A method and system for optimizing the design of a biogenic natural gas system

Abstract

The application discloses a kind of biological natural gas system optimization design method and system, method includes: obtaining renewable energy power generation data, initial biogas data and power grid operation data;Based on the data obtained, a two-stage hydrogenation conversion of biogas-green hydrogen collaborative system optimization configuration model is constructed, the model maximizes the net present value of the whole life cycle as the target, the key component capacity is the decision variable, and contains the coupling constraint representing the competitive allocation relationship between hydrogen in the first biosynthesis and the second chemical synthesis;The model is decomposed into upper investment decision layer and lower operation decision layer, and a two-level optimization solving framework is constructed;A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, and the optimal capacity configuration scheme of the key components is obtained. The two-stage hydrogenation conversion process is collaboratively optimized and the capacity of the key components of the whole system is matched, which significantly improves the economy and operation flexibility of the biogas-green hydrogen coupled system.

Description

Technical Field

[0001] This invention belongs to the field of renewable energy system optimization design technology, and particularly relates to a biogas system optimization design method and system. Background Technology

[0002] Unlike traditional single-conversion pathways, the two-stage hydrogenation biogas production route involves: firstly, injecting a portion of the green hydrogen produced by electrolysis into an anaerobic fermentation system, where first-stage biosynthesis occurs through the action of hydrotrophic methanogens, increasing the methane concentration in the biogas; then, the remaining hydrogen undergoes a second-stage catalytic methanation reaction with the methane-rich gas produced in the first stage, further increasing the methane concentration to a level suitable for direct injection into natural gas pipelines or grid-connected power generation. This technology achieves deep coupling of green electricity, green hydrogen, and biomass energy, enabling the production of green fuels and serving as a long-term energy storage medium for peak shaving and valley filling in the power grid.

[0003] This technological approach involves multiple energy conversion and storage stages, including renewable energy power generation, hydrogen electrolysis, hydrogen storage buffering, two-stage hydrogen conversion, gas storage buffering, and gas-fired power generation. The system is complex, with diverse energy forms and a long timescale. The key components (electrolyzer capacity, hydrogen storage capacity, fermentation volume, catalytic reactor capacity, gas storage capacity, and generator capacity) are subject to material balance constraints and are also influenced by multiple factors such as fluctuations in wind and solar power output, time-of-use electricity prices, and grid peak-shaving demands. Their capacity configuration and operational strategies are highly coupled. Currently, there is a lack of systematic optimization planning methods for the entire two-stage hydrogen conversion chain. Existing research often focuses on single stages or neglects the configuration optimization of key buffer stages such as hydrogen and gas storage, making it difficult to achieve a synergistic optimization of overall system economy and operational flexibility.

[0004] In addition, existing technologies have the following shortcomings in system economic evaluation: First, they often use simplified static investment payback period indicators, failing to fully consider the time value of money and the benefits and costs throughout the entire life cycle; second, the important environmental benefit indicator of carbon emission reduction benefits is often ignored, making it difficult to comprehensively evaluate the overall economic performance of the system; and third, there is a lack of a two-layer solution framework that separates investment decision-making from operational optimization, resulting in low solution efficiency or getting trapped in local optima. Summary of the Invention

[0005] This invention provides a method and system for optimizing the design of biogas systems, which addresses the technical problems of uncoordinated capacity configuration of key components, incomplete economic assessment, and low solution efficiency in existing technologies.

[0006] In a first aspect, the present invention provides a method for optimizing the design of a biogas system, comprising:

[0007] Acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas;

[0008] Based on the renewable energy power generation data, the initial biogas data, and the power grid operation data, an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system is constructed. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogen conversion.

[0009] The optimization configuration model is decomposed into an upper investment decision layer and a lower operation decision layer to obtain a two-layer optimization model.

[0010] A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, thereby obtaining the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0011] Secondly, the present invention provides a biogas system optimization design system, comprising:

[0012] The acquisition module is configured to acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas.

