Capacity planning method and system for wind power hydrogen production and methanation system

Abstract

The invention discloses a wind power hydrogen production and methanation system capacity planning method and system, and belongs to the technical field of new energy planning, and the method comprises the following steps: constructing a wind power hydrogen production and methanation system architecture, and defining the energy flow and material flow coupling relationship between modules; based on historical wind power output and load data, a multi-scene typical day model is established, and uncertainty parameters are quantified; proposing a system planning model, setting an objective function and constraint conditions, and solving the optimal capacity configuration of the system through an intelligent optimization algorithm; and according to the optimal capacity configuration result, optimizing a system operation strategy, and realizing dynamic coordination and energy balance of each module. Through an electricity-chemical collaborative wind power hydrogen production and methanation system architecture, dynamic matching of wind power and chemical loads is achieved, the wind curtailment rate is remarkably reduced, and the wind power utilization efficiency is improved; the hierarchical clustering algorithm is utilized to perform multi-scene typical day division on historical wind power and load data, and the calculation amount of system operation optimization data is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy planning, and in particular to a method and system for capacity planning of a wind power hydrogen production and methanation system. Background Art

[0002] Amid the global transition to a low-carbon energy mix, thermal power still needs to be equipped with emission reduction measures such as carbon capture. Large-scale installations of renewable energy sources such as wind power are driving a surge in demand for hydrogen storage. Wind power hydrogen production technology converts wind energy into hydrogen through water electrolysis, providing an effective means for the storage and utilization of renewable energy. However, hydrogen storage and transportation face technical bottlenecks such as corrosion, leakage, and high costs. Leveraging the proven methanation technology of existing chemical plants to synthesize hydrogen produced by wind power with carbon dioxide captured by thermal power generation not only addresses hydrogen storage and transportation challenges but also leverages existing natural gas infrastructure and generates revenue, while reducing raw material costs and environmental penalties for chemical plants, further expanding the potential for wind energy applications in the chemical industry. Economical transactions with chemical plants, such as utilizing treated industrial wastewater for hydrogen production through electrolysis and direct sales to chemical plants, can effectively reduce emissions and raw material costs for chemical plants and leverage existing natural gas pipelines, significantly improving energy efficiency.

[0003] In existing technologies, the intermittent and volatile nature of wind power leads to inefficient hydrogen production systems and widespread wind curtailment. Furthermore, there is insufficient synergy between wind power and chemical loads, and a lack of systematic capacity planning methods. Existing optimization models often use fixed scenarios or large amounts of data, making it difficult to quickly and accurately achieve the optimal match between equipment capacity and operating mode. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: insufficient synergy between wind power and chemical loads, lack of systematic capacity planning method, and difficulty in quickly and accurately achieving optimal matching of equipment capacity and operating mode.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a capacity planning method for a wind power hydrogen production and methanation system, comprising the following steps:

[0007] Construct the wind power hydrogen production and methanation system architecture and define the coupling relationship between energy flow and material flow between modules;

[0008] Based on historical wind power output and load data, a multi-scenario typical day model is established to quantify uncertainty parameters;

[0009] Propose a system planning model, set the objective function and constraints, input quantized parameters, and solve the optimal capacity configuration of the system through intelligent optimization algorithms;

[0010] According to the optimal capacity configuration results, combined with the coupling relationship between wind power hydrogen production and methanation system architecture, the system operation strategy is optimized.

[0011] As a preferred solution of the capacity planning method of a wind power hydrogen production and methanation system described in the present invention, wherein: the wind power hydrogen production and methanation system architecture includes:

[0012] Wind turbines and thermal power units serve as power supply units; the carbon capture module captures carbon dioxide produced by the thermal power units; the hydrogen production module uses electricity to electrolyze water to generate hydrogen; and the methanation module combines hydrogen and carbon dioxide to synthesize methane. Electricity is supplied to electrical loads and the hydrogen production module, while hydrogen and methane are supplied to downstream energy markets.

