Environment-friendly methanol system capacity optimization configuration method

Abstract

The invention provides a green methanol system capacity optimization configuration method, and relates to the technical field of energy and chemical industry. The method comprises the following steps: S1, establishing a framework of a green methanol system; s2, determining an optimization period and time granularity; s3, establishing a to-be-optimized variable of the green methanol system; s4, establishing an optimization objective function, wherein the optimization objective function enables the annual net operation cost to be the lowest; s5, establishing a constraint condition equation, wherein the constraint condition equation comprises an electric power balance constraint, an upper and lower network electric quantity constraint, an upper and lower network power constraint, an electricity abandoning rate constraint, a unit product carbon emission constraint, a material balance constraint and a workshop section operation constraint; s6, configuring an optimization algorithm; s7, inputting capacity optimization configuration parameters of the green methanol system; and S8, solving an optimization problem, and outputting an optimization result of the green methanol system. The method is used for improving the energy utilization efficiency, reducing the cost and realizing high efficiency and stability of green methanol production, and has important practical significance and wide market prospects.

Description

Technical Field

[0001] The present application relates to the field of energy and chemical technology, and in particular to a method for optimizing the capacity configuration of a green methanol system. Background Art

[0002] As global climate change and environmental pollution become increasingly serious, the development and application of green methanol as a low-carbon, environmentally friendly energy carrier has received widespread attention. Large-scale green methanol production is an emerging technical field and is still in the early stages of engineering demonstration. There are several theoretically feasible technical routes, including biomass gasification, biomass gasification + green hydrogen supplementation, biomass power generation to capture carbon dioxide + green hydrogen, and direct air capture of carbon dioxide + green hydrogen. Among them, the biomass gasification + green hydrogen supplementation technical route is currently considered to be the lowest cost and most mature technical route, and is also the technical route selected for the large-scale green methanol demonstration project currently being promoted.

[0003] However, even the lowest-cost and most mature biomass gasification + green hydrogen supplementation technology route still faces many challenges in practical applications, especially in terms of system capacity configuration. There is a lack of effective optimization methods to adapt to the volatility of renewable energy, resulting in low energy utilization efficiency and high costs.

[0004] In recent years, although some technologies have been devoted to solving the problems in the green methanol preparation process, there are still deficiencies in the technology for green methanol system capacity configuration. For example, the existing technology fails to fully consider the impact of renewable energy generation volatility on the methanol production system, which makes it difficult for the system to achieve stable and efficient methanol production when facing the unstable output of renewable energy such as wind and solar energy; methanol synthesis is usually considered as a unified process section, without detailed consideration of the differences and matching of the operating characteristics of different processes such as air separation, gasification, transformation, purification, synthesis, and distillation, resulting in the failure to fully utilize the operating flexibility within the system; only the main raw materials and products are considered, and the material balance of intermediate products and by-products in the system process is not considered, resulting in a large deviation between the operating conditions considered in the system capacity configuration and the actual operating conditions; the many constraints on the system operation imposed by the upstream power grid and downstream product buyers are not carefully considered.

[0005] In summary, although green methanol has great development potential as a clean energy source, the existing technology still has obvious shortcomings in system capacity configuration and subsystem coordinated optimization.

[0006] Therefore, developing a green methanol system capacity optimization configuration method to improve energy utilization efficiency, reduce costs, and achieve efficient and stable green methanol production has important practical significance and broad market prospects. Summary of the invention

[0007] The purpose of the embodiments of the present application is to provide a method for optimizing the capacity configuration of a green methanol system, so as to improve energy utilization efficiency, reduce costs, and achieve efficient and stable green methanol production, which has important practical significance and broad market prospects.

[0008] In a first aspect, the present invention provides a method for optimizing capacity configuration of a green methanol system, the method comprising:

[0009] S1: Establish the architecture of green methanol system;

[0010] S2: Determine the optimization cycle and time granularity;

[0011] S3: Establish the variables to be optimized for the green methanol system;

[0012] S4: establishing an optimization objective function, wherein the optimization objective function is to minimize the annual net operating cost;

[0013] S5: Establish constraint equations, where the constraint equations include power balance constraints, grid-connected power constraints, grid-connected power constraints, power abandonment rate constraints, unit product carbon emission constraints, material balance constraints, and section operation constraints;

[0014] S6: configuration optimization algorithm;

[0015] S7: inputting capacity optimization configuration parameters of the green methanol system;

[0016] S8: Solve the optimization problem and output the optimization results of the green methanol system.