[0013] The construction module is configured to construct an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system based on the renewable energy power generation data, the initial biogas data, and the power grid operation data. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogen conversion.

[0014] The decomposition module is configured to decompose the optimization configuration model into an upper investment decision layer and a lower operation decision layer to obtain a two-layer optimization model.

[0015] The solution module is configured to use a hybrid algorithm combining genetic algorithm and linear programming to solve the two-layer optimization model and obtain the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0016] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the biogas system optimization design method according to any embodiment of the present invention.

[0017] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the biogas system optimization design method according to any embodiment of the present invention.

[0018] The biogas system optimization design method and system of this application constructs an optimal configuration model for a biogas-green hydrogen synergistic system based on acquired data. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components as decision variables, and includes coupling constraints characterizing the competitive allocation relationship of hydrogen between primary biosynthesis and secondary chemical synthesis. The model is decomposed into an upper-level investment decision layer and a lower-level operation decision layer, constructing a two-layer optimization solution framework. A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-layer optimization model, obtaining the optimal capacity configuration scheme of key components. This achieves synergistic optimization of the two-stage hydrogen conversion process and capacity matching of key components throughout the system, significantly improving the economy and operational flexibility of the biogas-green hydrogen coupled system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating an optimized design method for a biogas system according to an embodiment of the present invention;

[0021] Figure 2 This is a structural block diagram of a biogas system optimization design system provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 The diagram shows a flowchart of an optimization design method for a biogas system according to this application.

[0025] like Figure 1 As shown, the optimization design method for biogas systems specifically includes the following steps:

[0026] Step S101: Obtain renewable energy power generation data, initial biogas data, and power grid operation data. The initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas.

[0027] In this step, renewable energy generation data is acquired:

[0028] The renewable energy power generation data includes photovoltaic power generation output data and wind power generation output data. The data is in time series format, usually with hourly sampling intervals (e.g., 8760 hours per year).

[0029] The data source can be historical operating data exported from the actual power plant's monitoring system (SCADA system), or typical annual output curves generated by simulating local meteorological resource data (such as irradiance and wind speed) using photovoltaic and wind power simulation software (such as PVsyst and WASP).

[0030] If a newly established project lacks actual measured data, publicly available typical meteorological year data can be combined with equipment power curves to generate simulations to ensure data representativeness.

[0031] Acquisition of initial biogas data:

[0032] The initial biogas data includes at least the initial biogas flow rate (in Nm³ / h) and the initial methane concentration of the initial biogas (volume percentage, %).

[0033] Obtaining the initial biogas flow rate: For existing anaerobic fermentation projects, the flow rate can be measured and recorded in real time using a gas flow meter (such as a vortex flow meter or thermal mass flow meter) installed on the outlet pipe of the fermenter. For projects in the planning stage, the theoretical flow rate can be calculated based on the type of fermentation raw materials, the amount of raw materials fed, fermentation process parameters, etc., using empirical formulas for gas production rate or material balance calculations.

[0034] Initial methane concentration in biogas can be obtained by continuous monitoring at the biogas outlet using an online gas analyzer (such as an infrared gas analyzer or a gas chromatograph). If online monitoring is not available, periodic sampling and laboratory analysis can be performed, or the initial concentration can be set based on the characteristics of the raw materials and fermentation process conditions, referring to the experience values ​​of similar projects (for example, the methane concentration in biogas from typical agricultural waste is approximately 50% to 60%).

[0035] Data recording frequency should be consistent with renewable energy data (e.g., one point per hour) to facilitate subsequent collaborative optimization.

[0036] Acquisition of power grid operation data:

[0037] The power grid operation data includes time-of-use electricity prices, time-of-use electricity purchase prices, and power grid peak-shaving demand signals.