[0013] Through modular design, the full process coupling of wind power generation, carbon capture, hydrogen production and methanation is achieved, energy utilization efficiency is improved, thermal power units and wind power units provide complementary power supply, and the carbon capture module recycles carbon dioxide for methane synthesis, avoiding carbon emissions and enhancing the environmental friendliness of the system.

[0014] As a preferred solution of the capacity planning method for a wind power hydrogen production and methanation system described in the present invention, the step of quantifying uncertainty parameters includes constructing a load uncertainty model and a wind power output uncertainty model; quantifying the uncertainty parameters by using a scenario analysis method;

[0015] The scenario analysis method covers multiple possible operating states through probability weights, improves the robustness of planning results, simplifies massive historical data into a limited number of typical scenarios, reduces the computational burden of the optimization model, and retains key uncertainty information.

[0016] As a preferred solution of the capacity planning method of a wind power hydrogen production and methanation system described in the present invention, the calculation method of the optimal capacity configuration of the system includes:

[0017] The objective function is set to minimize the total system cost; constraints are set to include equipment capacity restrictions and energy balance constraints; an improved quantum particle swarm algorithm is used to solve the system's optimal capacity configuration, and the solution results are compared to determine whether to perform mutation operations until the end conditions are met and the optimal capacity is obtained;

[0018] The improved quantum particle swarm algorithm avoids falling into local optimality by dynamically adjusting population diversity and mutation operations, thereby improving global search capabilities and convergence speed.

[0019] As a preferred solution of the capacity planning method of a wind power hydrogen production and methanation system described in the present invention, the optimization method of the system operation strategy includes:

[0020] The objective function is to minimize operating costs, maximize renewable energy utilization, and improve system stability. Constraints include module power balance constraints, energy storage system constraints, and transmission line constraints. The agent state variables are initialized, and then a consensus algorithm is used to gradually converge the state variables of all agents to the global optimum. Messages are exchanged between agents in real time, and iterations terminate when convergence conditions are met. Otherwise, the iteration continues.

[0021] Simultaneously optimizing economy, environmental protection, and reliability, and avoiding the one-sidedness of single-objective optimization, the multi-agent model decomposes complex systems into independently controllable units, reducing the communication and computing pressure of centralized control.

[0022] As a preferred solution of the capacity planning method for a wind power hydrogen production and methanation system according to the present invention, wherein: the convergence condition is that the power allocation deviation of each agent is less than a preset threshold;

[0023] Ensure that the decision variables of each agent are consistent within the allowable error range to avoid system shock or collapse due to local deviations.

[0024] As a preferred solution of the capacity planning method for a wind power hydrogen production and methanation system described in the present invention, when constructing the wind power hydrogen production and methanation system architecture, a hierarchical clustering algorithm is combined with the Euclidean distance and Ward link method to complete the division of typical days in multiple scenarios;

[0025] The Euclidean distance accurately measures data similarity, and the Ward link method minimizes intra-class variance, ensuring that typical scenarios cover major operating modes.

[0026] Another object of the present invention is to provide a system for capacity planning of wind power hydrogen production and methanation systems.

[0027] To solve the above technical problems, the present invention provides the following technical solutions: a system for capacity planning of wind power hydrogen production and methanation system, comprising a system architecture module, an energy management module, a data processing module, and an optimization algorithm module;

[0028] The system architecture module is used to construct a wind power hydrogen production and methanation system architecture;

[0029] The energy management module is used to define the coupling relationship between energy flow and material flow among modules;

[0030] The data processing module is used to establish a typical day model for multiple scenarios and quantify uncertainty parameters;

[0031] The optimization algorithm module uses an intelligent optimization algorithm to solve the optimal capacity configuration of the system.

[0032] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the wind power hydrogen production and methanation system capacity planning method are implemented.

[0033] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for capacity planning of a wind power hydrogen production and methanation system are implemented.