[0017] In an optional embodiment, in S3, the variables to be optimized include real variables and one-dimensional array variables. The real variables include the designed rated capacity of the components of the green methanol system and the initial power and initial capacity of the batteries, storage tanks, and intermediate tanks during the optimization period. The one-dimensional array variables include the input, output and state quantities of the components of the green methanol system at each time granularity.

[0018] In an optional implementation, in S4, annual net operating cost = dynamic investment converted on an annual basis + fixed annual operation and maintenance cost + electricity charge for electricity volume + capacity charge or demand charge + biomass purchase cost - annual sales revenue.

[0019] In an optional embodiment, in S5, the material balance constraints include balance constraints of input and output material flows, and the material flows include carbon, water, steam, hydrogen, oxygen, biomass, carbon monoxide, carbon dioxide, methane, crude methanol, refined methanol, and fusel oil.

[0020] In an optional implementation, in S5, the section operation constraints include the operating condition switching and transition process constraints of a typical chemical section with limited flexibility, and the expressions of the operating condition switching and transition process constraints of a typical chemical section with limited flexibility are as follows:

[0021]

[0022] Where: q(τ) is the input or output material flow representing the operating conditions of a typical chemical process section with limited flexibility at a time granularity τ; is the input or output material flow representing the kth quasi-steady-state operating condition of a typical chemical process section with limited flexibility; T trans It is the time constant of the transition process of the typical chemical process with limited flexibility; ΔT AS It is the working condition switching cycle of a typical chemical process section with limited flexibility; From 1 to The set of all integers between is the number of quasi-steady-state conditions in a typical chemical process section with limited flexibility during the optimization period; η and are the lowest and highest operating load rates of a typical chemical plant with limited flexibility; q r is the input or output material flow representing the nominal operating conditions of a typical chemical process section with limited flexibility; r and are the maximum load-down and load-up rates for a typical chemical plant with limited flexibility.

[0023] In an optional embodiment, S6 includes:

[0024] According to the objective function and constraint equations, select commercial solvers, open source solvers or self-written solving algorithms, determine the relative deviation of the optimization results allowed at the end of the optimization iteration, and determine the maximum allowed time for the optimization operation.

[0025] In an optional implementation, in S7, the capacity optimization configuration parameters include:

[0026] The capacity factor, depreciation period, unit fixed investment, unit annual operation and maintenance cost, and power abandonment rate limit of wind power and photovoltaic power within the optimization period;

[0027] The carbon emission factor of the power grid, monthly and annual on-grid and off-grid power limits, on-grid and off-grid power limits, on-grid and off-grid electricity prices with time granularity within the optimization cycle, unit capacity electricity price, and unit demand electricity price;

[0028] The maximum charge rate, maximum discharge rate, charge and discharge efficiency, self-discharge rate, SOC upper and lower limits, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of the battery;

[0029] The upper and lower limits of load rates, hydrogen-to-electricity conversion efficiency, depreciation period, unit fixed investment, and unit annual operation and maintenance costs of hydrogen production and fuel cells respectively;

[0030] The working condition switching cycle, transition process time constant, upper and lower limits of load rate, maximum single working condition adjustment range, unit power consumption, proportional relationship between input and output material flows, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of air separation, gasification, synthesis, and distillation respectively;

[0031] The upper and lower limits of the compression load rate, unit compression work, depreciation period, unit fixed investment, and unit annual operation and maintenance cost;

[0032] The upper and lower limits of storage capacity, charging and discharging losses, depreciation period, unit fixed investment, and unit annual operation and maintenance costs of storage tanks and intermediate tanks;

[0033] The purchase unit price of the raw material, i.e. biomass, at the time granularity within the optimization period; the unit selling price of the main product, i.e. refined methanol, at the time granularity within the optimization period and the unit product carbon emission limit; the unit selling price of the by-products, i.e. fusel oil, steam and methane, at the time granularity within the optimization period.

[0034] The embodiments of the present application are aimed at the planning and design stage of the green methanol system, especially the demand for optimal configuration of system capacity of the biomass gasification + green hydrogen supplementation technology route, which is currently the lowest cost, most mature technology, and closest to large-scale engineering promotion. A green methanol system capacity optimization configuration method is provided, which fully considers all major sections involved in the green methanol system and their capacities and operating parameters, the main energy and material balances involved, different types of grid interaction constraints, unit product carbon emission constraints, and the operating condition switching and transition process of typical chemical sections with limited flexibility, and obtains the capacity configuration and operating parameter production simulation with the optimal annual net operating cost of the green methanol system, which has certain practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flow chart of a green methanol system capacity optimization configuration method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0038] Figure 1The green methanol system capacity optimization configuration method (hereinafter referred to as: method) provided in the embodiment of the present application includes the following steps:

[0039] S1: Establish the architecture of green methanol system.