[0038] Time-of-use (TOU) pricing: Obtained based on the peak-valley TOU pricing policy documents issued by the local power grid company. It is typically divided into peak hours, normal hours, and valley hours, with corresponding purchase prices (RMB / kWh) and sales prices (RMB / kWh) for each period. If no specific sales price is provided, the local benchmark price for coal or the average market transaction price can be used as a reference.

[0039] Grid peak demand signal: The peak demand curve of a typical day can be obtained from the grid dispatching department, or the peak period of the grid can be identified based on historical load data, and peak response constraints can be set (for example, requiring generator units to provide support of no less than a certain power during a specific period).

[0040] All acquired data needs to be preprocessed, including outlier removal, missing value imputation, and time alignment, to ultimately form a standardized dataset that meets the input requirements of the optimization model and is stored in the database for subsequent use.

[0041] Step S102: Based on the renewable energy power generation data, the initial biogas data, and the power grid operation data, construct an optimal configuration model for the biogas-green hydrogen synergistic system of two-stage hydrogenation conversion. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogenation conversion.

[0042] In this step, the expression for calculating the net present value over the entire life cycle is:

[0043]

[0044] ,

[0045] ,

[0046] In the formula, Net present value over the entire life cycle. For the system's design lifespan, For the first Annual net cash flow The discount rate is... The total investment cost of the system, This is the unit power investment cost coefficient for the electrolytic cell. To input electrical power into the electrolytic cell, This represents the investment cost coefficient per unit capacity of the hydrogen storage tank. For the capacity of the hydrogen storage tank, This represents the unit volume investment cost coefficient for anaerobic digesters. The effective volume of the anaerobic fermenter The investment cost coefficient per unit processing capacity of the catalytic reactor. The rated output capacity of the catalytic reactor, This represents the unit capacity investment cost coefficient for natural gas storage tanks. For the capacity of the natural gas storage tank, This is the unit power investment cost coefficient for generators. This refers to the generator's rated power. This represents the annual maintenance cost coefficient per unit capacity of the electrolytic cell. This represents the annual maintenance cost coefficient per unit capacity of the hydrogen storage tank. This represents the annual operating and maintenance cost coefficient per unit volume of the anaerobic digester. This represents the annual operation and maintenance cost coefficient per unit processing capacity of the catalytic reactor. This is the annual operation and maintenance cost coefficient per unit volume of natural gas storage tanks. This represents the annual operation and maintenance cost coefficient per unit power of the generator set. For time step, For the sales price of biogas, For time period The flow of biogas sold directly into the natural gas pipeline network. For time period Electricity sales price, For time period Electricity purchase price, For time period The grid-connected power output of the generator set, For carbon trading prices, As a carbon emission factor for the power grid, As a carbon emission factor of natural gas, For time period Electrolytic cell input power, For time period Photovoltaic power output, For time period Wind power output.

[0047] The expression for the coupling constraint is:

[0048] ,

[0049] ,

[0050] ,

[0051] ,

[0052] ,

[0053] In the formula, For time period methane production flow rate from primary biosynthesis This is the initial biogas flow rate. The hydrogen conversion efficiency of primary biosynthesis. For time period Hydrogen flow rate allocated to primary biosynthesis, For time period The methane flow rate from the secondary chemical synthesis The hydrogen conversion efficiency of secondary chemical synthesis. For time period Hydrogen flow rate allocated to secondary chemical synthesis, For time period Total hydrogen production flow rate This represents the concentration of methane produced by primary biosynthesis. The concentration of methane produced in the secondary chemical synthesis. The initial methane concentration, This represents the minimum methane concentration required for primary biosynthesis. This represents the minimum yield methane concentration for secondary chemical synthesis.

[0054] It should be noted that the optimization configuration model also includes hydrogen material balance constraints, natural gas material balance constraints, power generation balance constraints, equipment capacity constraints, and grid peak shaving and valley filling response constraints.

