank capacity using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation.

[0129] Output optimal capacity ratio.

[0130] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0131] In this embodiment, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this embodiment, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0132] The computer readable storage medium can be written in one or more programming languages ​​or a combination thereof to execute the computer program for performing the present embodiment, including object-oriented programming languages ​​such as Java, Python, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect via the Internet).

[0133] Obviously, the embodiments described above are only part of the embodiments of the present invention, rather than all the embodiments. The present invention is not limited to the details of the above embodiments. Any appropriate changes or modifications made by ordinary technicians in the relevant technical field are deemed to be within the patent scope of the present invention.

Claims

1. A two-stage optimization planning method for a wind-solar-hydrogen storage coupling system, characterized in that: The following steps are involved: Obtain wind and solar resource data; Model the electrolytic hydrogen production equipment, construct a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment; Establish the first stage objective function, the expression is: Among them, T is the total length of simulation time, t is the time index, N is the number of electrolytic hydrogen production equipment, n is the module index of electrolytic hydrogen production equipment, P h is the hydrogen sales price, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, P hinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the integrated system to the large power grid at time t; Δt is the simulation time step; The hydrogen storage tank is modeled and a hydrogen storage tank optimization model based on time series simulation is constructed. The optimized hydrogen storage tank capacity is calculated using the optimized installed capacity of the electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation. The second-stage objective function is established, and the expression is: Among them, T is the total length of simulation time, t is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, n represents the module index of the electrolytic hydrogen production equipment, P h is the hydrogen sales price, represents the hydrogen production of the nth electrolytic hydrogen production equipment at time t, P hinv is the investment cost of a single electrolytic hydrogen production equipment, LT h For the entire life cycle of electrolytic hydrogen production equipment, P sinv is the investment cost of hydrogen storage tank per unit capacity, C s is the scale of hydrogen storage tank; LT s For the entire life cycle of the hydrogen storage tank, and are the start-up and shutdown states of the nth alkaline electrolyzer module at time t, P s and P d are the startup cost and shutdown cost of the alkaline electrolyzer, is the electricity price of the large power grid at time t, is the power provided by the wind-solar-hydrogen-storage coupled system to the large power grid at time t; Δt is the simulation time step; Output optimal capacity ratio.

2. The two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to claim 1 is characterized in that: Model the electrolytic hydrogen production equipment, build a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment, including the following steps: Establish a state model of electrolysis hydrogen production equipment; Set the overall constraints of the wind-solar-hydrogen optimization model based on time series simulation, including overall equipment constraints of the electrolytic hydrogen production equipment, new energy output constraints, and electric power balance constraints. Among them, the overall equipment constraints of the electrolytic hydrogen production equipment include upper and lower power constraints of the electrolytic hydrogen production equipment, hydrogen production output constraints of the electrolytic hydrogen production equipment, and logical state constraints of the electrolytic hydrogen production equipment; The first-stage objective function is set with the goal of maximizing the overall benefits of the wind-photovoltaic-hydrogen coupling system; The optimization algorithm is used to calculate the optimal installed capacity of electrolytic hydrogen production equipment.

3. The two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to claim 2 is characterized in that: Establishing the state model of the electrolysis hydrogen production equipment includes the following steps: A logical state model for the operation of the electrolytic hydrogen production equipment is established. The operation state of the electrolytic hydrogen production equipment is divided into three states: working, standby, and idle. Binary variables L, S, and I are used to represent the working, standby, and idle states, respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable. Establish an action logic state model for the electrolytic hydrogen production equipment, using binary variables Y and Z to represent the start-up and shutdown actions of the electrolytic hydrogen production equipment, respectively. When the value of the binary variable is 1, it indicates that the electrolytic hydrogen production equipment is in the action state represented by the binary variable; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production equipment is not in the action state represented by the binary variable. An input-output relationship model of the electrolytic hydrogen production equipment is established, with F representing the hydrogen production of the alkaline electrolyzer and U representing the power consumed by the alkaline electrolyzer.

4. The two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to claim 3 is characterized in that: The logical state constraint of the electrolytic hydrogen production equipment is expressed as: Among them, L, S, and I are binary variables indicating that the electrolysis hydrogen production equipment is in working, standby, and idle states, respectively; Y and Z are binary variables indicating the start-up and shutdown actions of the electrolysis hydrogen production equipment; t represents the time index, the superscript represents the moment of the electrolysis hydrogen production equipment, and the subscript n is the module index of the electrolysis hydrogen production equipment.

5. The two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to claim 1 is characterized in that: Model the hydrogen storage tank and build a hydrogen storage tank optimization model based on time series simulation. Calculate the optimal hydrogen storage tank capacity using the optimized installed capacity of the electrolysis hydrogen production equipment and the hydrogen storage tank optimization model based on time series simulation: Set the overall constraints of the hydrogen storage tank optimization model based on time series simulation, including overall equipment constraints of the hydrogen storage tank, new energy output constraints, and electric power balance constraints. Among them, the overall equipment constraints of the hydrogen storage tank include the maximum capacity constraint of the hydrogen storage tank and the dynamic capacity constraint of the hydrogen storage tank; The second-stage objective function is set with the goal of maximizing the overall benefits of the wind-photovoltaic-hydrogen coupling system; The optimization algorithm is used to calculate the optimal hydrogen storage tank installed capacity.

6. The two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to claim 5 is characterized in that: The maximum capacity constraint of the hydrogen storage tank is expressed as: 0≤Q t ≤Q max Among them, Q t is the hydrogen storage capacity of the hydrogen storage tank at time t; Q max is the maximum hydrogen storage capacity of the hydrogen storage tank; The dynamic capacity constraint of the hydrogen storage tank is expressed as: in, is the external delivery volume of the hydrogen storage tank, ∑F t It represents the total hydrogen production of all electrolytic hydrogen production equipment at time t.

7. A two-stage optimization planning system for a wind-solar-hydrogen-storage coupled system, used to implement the two-stage optimization planning method for a wind-solar-hydrogen-storage coupled system according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to obtain wind and light resource data; The wind-solar-hydrogen optimization model module is used to model the electrolytic hydrogen production equipment, build a wind-solar-hydrogen optimization model based on time series simulation, and use the wind-solar-hydrogen optimization model based on time series simulation to calculate the optimal installed capacity of the electrolytic hydrogen production equipment; The hydrogen storage tank optimization model module is used to model the hydrogen storage tank, build a hydrogen storage tank optimization model based on time series simulation, and calculate the optimized hydrogen storage tank capacity using the optimized electrolysis hydrogen production equipment installed capacity and the hydrogen storage tank optimization model based on time series simulation; Output module, used to output the optimal capacity ratio.

8. A storage medium storing a program, characterized in that: When the program is executed by the processor, the two-stage optimization planning method for the wind-solar-hydrogen storage coupling system according to any one of claims 1 to 6 is implemented.

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