[0040] Specifically, the architecture of the green methanol system can be an existing architecture, and the embodiment of the present application mainly provides a capacity optimization configuration method applicable to the existing architecture. For example, the components of the architecture of the green methanol system include power balance nodes, wind power, photovoltaics, power grids, batteries, hydrogen production, fuel cells, air separation, gasification, conversion, purification, synthesis, distillation, synthesis gas compression, purified gas compression, hydrogen compression, oxygen storage tanks, hydrogen storage tanks, crude methanol intermediate tanks, refined methanol intermediate tanks, and fusel oil intermediate tanks.

[0041] Among them, there can be one or more power balancing nodes, wind power, photovoltaics, power grid, and batteries, and there is only one remaining component element. Each power balancing node is connected to at most one of wind power, photovoltaics, power grid, and battery.

[0042] The components of the green methanol system architecture include energy flows and material flows such as electricity, carbon, water, steam, hydrogen, oxygen, biomass, carbon monoxide, carbon dioxide, methane, crude methanol, refined methanol, and fusel oil. Among them, the oxygen output from hydrogen production and air separation, as well as the carbon dioxide output from purification, can be discharged.

[0043] S2: Determine the optimization cycle and time granularity.

[0044] Specifically, the optimization period may be any integer between 1 year and 20 years, and the time granularity may be 15 minutes, half an hour, or 1 hour.

[0045] S3: Establish the variables to be optimized for the green methanol system.

[0046] Specifically, the variables to be optimized in the green methanol system include real variables and one-dimensional array variables. Real variables include the design rated capacity of the components of the green methanol system and the initial power and initial capacity of the battery, storage tank, and intermediate tank during the optimization period. One-dimensional array variables include the input, output, and state quantities of the components of the green methanol system at each time granularity.

[0047] S4: Establish optimization objective function.

[0048] Specifically, the optimization objective function is to minimize the annual net operating cost, which is: annual net operating cost = dynamic investment converted on an annual basis + fixed annual operation and maintenance cost + electricity charge + capacity charge or demand charge + biomass purchase cost - annual sales revenue.

[0049] S5: Establish constraint equations.

[0050] Specifically, the constraint equations include power balance constraints, up and down grid power constraints, up and down grid power constraints, power abandonment rate constraints, unit product carbon emission constraints, material balance constraints, and work section operation constraints.

[0051] Among them, the material balance constraints include the balance constraints of input and output material flows, and the material flows include carbon, water, steam, hydrogen, oxygen, biomass, carbon monoxide, carbon dioxide, methane, crude methanol, refined methanol, and fusel oil.

[0052] Among them, the section operation constraints include the operating condition switching and transition process constraints of typical chemical sections with limited flexibility, such as air separation, gasification, synthesis, and distillation:

[0053]

[0054] Wherein: q(τ) is the input or output material flow representing the operating conditions of typical chemical processes with limited flexibility such as air separation, gasification, synthesis, and distillation at a time granularity τ. Preferably, the input or output material flow used to represent the operating conditions of air separation is the rate at which air separation outputs oxygen, or the input or output material flow used to represent the operating conditions of gasification is the rate at which gasification inputs biomass, or the input or output material flow used to represent the operating conditions of synthesis is the rate at which synthesis outputs crude methanol, or the input or output material flow used to represent the operating conditions of distillation is the rate at which distillation outputs refined methanol; is the input or output material flow representing the kth quasi-steady-state operating condition of a typical chemical process with limited flexibility, such as air separation, gasification, synthesis, and distillation; T trans It is the time constant of the transition process of the working condition switching of typical chemical industry sections with limited flexibility, such as air separation, gasification, synthesis, and distillation; ΔT AS It is the working condition switching cycle of typical chemical process sections with limited flexibility, such as air separation, gasification, synthesis, and distillation; From 1 to The set of all integers between is the number of quasi-steady-state operating conditions in typical chemical processes with limited flexibility, such as air separation, gasification, synthesis, and distillation, within the optimization period, which is equal to the optimization period divided by the operating condition switching period and rounded up; η and are the lowest and highest operating load rates of typical chemical processes with limited flexibility, such as air separation, gasification, synthesis, and distillation; q r is the input or output material flow representing the rated operating conditions of a typical chemical process section with limited flexibility, such as air separation, gasification, synthesis, distillation, etc.; r and They are the maximum load reduction and load increase rates of typical chemical processes with limited flexibility, such as air separation, gasification, synthesis, and distillation.