[0055] The expression for the hydrogen material balance constraint is:

[0056] ,

[0057] ,

[0058] ,

[0059] In the formula, For time period Hydrogen storage tank storage status For time period Hydrogen storage tank storage status The efficiency of hydrogen production in an electrolyzer. For time period Electrolytic cell input power, For time period Hydrogen production flow rate of secondary chemical synthesis For time period Hydrogen production flow rate from primary biosynthesis, For time step, For the capacity of the hydrogen storage tank, This represents the amount of hydrogen in the storage tank at 0:00 (initial time) of the day. This represents the amount of hydrogen in the hydrogen storage tank at time T (the end of the day);

[0060] The expression for the natural gas material balance constraint is:

[0061] ,

[0062] ,

[0063] ,

[0064] In the formula, For time period Storage status of the gas storage tank For time period Storage status of the gas storage tank For time period The flow of biogas sold directly into the natural gas pipeline network. For time period Biogas flow used for power generation For the capacity of the natural gas storage tank, This represents the amount of natural gas stored in the natural gas storage tank at 0:00 (initial time) of the day. This represents the amount of natural gas stored in the natural gas storage tank at time T (the end of the day);

[0065] The expression for the power generation balance constraint is:

[0066] ,

[0067] ,

[0068] In the formula, Let be the generator power at time t. This refers to the generator's rated power. The lower heating value of methane, This refers to the electrical efficiency of the generator set.

[0069] Step S103: Decompose the optimization configuration model into an upper investment decision layer and a lower operation decision layer to obtain a two-layer optimization model.

[0070] In this step, the capacity decision variable of key components is used as the optimization variable of the upper-level investment decision layer, and the maximization of the net present value over the entire life cycle is taken as the upper-level objective. The expression of the upper-level objective is as follows:

[0071] ,

[0072] In the formula, Configure for a given capacity Net present value over the entire life cycle, For capacity configuration schemes, For the system's design lifespan, The discount rate is... For year indexing, The total investment cost of the system, Configure for a given capacity At that time, the optimal annual net cash flow is obtained by optimizing the lower-level operation;

[0073] The operational variables for each time period are used as optimization variables for the lower-level operational decision-making layer. The goal of the lower-level layer is to maximize the annual operational revenue. The optimal annual operational revenue obtained from the lower-level optimization is fed back to the upper level to calculate the net present value of the entire life cycle under different capacity configurations. The operational variables include at least one of the following: electrolyzer input power, hydrogen flow rate allocated to primary biosynthesis, hydrogen flow rate allocated to secondary chemical synthesis, biomethane flow rate sold externally, biomethane flow rate used for power generation, storage status of hydrogen storage tanks, storage status of natural gas storage tanks, and grid-connected power output of generator sets.

[0074] Step S104: A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-layer optimization model to obtain the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0075] In this step, based on the two-layer optimization solution framework constructed in step S103, a hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-layer optimization model to obtain the optimal capacity configuration scheme for key components in the biogas-green hydrogen synergistic system. Specifically, it includes the following sub-steps:

[0076] Upper-level genetic algorithm initialization:

[0077] Chromosome encoding: Incorporating upper-level capacity decision variables Encoded as a real number vector, the chromosome structure is as follows:

[0078] ,

[0079] In the formula, A real number vector, For the power of the electrolytic cell, For the capacity of the hydrogen storage tank, The effective volume of the anaerobic fermenter The rated processing capacity of the catalytic reactor, For the capacity of the natural gas storage tank, This refers to the rated power of the generator set.

[0080] The value range of each variable is set according to the actual project requirements, such as the power of the electrolytic cell. The range of values ​​is Hydrogen storage tank capacity The range of values ​​is wait.

[0081] Population initialization: Set the population size (For example, taking a range of 50 to 100), maximum number of iterations (For example, take 200), crossover probability (e.g., 0.8), mutation probability (e.g., 0.1). An initial population is generated using a random uniform distribution within the feasible region of the decision variables. .