[0034] Beneficial effects of the present invention:

[0035] Through the electro-chemical coordinated wind power hydrogen production and methanation system architecture, dynamic matching of wind power and chemical loads is achieved, significantly reducing the wind curtailment rate and improving wind power utilization efficiency; using the hierarchical clustering algorithm to divide historical wind power and load data into multiple scenarios and typical days, based on the obtained multi-scenario typical day data, the amount of system operation optimization data calculation is significantly reduced; through the improved quantum particle swarm algorithm, the equipment capacity of the wind power hydrogen production and methanation system is accurately determined, reducing investment and operating costs, improving economic benefits, and enhancing the environmental protection and economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a general flow chart of a capacity planning method for a wind power hydrogen production and methanation system provided in one embodiment of the present invention.

[0038] Figure 2 A structural diagram of a wind power hydrogen production and methanation system provided in one embodiment of the present invention.

[0039] Figure 3 A hierarchical clustering typical day division flowchart of a wind power hydrogen production and methanation system capacity planning method provided in one embodiment of the present invention.

[0040] Figure 4 A typical scenario division of wind power output data for a wind power hydrogen production and methanation system capacity planning method provided in one embodiment of the present invention.

[0041] Figure 5A typical scenario division of load data for a wind power hydrogen production and methanation system capacity planning method provided in one embodiment of the present invention.

[0042] Figure 6 A flowchart of an improved quantum particle swarm algorithm for a capacity planning method for a wind power hydrogen production and methanation system provided in one embodiment of the present invention.

[0043] Figure 7 This is a system operation optimization result of a wind power hydrogen production and methanation system capacity planning method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0045] Example 1, with reference to Figures 1 to 7 , which is the first embodiment of the present invention, provides a method for capacity planning of a wind power hydrogen production and methanation system, comprising the following steps:

[0046] S1. Construct the wind power hydrogen production and methanation system architecture and define the coupling relationship between energy flow and material flow among each module.

[0047] The wind power hydrogen production and methanation system architecture includes: wind turbines and thermal power units as power supply units;

[0048] The carbon capture module captures carbon dioxide produced by thermal power units; the hydrogen production module uses electricity to electrolyze water to generate hydrogen; and the methanation module synthesizes methane from hydrogen and carbon dioxide.

[0049] Among them, electricity is supplied to electrical loads and hydrogen production modules, and hydrogen and methane are supplied to the downstream energy market.

[0050] In one optional embodiment, wind power hydrogen production and methanation technology are used to convert wind power into hydrogen and synthesize methane, improving the utilization rate of renewable energy while promoting multi-energy conversion in the system. A new energy chemical plant using wind power hydrogen production can convert fluctuating wind power into stable hydrogen energy. Methanation technology can then be used to combine hydrogen with carbon dioxide to produce synthetic methane. This demonstrates the structure of a wind power hydrogen production and methanation system for a new energy chemical plant.

[0051] The historical wind power output and load data were divided using a hierarchical clustering algorithm. The specific steps are as follows: First, the 8760 hours of historical wind power output and load data collected were preprocessed by data cleaning and normalization, and cluster centers were randomly selected. Then, the shortest distance between the sample data and the cluster center was calculated based on the Euclidean distance. The calculation formula is expressed as:

[0052]

[0053] Then the Ward link method is used to continuously update the cluster center, and the formula is expressed as:

[0054]

[0055] Where d represents the Euclidean distance between any two points; D ward Represents the distance between any two data clusters; C A 、C B represents data clusters A and B; μ A 、μ B represents the centroid of data clusters A and B; x i and x j Represent two sample data points respectively; x ik and x jk Represents two sample data points x i and x j The values ​​in k dimensions.

[0056] Finally, the scenarios are grouped into the data cluster closest to the cluster center, and the cluster center is determined to see if it has changed, until the optimal clustering result is output. The four most representative scenarios are generated as typical daily scenarios, and the typical daily wind power output and electrical load are obtained.

[0057] S2. Based on historical wind power output and load data, a multi-scenario typical day model is established to quantify uncertainty parameters. Specifically: a hierarchical clustering algorithm is used to divide historical wind power and load data into multi-scenario typical days, such as Figure 4 and Figure 5 As shown in the figure, based on the obtained multi-scenario typical day data, the scenario analysis method is used to establish the uncertainty model of wind power and load data and quantify the uncertainty parameters. The hierarchical clustering typical day division flow chart is shown in the attached figure. Figure 3 shown.