[0055] S6: Configuration optimization algorithm.

[0056] Specifically, configuring the optimization algorithm includes: selecting a commercial solver, an open source solver, or a self-written solving algorithm according to the objective function and constraint equations, determining the relative deviation of the optimization result allowed at the end of the optimization iteration, and determining the maximum allowed time for the optimization operation.

[0057] In this embodiment, the configuration optimization algorithm uses the commercial solver CPLEX for solving, determines that the relative deviation of the optimization result allowed at the end of the optimization iteration is 0.01%, and determines that the maximum allowed duration of the optimization operation is 12 hours.

[0058] S7: Input capacity optimization configuration parameters of the green methanol system.

[0059] Specifically, the capacity optimization configuration parameters of the green methanol system include: capacity factor, depreciation period, unit fixed investment, unit annual operation and maintenance cost, and power abandonment rate limit of wind power and photovoltaic power within the optimization period; carbon emission factor of the power grid, monthly and annual on-grid power limit, on-grid power limit, on-grid electricity price, unit capacity price, unit demand price within the optimization period; maximum charge rate, maximum discharge rate, charge and discharge efficiency, self-discharge rate, SOC upper and lower limits, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of the battery; upper and lower limits of load rate, hydrogen-to-electricity conversion efficiency, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of hydrogen production and fuel cells respectively; air separation, gasification, synthesis, The operating switching cycle of distillation, the time constant of transition process, the upper and lower limits of load rate, the maximum single operating condition adjustment range, unit power consumption, the proportional relationship between input and output material flows, depreciation period, unit fixed investment, and unit annual operation and maintenance cost; the upper and lower limits of load rate, unit compression work, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of compression; the upper and lower limits of storage capacity of storage tanks and intermediate tanks, charging and discharging losses, depreciation period, unit fixed investment, and unit annual operation and maintenance cost; the purchase unit price of raw materials, i.e. biomass, at the time granularity within the optimization period, the unit selling price of the main product, i.e. refined methanol, at the time granularity within the optimization period, and the carbon emission limit of the unit product; the unit selling price of by-products, i.e. fusel oil, steam, and methane, at the time granularity within the optimization period.

[0060] S8: Solve the optimization problem and output the optimization results of the green methanol system.

[0061] Specifically, after executing S7, the model automatically outputs the optimization results for the variables to be optimized determined in S3 according to the optimization objective function, constraint condition equations, capacity optimization configuration parameters and optimization algorithm.

[0062] The beneficial effects of the green methanol system capacity optimization configuration method provided in the embodiment of the present application include:

[0063] 1. It provides a set of methods for optimizing the capacity configuration of green methanol systems, especially the biomass gasification + green hydrogen supplementation technology route, which has the lowest cost, the most mature technology and is closest to large-scale engineering promotion. It can be used to support the planning and design of large-scale green methanol demonstration projects;

[0064] 2. This method takes into account the process sections, energy flows, materials, and operating constraints of the green methanol system in detail, including all the main process sections involved in the green methanol system, their capacities and operating parameters, the main energy and material balances involved, upstream grid interaction constraints, downstream product unit carbon emission constraints, and the operating condition switching and transition processes of typical chemical process sections with limited flexibility such as air separation, gasification, synthesis, and distillation. This method can well ensure that the output system capacity configuration fully realizes the coordinated, efficient, and flexible operation of various subsystems with different operating characteristics, thereby improving the economic efficiency of the project.

[0065] 3. This method can obtain the input and output energy flow and material flow operating parameters of each component element of the green methanol system in the selected optimization period and time granularity, which can be used for production simulation in the engineering design stage.

[0066] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0067] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0069] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0070] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0071] The above are only embodiments of the present application and are not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing the capacity configuration of a green methanol system, characterized in that: The method comprises: S1: Establish the architecture of green methanol system; S2: Determine the optimization cycle and time granularity; S3: Establish the variables to be optimized for the green methanol system; S4: Establishing an optimization objective function, wherein the optimization objective function is to minimize the annual net operating cost; S5: Establish constraint equations, wherein the constraint equations include power balance constraints, up and down grid power constraints, up and down grid power constraints, power abandonment rate constraints, unit product carbon emission constraints, material balance constraints, and section operation constraints; S6: configuration optimization algorithm; S7: inputting capacity optimization configuration parameters of the green methanol system; S8: Solve the optimization problem and output the optimization results of the green methanol system.