[0082] Lower-level linear programming solution and fitness evaluation:

[0083] For each individual in the population Perform the following operations:

[0084] Constructing the lower-level linear programming model: Substitute the current capacity configuration into the lower-level optimization problem, and linearize the objective function and constraints, transforming it into a standard linear programming form:

[0085] ,

[0086] In the formula, The coefficient vector of the objective function. The symbol for vector transpose. For the decision variable vector, For the constraint matrix, The vector on the right end;

[0087] The decision variable vector Includes all time periods runtime variables;

[0088] objective function coefficient vector The coefficients corresponding to the gas sales, electricity sales, carbon revenue, and electricity purchase cost items are determined based on the annual operating revenue expression. Constraint Matrix and the right-hand vector It is determined by both capacity configuration and external input data (wind and solar power output, time-of-use electricity price, initial biogas data, etc.).

[0089] Calling a linear programming solver: Use the dual simplex method or interior point method (e.g., using commercial solvers such as Gurobi, CPLEX, or open-source solvers such as GLPK) to solve the above linear programming problem and obtain the optimal values ​​of the running variables. and corresponding annual operating revenue .

[0090] Calculate the fitness value: Treat the optimal annual operating return returned by the lower layer as the same annual net cash flow for each year (i.e., assume that the operating strategy is the same every year), and substitute it into the objective function of the upper layer to calculate the net present value over the entire life cycle;

[0091] Genetic manipulation and population evolution:

[0092] Selection: A tournament selection mechanism is adopted. Each time, two individuals are randomly selected from the current population, their fitness values ​​are compared, and the individual with higher fitness is retained to enter the mating pool. This operation is repeated until the size of the mating pool reaches the size of the population.

[0093] Crossover: Individuals in the mating pool are randomly paired, and each pair of parent individuals is crossovered with a probability. Simulated binary crossover (SBX) is performed to generate two offspring individuals. Based on the characteristics of real-number encoding, the crossover operator generates new solutions near the parent, maintaining population diversity.

[0094] Mutation: For each offspring individual, the mutation probability is... Perform polynomial mutation to perturb each gene position on the chromosome with a small probability to prevent premature convergence of the algorithm.

[0095] Population renewal: The process of replacing the original population with a new generation of individuals generated through crossover mutation to form a new population. .

[0096] Termination judgment and result output:

[0097] Termination condition: Determine if the maximum number of iterations has been reached. Or the change in the optimal solution over multiple consecutive generations (e.g., 20 generations) is less than a preset threshold. If the termination condition is met, then evolution stops; otherwise, let... Continue iterating.

[0098] Optimal solution output: Output the best individual obtained during the evolution process, i.e. the optimal capacity configuration scheme.

[0099] To obtain stable and reliable optimization results, the parameters of the genetic algorithm can be appropriately adjusted according to the problem size in practical applications. For example, the population size... The chromosome length can be 10 to 20 times; crossover probability Typically set between 0.6 and 0.9; mutation probability The value can be between 0.01 and 0.1. Furthermore, when solving lower-level linear programming problems, various optimization options for the solver (such as preprocessing, scaling, etc.) can be enabled to improve solution efficiency.

[0100] Through the above hybrid algorithm, the upper-level genetic algorithm is responsible for the global search for the optimal capacity configuration, while the lower-level linear programming precisely solves the optimal operating strategy under the given configuration, realizing the synergy between investment decision-making and operation optimization, and effectively solving the optimization design problem of this complex system.

[0101] In summary, the method presented in this application achieves synergistic optimization of the two-stage hydrogen conversion process by establishing coupling constraints that explicitly describe the competitive allocation of hydrogen between primary biosynthesis and secondary chemical synthesis. For the first time, it incorporates the capacities of key components such as electrolyzers, hydrogen storage tanks, anaerobic digesters, catalytic reactors, natural gas storage tanks, and generator sets into a unified optimization framework, completing the coordinated configuration of the entire chain from wind and solar power generation to biogas grid connection. With the goal of maximizing the net present value over the entire life cycle, it comprehensively considers revenue from gas sales, electricity sales, carbon emission reduction, and various costs, and introduces a discount rate to reflect the time value of money, making the economic assessment more comprehensive and objective. Through the optimized configuration of hydrogen and gas storage tanks, the system can respond to time-of-use electricity pricing and grid peak-shaving demands, fully leveraging the value of long-term energy storage. The application employs a two-layer optimization solution strategy combining genetic algorithms and linear programming, balancing global optimality and computational efficiency, providing an efficient solution for the optimization design of complex systems, significantly improving the economy and operational flexibility of the biogas-green hydrogen coupling system, and possessing significant engineering application value.