[0058] Construct load uncertainty model and wind power output uncertainty model; quantify uncertain parameters through scenario analysis method.

[0059] In an optional embodiment, when constructing a load uncertainty model, the statistical characteristics of the load data are analyzed by calculating the mean, variance, and kurtosis. The core of the load uncertainty model is to describe the fluctuation characteristics of load demand through probability distribution. Since the load fluctuation is small, a normal distribution is used to fit the load data, which can be expressed by the numerical formula:

[0060] D t A =D t +e t D ;

[0061]

[0062] Where D t A and D t They represent the actual value and predicted value of the system load during period t respectively; e t D represents the load forecast error; σ D,t represents the standard deviation of load forecast error; e LF is the proportionality coefficient that characterizes the prediction accuracy.

[0063] In another optional embodiment, when constructing an uncertain wind power output model, wind power output is affected by natural conditions (wind speed, wind direction, temperature, etc.) and has randomness and uncertainty. A probability distribution model can be used to describe its distribution characteristics. The Weibull distribution is used to describe the change in wind speed. The mathematical formula is expressed as:

[0064]

[0065] Then, the wind power output is predicted based on the average wind speed on a typical day, and the normal distribution is used to describe the wind power prediction error. The probability density function of wind power is expressed as:

[0066]

[0067] Where F represents the probability that the wind speed is less than or equal to a specific value, k and c are the shape and scale parameters of the Weibull distribution function, v represents the wind speed, μ P and σ p are the expected and standard deviation of wind power output P respectively.

[0068] If these two embodiments are combined, the uncertain parameters are quantified based on the scenario analysis method. The scenario analysis method is used to describe the uncertainty of load and wind power output. First, a large number of random scenarios are generated using Latin hypercube sampling. This method uses stratified sampling to randomly select a sample in each group based on existing historical data to generate a uniformly distributed random sequence, and divide the sample interval into n sub-intervals; the midpoint of each interval is taken as the approximate value of the interval, and the sampling result of the tth hour is obtained by calculating the inverse function of the scenario distribution function. The calculation is repeated many times to obtain n scenario sampling results, and the probability of each scenario is 1 / n. After generating a large number of scenarios, a hierarchical clustering algorithm is used to reduce the scenarios, and finally generate typical uncertainty scenarios and their corresponding occurrence probabilities.

[0069] Specifically: Collect wind power output and load data for the past year (8,760 hours). Remove outliers and missing values ​​to ensure data integrity and accuracy. Normalize wind power output and load data to the [0,1] range for subsequent processing.

[0070] Divide the sample interval into n = 10 subintervals, randomly select a sample in each subinterval, and generate a uniformly distributed random sequence: for each subinterval, take the midpoint as the approximate value of the interval. Obtain the sampling result at hour t by calculating the inverse function of the scenario distribution function. Repeat the calculation multiple times: Repeat the above steps multiple times to generate n = 100 random scenarios. The probability of each scenario is 1 / n = 0.01;

[0071] Calculate the Euclidean distance between scenarios and cluster the 100 generated scenarios using a hierarchical clustering algorithm. Set the number of clusters to k = 5, and generate five representative scenarios. The probability of each representative scenario is the sum of the probabilities of all scenarios in the cluster to which it belongs.

[0072] Typical scenarios can be described as follows: Typical scenario 1: high wind power output and low load; Typical scenario 2: medium wind power output and medium load; Typical scenario 3: low wind power output and high load; Typical scenario 4: large wind power output fluctuations and small load fluctuations; Typical scenario 5: small wind power output fluctuations and large load fluctuations. The probabilities of typical scenarios are: Typical scenario 1 is 0.25; Typical scenario 2 is 0.2; Typical scenario 3 is 0.3; Typical scenario 4 is 0.15; and Typical scenario 5 is 0.1.

