A configuration optimization method, device and equipment of a comprehensive hydrogen production system

Abstract

The application discloses a kind of configuration optimization method, device and system of integrated hydrogen production system, belong to hydrogen production technical field, the integrated hydrogen production system configuration optimization method provided by the present application, the two-stage configuration model of integrated hydrogen production system considering the uncertainty of photovoltaic output and biomass water content is established, the objective function is set as the minimization of investment cost and the operating cost of the worst scenario corresponding to the uncertainty set;Solve the above two-stage configuration model to obtain the optimized configuration scheme, thereby improving the robustness of configuration to ensure smooth hydrogen production.The scheme can ensure smooth hydrogen supply while meeting the system carbon emission requirements under photovoltaic, biomass access, reduce system component investment.In addition, the technical parameters involved therein relate to hydrogen production components and energy storage components, and the key parameters affecting the configuration results of system hydrogen production and energy storage can be subjected to sensitivity analysis.

Description

Technical Field

[0001] This invention belongs to the field of hydrogen production technology, and more specifically, relates to a method, apparatus and equipment for optimizing the configuration of an integrated hydrogen production system. Background Technology

[0002] Hydrogen energy is considered the most promising green secondary energy source. To ensure the safety and stability of the oil refining process, hydrogen cannot be stored, and hydrogen production and consumption must be balanced. Currently, hydrogen in the oil refining industry mainly comes from by-product hydrogen, coal, and natural gas, generating nearly 100 million tons of carbon emissions annually. Utilizing renewable energy for hydrogen production is an important means of reducing carbon emissions; however, renewable energy is highly volatile and uncertain, which contradicts the stable hydrogen demand of the oil and petrochemical industry. Therefore, how to rationally configure an integrated hydrogen production system to simultaneously meet stable hydrogen demand and carbon reduction requirements warrants in-depth research.

[0003] Integrating different hydrogen production technologies is an effective solution to meet stable hydrogen demand. Integrated hydrogen production systems that couple renewable energy and fossil fuel-based hydrogen production can leverage the advantages of both methods simultaneously. However, the uncertainty of renewable energy sources poses a significant challenge to a stable hydrogen supply, further highlighting the importance of considering this uncertainty in the research of optimal configuration models and algorithms for integrated hydrogen production systems.

[0004] Stochastic programming and robust optimization are currently the main methods for considering the characteristics of wind and solar power in decision-making processes. Compared to stochastic programming, robust optimization uses deterministic sets to describe the uncertainties of wind and solar power, and is not limited by the probability density distribution of wind and solar power. However, the impact of biomass uncertainty on the configuration optimization results is rarely considered in configuration optimization problems, while biomass uncertainty leads to poor robustness of integrated hydrogen production system configuration. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a configuration optimization method, apparatus, and equipment for an integrated hydrogen production system. The objective is to establish a two-stage configuration model for the integrated hydrogen production system that takes into account the uncertainties of photovoltaic output and biomass water content. The objective function is set as minimizing the investment cost and the worst-case operating cost corresponding to the uncertainty set. Solving the two-stage configuration model yields an optimized configuration scheme, thereby improving the robustness of the configuration to ensure stable hydrogen production. This solves the technical problem of poor configuration robustness in integrated hydrogen production systems caused by biomass uncertainty.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing the configuration of an integrated hydrogen production system is provided, comprising:

[0007] S1: Obtain the technical parameters of each component of the integrated hydrogen production system, including: a photovoltaic water electrolysis device, a biomass gasification device, a natural gas reforming device, an energy storage device, an oxygen storage device, and a gas storage device;

[0008] S2: Construct an uncertainty set considering the photovoltaic output corresponding to the photovoltaic water electrolysis device and the biomass moisture content corresponding to the biomass gasification device;

[0009] S3: Construct a two-stage configuration model for the integrated hydrogen production system using the technical parameters of each component. The objective function is the sum of the objective functions of the first stage and the second stage. The objective function of the first stage is to minimize the investment cost. The objective function of the second stage is to minimize the operating cost under the worst-case scenario corresponding to the uncertainty set.

[0010] S4: Solve the two-stage configuration model of the integrated hydrogen production system to obtain the investment strategy for the first stage and the worst-case scenario and its corresponding operational decision for the second stage; use the investment strategy and the worst-case scenario and its operational decision to optimize the configuration of the integrated hydrogen production system.

[0011] In one embodiment, the objective function of the first stage is: The investment costs for:

[0012] ;

[0013] in, Whether to install components X 0-1 variables, element X Unit capacity investment cost For the planned capacity of component X, X ∈{PV photovoltaic device, WE water electrolysis device, BG biomass gasification device, NGR natural gas reforming device, ES energy storage device, OS oxygen storage device, GS gas storage device}; This is the annual cost conversion factor. r For interest rates, , n This refers to the investment payback period.