[0102] Please see Figure 2 The diagram shows a structural block diagram of an optimized design system for a biogas system according to this application.

[0103] like Figure 2 As shown, the biogas system optimization design system 200 includes an acquisition module 210, a construction module 220, a decomposition module 230, and a solution module 240.

[0104] The system includes: an acquisition module 210, configured to acquire renewable energy power generation data, initial biogas data, and grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas; a construction module 220, configured to construct an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system based on the renewable energy power generation data, the initial biogas data, and the grid operation data, wherein the optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogen conversion; a decomposition module 230, configured to decompose the optimal configuration model into an upper investment decision layer and a lower operation decision layer, resulting in a two-layer optimization model; and a solution module 240, configured to solve the two-layer optimization model using a hybrid algorithm combining genetic algorithm and linear programming, to obtain the optimal capacity configuration scheme for key components in the biogas-green hydrogen synergistic system, wherein the key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0105] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0106] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the biogas system optimization design method in any of the above method embodiments.

[0107] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0108] Acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas;

[0109] Based on the renewable energy power generation data, the initial biogas data, and the power grid operation data, an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system is constructed. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogen conversion.

[0110] The optimization configuration model is decomposed into an upper investment decision layer and a lower operation decision layer to obtain a two-layer optimization model.

[0111] A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, thereby obtaining the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0112] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the biogas system optimization design system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the biogas system optimization design system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0113] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the biogas system optimization design method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the biogas system optimization design system. The output device 340 may include a display screen or other display device.

[0114] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0115] In one implementation, the above-described electronic device is applied in a biogas system optimization design system for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0116] Acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas;

[0117] Based on the renewable energy power generation data, the initial biogas data, and the power grid operation data, an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system is constructed. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship between primary biosynthesis and secondary chemical synthesis of hydrogen in the two-stage hydrogen conversion.

[0118] The optimization configuration model is decomposed into an upper investment decision layer and a lower operation decision layer to obtain a two-layer optimization model.

[0119] A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, thereby obtaining the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the design of a biogas system, characterized in that, include: Acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas; Based on the renewable energy power generation data, the initial biogas data, and the power grid operation data, an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system is constructed. This optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints characterizing the competitive allocation relationship of hydrogen between primary biosynthesis and secondary chemical synthesis in the two-stage hydrogen conversion. The expression for these coupling constraints is: ， ， ， ， ， In the formula, For time period methane production flow rate from primary biosynthesis This is the initial biogas flow rate. The hydrogen conversion efficiency of primary biosynthesis. For time period Hydrogen flow rate allocated to primary biosynthesis, For time period methane production flow rate from secondary chemical synthesis The hydrogen conversion efficiency of secondary chemical synthesis. For time period Hydrogen flow rate allocated to secondary chemical synthesis, For time period Total hydrogen production flow rate This represents the concentration of methane produced by primary biosynthesis. The concentration of methane produced in the secondary chemical synthesis. The initial methane concentration, This represents the minimum methane concentration required for primary biosynthesis. This represents the minimum yield methane concentration for secondary chemical synthesis. The optimization configuration model is decomposed into an upper-level investment decision-making layer and a lower-level operational decision-making layer, resulting in a two-layer optimization model, including: Using the capacity decision variable of key components as the optimization variable of the upper-level investment decision layer, and taking the maximization of net present value over the entire life cycle as the upper-level objective, the expression of the upper-level objective is as follows: ， In the formula, Configure for a given capacity Net present value over the entire life cycle, For capacity configuration schemes, For the system's design lifespan, The discount rate is... For year indexing, The total investment cost of the system, Configure for a given capacity At that time, the optimal annual net cash flow is obtained by optimizing the lower-level operation; The operational variables for each time period are used as optimization variables for the lower-level operational decision-making layer. The goal of the lower-level layer is to maximize the annual operational revenue. The optimal annual operational revenue obtained from the lower-level optimization is fed back to the upper level to calculate the net present value of the entire life cycle under different capacity configurations. The operational variables include at least one of the following: electrolyzer input power, hydrogen flow rate allocated to primary biosynthesis, hydrogen flow rate allocated to secondary chemical synthesis, biomethane flow rate sold externally, biomethane flow rate used for power generation, storage status of hydrogen storage tanks, storage status of natural gas storage tanks, and grid-connected power output of generator sets. A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, thereby obtaining the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