[0073] The five typical scenarios and their associated probabilities were used as input for uncertainty analysis in the system planning model. Under each typical scenario, the optimal capacity configuration of the system was calculated. By integrating the probabilities of each scenario, the final optimal capacity configuration was determined. Finally, five typical uncertainty scenarios and their corresponding occurrence probabilities were successfully generated. These scenarios can be used for capacity planning and operational strategy optimization of wind power hydrogen production and methanation systems, ensuring system stability and cost-effectiveness under varying uncertainty conditions.

[0074] S3. Propose a system planning model, set the objective function and constraints, input the quantified parameters, and solve the optimal capacity configuration of the system through an intelligent optimization algorithm.

[0075] The calculation method for the optimal capacity configuration of the system includes: setting the objective function as minimizing the total cost of the system; setting constraints including equipment capacity limit and energy balance constraint; using the improved quantum particle swarm algorithm to solve the optimal capacity configuration of the system, and comparing the solution results to determine whether to perform mutation operations until the end conditions are met and the optimal capacity is obtained.

[0076] In this embodiment, the improved quantum particle swarm algorithm is used to solve the system optimization capacity configuration. The calculation flow chart is as follows: Figure 6 As shown in the figure, a mathematical model for wind power hydrogen production and methanation system planning with electricity-chemical synergy is proposed. The objective function and constraints are determined, and the improved quantum particle swarm algorithm is used to solve the system optimal capacity planning. The minimum operating cost of the system is taken as the objective function, while considering the energy balance constraint and equipment power constraint. The specific steps are as follows:

[0077] minC=C inv +C wind +C tp +C P2H +C P2G ;

[0078]

[0079] Where, C, C inv 、C wind 、C tp 、C P2H 、C P2G are the total cost, investment cost of wind power, thermal power, electricity-to-hydrogen, power-to-gas (methane), and equipment operation and maintenance costs; P wind,t 、P tp,t 、P E,t 、P CCS,t 、P P2H,t 、P P2G,t They are the power of wind turbine, thermal power unit, electric load, carbon capture module, electric hydrogen production module and methanation module at time t respectively.

[0080] A planning model for a wind power-to-hydrogen and methanation system that considers electricity-to-chemical synergy is proposed. The objective function is to minimize the total system cost, which includes the operating costs of wind power and thermal power units, the operating cost of carbon capture modules, the investment and operating costs of hydrogen production and methanation, and the cost of gas storage. Constraints include equipment capacity limitations and energy balance constraints.

[0081] The improved quantum particle swarm algorithm can significantly improve the global search capability and convergence speed of the algorithm for solving the objective function. Among them, the position of the particle is determined by the attractor p and the disturbance part. By improving the attractor, it can ensure the diversity of the population in the process of tending to the local optimum and avoid falling into the local optimal solution. The improved quantum particle swarm algorithm is used to solve the system optimization capacity configuration. First, the quantum particle swarm algorithm is used to search for the target in the D-dimensional space. There is a set of N potential solutions of the objective function {x 1，k ,…,x N，k}, where k is the number of iterations. Since particles do not have velocity vectors, the optimal position of the particles in the group is expressed as P g，k =(p g,k-1 ,…,p g,k-D ), calculate the fitness of the particles and the average optimal position of the population; calculate the diversity measure ω of the population, which is specifically expressed as:

[0082]

[0083] Compare the obtained metric value ω with the set value ω0 to determine whether to perform mutation operation and update the particle position until the end condition is met to obtain the optimal capacity, where M represents the number of particles in the population, X i Indicates the position of the particle.

[0084] In this embodiment, the parameters are expressed as: M=5; maximum number of iterations k=100, and population diversity threshold ω0=0.1.

[0085] The position of each particle is represented as a D-dimensional vector, where each dimension corresponds to the capacity configuration of a device in the system (such as wind turbine capacity or hydrogen production module capacity). M = 50 particle positions are randomly generated in this D-dimensional space. The fitness function is used to calculate the fitness of each particle, i.e., the total system cost, and the optimal position of each particle in the current population is recorded. Finally, a population diversity metric is obtained and the calculated ω is compared with a set value of 0.1. If ω < ω0, a mutation operation is performed to increase population diversity. The positions of some particles are randomly perturbed to avoid falling into local optimal solutions. The position of each particle is updated according to the update rules of the improved quantum particle swarm algorithm, ensuring that the updated particle position satisfies all constraints.