2. The green methanol system capacity optimization configuration method according to claim 1, characterized in that: In S3, the variables to be optimized include real variables and one-dimensional array variables. The real variables include the designed rated capacity of the components of the green methanol system and the initial power and initial capacity of the batteries, storage tanks, and intermediate tanks during the optimization period. The one-dimensional array variables include the input, output and state quantities of the components of the green methanol system at each time granularity.

3. The green methanol system capacity optimization configuration method according to claim 1, characterized in that: In S4, annual net operating cost = dynamic investment converted on an annual basis + fixed annual operation and maintenance cost + electricity charge for electricity volume + capacity charge or demand charge + biomass purchase cost - annual sales revenue.

4. The green methanol system capacity optimization configuration method according to claim 1, characterized in that: In S5, the material balance constraints include balance constraints of input and output material flows, and the material flows include carbon, water, steam, hydrogen, oxygen, biomass, carbon monoxide, carbon dioxide, methane, crude methanol, refined methanol, and fusel oil.

5. The green methanol system capacity optimization configuration method according to claim 1, characterized in that: In S5, the section operation constraints include the operating condition switching and transition process constraints of a typical chemical section with limited flexibility. The expressions of the operating condition switching and transition process constraints of a typical chemical section with limited flexibility are as follows: Where: q(τ) is the input or output material flow representing the operating conditions of a typical chemical process section with limited flexibility at a time granularity τ; is the input or output material flow representing the kth quasi-steady-state operating condition of a typical chemical process section with limited flexibility; T trans It is the time constant of the transition process of the typical chemical process with limited flexibility; ΔT AS It is the working condition switching cycle of a typical chemical process section with limited flexibility; From 1 to The set of all integers between is the number of quasi-steady-state conditions in a typical chemical process section with limited flexibility during the optimization period; η and are the lowest and highest operating load rates of a typical chemical plant with limited flexibility; q r is the input or output material flow representing the nominal operating conditions of a typical chemical process section with limited flexibility; r and are the maximum load-down and load-up rates for a typical chemical plant with limited flexibility.

6. The green methanol system capacity optimization configuration method according to claim 1, characterized in that S6 include: According to the objective function and constraint equations, select commercial solvers, open source solvers or self-written solving algorithms, determine the relative deviation of the optimization results allowed at the end of the optimization iteration, and determine the maximum allowed time for the optimization operation.

7. The green methanol system capacity optimization configuration method according to claim 1, characterized in that: In S7, the capacity optimization configuration parameters include: The capacity factor, depreciation period, unit fixed investment, unit annual operation and maintenance cost, and power abandonment rate limit of wind power and photovoltaic power within the optimization period; The carbon emission factor of the power grid, monthly and annual on-grid and off-grid power limits, on-grid and off-grid power limits, on-grid and off-grid electricity prices with time granularity within the optimization cycle, unit capacity electricity price, and unit demand electricity price; The maximum charge rate, maximum discharge rate, charge and discharge efficiency, self-discharge rate, SOC upper and lower limits, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of the battery; The upper and lower limits of load rates, hydrogen-to-electricity conversion efficiency, depreciation period, unit fixed investment, and unit annual operation and maintenance costs of hydrogen production and fuel cells respectively; The working condition switching cycle, transition process time constant, upper and lower limits of load rate, maximum single working condition adjustment range, unit power consumption, proportional relationship between input and output material flows, depreciation period, unit fixed investment, and unit annual operation and maintenance cost of air separation, gasification, synthesis, and distillation respectively; The upper and lower limits of the compression load rate, unit compression work, depreciation period, unit fixed investment, and unit annual operation and maintenance cost; The upper and lower limits of storage capacity, charging and discharging losses, depreciation period, unit fixed investment, and unit annual operation and maintenance costs of storage tanks and intermediate tanks; The purchase unit price of the raw material, i.e. biomass, at the time granularity within the optimization period; the unit selling price of the main product, i.e. refined methanol, at the time granularity within the optimization period and the unit product carbon emission limit; the unit selling price of the by-products, i.e. fusel oil, steam and methane, at the time granularity within the optimization period.

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