2. The biogas system optimization design method according to claim 1, characterized in that, in, The expression for calculating the net present value over the entire life cycle is: ， ， ， In the formula, Net present value over the entire life cycle. For the system's design lifespan, For the first Annual net cash flow The discount rate is... The total investment cost of the system, This is the unit power investment cost coefficient for the electrolytic cell. To input electrical power into the electrolytic cell, This represents the investment cost coefficient per unit capacity of the hydrogen storage tank. For the capacity of the hydrogen storage tank, This represents the unit volume investment cost coefficient for anaerobic digesters. The effective volume of the anaerobic fermenter The investment cost coefficient per unit processing capacity of the catalytic reactor. The rated output capacity of the catalytic reactor, This represents the unit capacity investment cost coefficient for natural gas storage tanks. For the capacity of the natural gas storage tank, This is the unit power investment cost coefficient for generators. This refers to the generator's rated power. This represents the annual maintenance cost coefficient per unit capacity of the electrolytic cell. This represents the annual maintenance cost coefficient per unit capacity of the hydrogen storage tank. This represents the annual operating and maintenance cost coefficient per unit volume of the anaerobic digester. This represents the annual operation and maintenance cost coefficient per unit processing capacity of the catalytic reactor. This is the annual operation and maintenance cost coefficient per unit volume of natural gas storage tanks. This represents the annual operation and maintenance cost coefficient per unit power of the generator set. For time step, For the sales price of biogas, For time period The flow of biogas sold directly into the natural gas pipeline network. For time period Electricity sales price, For time period Electricity purchase price, For time period The grid-connected power output of the generator set, For carbon trading prices, As a carbon emission factor for the power grid, As a carbon emission factor of natural gas, For time period Electrolytic cell input power, For time period Photovoltaic power output, For time period Wind power output.

3. The biogas system optimization design method according to claim 1, characterized in that, The optimization configuration model also includes hydrogen material balance constraints, natural gas material balance constraints, power generation balance constraints, equipment capacity constraints, and power grid peak shaving and valley filling response constraints. The expression for the hydrogen material balance constraint is: ， ， ， In the formula, For time period Hydrogen storage tank storage status For time period Hydrogen storage tank storage status The efficiency of hydrogen production in an electrolyzer. For time period Electrolytic cell input power, For time period Hydrogen production flow rate of secondary chemical synthesis For time period Hydrogen production flow rate from primary biosynthesis, For time step, For the capacity of the hydrogen storage tank, This represents the hydrogen level in the storage tank at 00:00 on the current day. This represents the amount of hydrogen in the hydrogen storage tank at time T on that day. The expression for the natural gas material balance constraint is: ， ， ， In the formula, For time period Storage status of the gas storage tank For time period Storage status of the gas storage tank For time period The flow of biogas sold directly into the natural gas pipeline network. For time period Biogas flow used for power generation For the capacity of the natural gas storage tank, This represents the amount of natural gas stored in the natural gas storage tank at 00:00 on the current day. This represents the amount of natural gas stored in the natural gas storage tank at time T on that day. The expression for the power generation balance constraint is: ， ， In the formula, Let be the generator power at time t. This refers to the generator's rated power. The lower heating value of methane, This refers to the electrical efficiency of the generator set.