[0086] In another optional embodiment, a dynamic population update mechanism is also set up. For example, the population size is 50 and the number of iterations is 200. The capacity of the wind turbine, hydrogen production module and methanation module is obtained through the quantum particle swarm algorithm to ensure that the wind power capacity can fully utilize wind energy resources and provide a stable power supply.

[0087] S4. Based on the optimal capacity configuration result, combined with the coupling relationship between the wind power hydrogen production and methanation system architecture, optimize the system operation strategy.

[0088] Reference Figure 7 As shown in the figure, the optimization method of the system operation strategy includes minimizing the operating cost, maximizing the utilization rate of renewable energy and improving the system stability as the objective function; taking the power balance constraints of each module, the energy storage system constraints and the transmission line constraints as the constraints; initializing the state variables of the agent, and then using the consistency algorithm to make the state variables of all agents gradually converge to the global optimum; exchanging messages between agents in real time, and finally terminating the iteration until the convergence conditions are met, otherwise, the iterative update continues.

[0089] In an optional embodiment, each device in the system is modeled as an independent agent, and operational coordination is achieved through information exchange, effectively solving the optimization problem of the power system. The overall wind power hydrogen production and methanation system is divided into a wind power generation agent, an electric hydrogen production agent, and a methanation agent. Optimization objectives and constraints are proposed for each of the three independent agents, and expressed using the mathematical formula:

[0090]

[0091]

[0092] Where x i 、x j is the decision variable of agent i and j, N i is the neighboring agent of agent i and j; P i,t wind is the wind power of agent i at time t, P i,t P2H is the output power of the hydrogen production module of agent i at time t, P i,t P2G is the output power of the methanogenation module at time t, P i,t H2 is the hydrogen production of agent i at time t.

[0093] The convergence condition is that the power allocation deviation of each agent is less than the preset threshold.

[0094] In an optional embodiment, a fixed threshold setting can be adopted. According to the historical operation data and experience of the system, a fixed power allocation deviation threshold is set, for example, 0.1 (unit is power unit); the state variables of each agent in the system are initialized, and the state variables of each agent are gradually updated through the consistency algorithm, and the power allocation deviation of each agent is calculated. After each iteration, it is checked whether the power allocation deviation of each agent is less than the preset fixed threshold. If the condition is met, the iteration is terminated; otherwise, the iterative update continues.

[0095] In another optional embodiment, dynamic threshold setting can also be adopted. By defining a threshold decay function, for example, as the number of iterations increases, the threshold gradually decreases. The state variables of each agent in the system are initialized, and the state variables of each agent are gradually updated through a consistency algorithm. The power allocation deviation of each agent is calculated. After each iteration, the current threshold is updated according to the threshold decay function. It is checked whether the power allocation deviation of each agent is less than the current dynamic threshold. If the condition is met, the iteration is terminated; otherwise, the iterative update continues.

[0096] In this embodiment, the attenuation function is expressed as:

[0097] threshold(t)=threshold0*e -λt ;

[0098] In this formula, threshold0 is the initial threshold, λ is the attenuation coefficient, and t is the number of iterations.

[0099] In summary, through the electro-chemical coordinated wind power hydrogen production and methanation system architecture, dynamic matching of wind power and chemical loads is achieved, the wind curtailment rate is significantly reduced, and the wind power utilization efficiency is improved; the hierarchical clustering algorithm is used to divide the historical wind power and load data into multiple scenario typical days, and based on the obtained multi-scenario typical day data, the amount of system operation optimization data calculation is significantly reduced; through the improved quantum particle swarm algorithm, the equipment capacity of the wind power hydrogen production and methanation system is accurately determined, the investment and operating costs are reduced, the economic benefits are improved, and the environmental protection and economy of the system are improved.

[0100] Example 2 is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0103] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0104] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Example 3 is the third embodiment of the present invention. This embodiment provides a system for capacity planning of a wind power hydrogen production and methanation system, including a system architecture module, an energy management module, a data processing module, and an optimization algorithm module.

[0106] The system architecture module is used to construct a wind power hydrogen production and methanation system architecture;

[0107] The energy management module is used to define the coupling relationship between energy flow and material flow among modules;

[0108] The data processing module is used to establish a typical day model for multiple scenarios and quantify uncertainty parameters;

[0109] The optimization algorithm module uses an intelligent optimization algorithm to solve the optimal capacity configuration of the system.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A capacity planning method for a wind power hydrogen production and methanation system, characterized by: The following steps are included: Construct the wind power hydrogen production and methanation system architecture and define the coupling relationship between energy flow and material flow between modules; Based on historical wind power output and load data, a multi-scenario typical day model is established to quantify uncertainty parameters; Propose a system planning model, set the objective function and constraints, input quantized parameters, and solve the optimal capacity configuration of the system through intelligent optimization algorithms; According to the optimal capacity configuration results, combined with the coupling relationship between wind power hydrogen production and methanation system architecture, the system operation strategy is optimized.

2. A wind power hydrogen production and methanation system capacity planning method according to claim 1, characterized in that: The wind power hydrogen production and methanation system architecture includes: Wind turbines and thermal power plants serve as power supply units; The carbon capture module captures carbon dioxide produced by the thermal power unit; The hydrogen production module uses electricity to electrolyze water to generate hydrogen; The methanation module synthesizes methane from hydrogen and carbon dioxide; Among them, electricity is supplied to electrical loads and hydrogen production modules, and hydrogen and methane are supplied to the downstream energy market.

3. A wind power hydrogen production and methanation system capacity planning method according to claim 2, characterized in that: The steps to quantify uncertainty parameters include, Construct load uncertainty model and wind power output uncertainty model; Quantify uncertain parameters through scenario analysis.

4. A capacity planning method for a wind power hydrogen production and methanation system according to claim 3, characterized in that: The method for calculating the optimal capacity configuration of the system includes: Set the objective function as minimizing the total cost of the system; Setting constraints includes equipment capacity limitations and energy balance constraints; Solve the system's optimal capacity configuration and compare the solution results to determine whether to perform mutation operations until the end conditions are met and the optimal capacity is obtained.

5. A wind power hydrogen production and methanation system capacity planning method according to claim 4, characterized in that: The optimization methods of system operation strategy include: The objective functions are to minimize operating costs, maximize renewable energy utilization, and improve system stability; The power balance constraints of each module, energy storage system constraints and transmission line constraints are used as constraints; Initialize the agent's state variables, and then use the consensus algorithm to gradually converge the state variables of all agents to the global optimum; Messages are exchanged between agents in real time until the convergence condition is met and the iteration is terminated. Otherwise, the iterative update continues.

6. A wind power hydrogen production and methanation system capacity planning method according to claim 5, characterized in that: The convergence condition is that the power allocation deviation of each agent is less than a preset threshold.

7. A wind power hydrogen production and methanation system capacity planning method according to claim 6, characterized in that: When constructing the wind power hydrogen production and methanation system architecture, the hierarchical clustering algorithm is combined with the Euclidean distance and Ward link method to complete the typical day division of multiple scenarios.

8. A system for capacity planning of a wind power hydrogen production and methanation system, applying a method for capacity planning of a wind power hydrogen production and methanation system according to any one of claims 1 to 7, characterized in that: Includes system architecture module, energy management module, data processing module, and optimization algorithm module; The system architecture module is used to construct a wind power hydrogen production and methanation system architecture; The energy management module is used to define the coupling relationship between energy flow and material flow among modules; The data processing module is used to establish a typical day model for multiple scenarios and quantify uncertainty parameters; The optimization algorithm module uses an intelligent optimization algorithm to solve the optimal capacity configuration of the system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a wind power hydrogen production and methanation system capacity planning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for capacity planning of a wind power hydrogen production and methanation system according to any one of claims 1 to 7 are implemented.

Images