4. The biogas system optimization design method according to claim 1, characterized in that, The optimal capacity configuration scheme includes sub-schemes for the rated power configuration of electrolyzers, capacity configuration of hydrogen storage tanks, effective volume configuration of anaerobic digesters, rated processing capacity configuration of catalytic reactors, capacity configuration of natural gas storage tanks, rated power configuration of generator sets, and target net present value for the entire life cycle corresponding to each sub-scheme.

5. A biogas system optimization design system, characterized in that, include: The acquisition module is configured to acquire renewable energy power generation data, initial biogas data, and power grid operation data, wherein the initial biogas data includes at least the initial flow rate and initial concentration of the initial biogas. The construction module is configured to build an optimal configuration model for a two-stage hydrogen conversion biogas-green hydrogen synergistic system based on the renewable energy power generation data, the initial biogas data, and the power grid operation data. The optimal configuration model aims to maximize the net present value over the entire life cycle, uses the capacity of key components in the biogas-green hydrogen synergistic system as the decision variable, and includes coupling constraints to characterize the competitive allocation relationship of hydrogen between primary biosynthesis and secondary chemical synthesis in the two-stage hydrogen conversion. The expression for the coupling constraints is: ， ， ， ， ， In the formula, For time period methane production flow rate from primary biosynthesis This is the initial biogas flow rate. The hydrogen conversion efficiency of primary biosynthesis. For time period Hydrogen flow rate allocated to primary biosynthesis, For time period methane production flow rate from secondary chemical synthesis The hydrogen conversion efficiency of secondary chemical synthesis. For time period Hydrogen flow rate allocated to secondary chemical synthesis, For time period Total hydrogen production flow rate This represents the concentration of methane produced by primary biosynthesis. The concentration of methane produced in the secondary chemical synthesis. The initial methane concentration, This represents the minimum methane concentration required for primary biosynthesis. This represents the minimum yield methane concentration for secondary chemical synthesis. The decomposition module is configured to decompose the optimized configuration model into an upper-level investment decision layer and a lower-level operational decision layer, resulting in a two-layer optimization model, including: Using the capacity decision variable of key components as the optimization variable of the upper-level investment decision layer, and taking the maximization of net present value over the entire life cycle as the upper-level objective, the expression of the upper-level objective is as follows: ， In the formula, Configure for a given capacity Net present value over the entire life cycle, For capacity configuration schemes, For the system's design lifespan, The discount rate is... For year indexing, The total investment cost of the system, Configure for a given capacity At that time, the optimal annual net cash flow is obtained by optimizing the lower-level operation; The operational variables for each time period are used as optimization variables for the lower-level operational decision-making layer. The goal of the lower-level layer is to maximize the annual operational revenue. The optimal annual operational revenue obtained from the lower-level optimization is fed back to the upper level to calculate the net present value of the entire life cycle under different capacity configurations. The operational variables include at least one of the following: electrolyzer input power, hydrogen flow rate allocated to primary biosynthesis, hydrogen flow rate allocated to secondary chemical synthesis, biomethane flow rate sold externally, biomethane flow rate used for power generation, storage status of hydrogen storage tanks, storage status of natural gas storage tanks, and grid-connected power output of generator sets. The solution module is configured to use a hybrid algorithm combining genetic algorithm and linear programming to solve the two-layer optimization model and obtain the optimal capacity configuration scheme of key components in the biogas-green hydrogen synergistic system. The key components include an electrolyzer, a hydrogen storage tank, an anaerobic digester, a catalytic reactor, a natural gas storage tank, and a generator set.

6. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1 to 4.

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