[0014] In one embodiment, the investment constraints of the first stage include: configuration capacity constraints for each of the components.

[0015] In one embodiment, the objective function of the two-stage process is: Operating costs for: ;in, For maintenance costs, For raw material costs, To incur penalties, Let be the set of uncertainties.

[0016] In one embodiment, the operational constraints of the two-stage process include: material complementary utilization constraints, energy complementary utilization constraints, and system stable hydrogen production constraints.

[0017] The constraints on the complementary use of materials include:

[0018] The oxygen complementarity utilization relationship constraint is expressed as: ;

[0019] The constraint of complementary utilization of hydrocarbon fuels is expressed as follows: ;

[0020] in, This refers to the amount of oxygen entering the oxygen storage device; , These refer to the amount of oxygen directly supplied to the biomass gasification unit and the natural gas reforming unit after the hydrogen is generated by electricity; , These refer to the amount of oxygen supplied by the oxygen storage device to the biomass gasification device and the natural gas reforming device. The biomass gasification unit produces hydrocarbon fuel that is supplied to the natural gas reforming unit. Hydrocarbon fuel to enter the gas storage device; The hydrocarbon fuel supplied from the gas storage device to the natural gas reforming unit;

[0021] The energy complementary utilization constraints include: the heat provided by the biomass gasification unit is greater than the heat obtained by the natural gas reforming unit and the feed temperature is within a preset variation range;

[0022] The system's stable hydrogen production constraint is that the sum of the hydrogen production capacities of all the aforementioned components should meet the stable hydrogen demand. .

[0023] In one embodiment, the system's stable hydrogen production constraint is expressed as: ;in, Unit conversion factor (mol / s → Nm) 3 / s); The amount of hydrogen produced by the photovoltaic water electrolysis device; The hydrogen production capacity of the biomass gasification device; The amount of hydrogen produced by the natural gas reforming unit.

[0024] In one embodiment, the operational constraints of the second stage also include: hydrogen production constraints for photovoltaic water electrolysis, hydrogen production constraints for biomass gasification, hydrogen production constraints for natural gas reforming, operational constraints for the three types of hydrogen production devices, minimum start-up and shutdown time constraints for the three types of hydrogen production devices, operational constraints for energy storage devices, and carbon emission constraints.

[0025] In one embodiment, S2 includes:

[0026] Predicted value of photovoltaic output The error from the true value is controlled within the range Internally; the predicted value of biomass moisture content The error from the true value is controlled within the range Internal; utilizing photovoltaic adjustable robust parameters and biomass adjustable robust parameters Adjust the conservative level of robust planning; represent the uncertainty set as:

[0027] ;

[0028] in, and These are 0-1 variables representing the upper and lower limits of the random fluctuations in photovoltaic data; The photovoltaic output per unit capacity is a random variable; and These are 0-1 variables representing the upper and lower limits of the random fluctuation of biomass moisture content; The water content of biomass is a random variable.

[0029] In one embodiment, S4 includes:

[0030] The nonlinear constraints in the two-stage configuration model of the integrated hydrogen production system are piecewise linearized to transform the problem of solving the two-stage configuration model of the integrated hydrogen production system into a mixed integer linear programming problem.

[0031] The mixed-integer linear programming problem is solved by using a nested column and constraint generation algorithm to obtain the investment strategy for the first stage and the worst-case scenario and its operation decision for the second stage, thereby performing configuration optimization.

[0032] According to another aspect of the present invention, a configuration optimization device for an integrated hydrogen production system is provided, comprising:

[0033] The acquisition module is used to acquire the technical parameters of each component of the integrated hydrogen production system, including: a photovoltaic water electrolysis device, a biomass gasification device, a natural gas reforming device, an energy storage device, an oxygen storage device, and a gas storage device.

[0034] The construction module is used to construct an uncertainty set considering the photovoltaic output corresponding to the photovoltaic water electrolysis device and the biomass moisture content corresponding to the biomass gasification device;

[0035] The modeling module is used to construct a two-stage configuration model of the integrated hydrogen production system using the technical parameters of each component. Its objective function is the sum of the objective functions of the first stage and the second stage. The objective function of the first stage is to minimize the investment cost. The objective function of the second stage is to minimize the operating cost under the worst-case scenario corresponding to the uncertainty set.

[0036] The configuration module is used to solve the two-stage configuration model of the integrated hydrogen production system to obtain the investment strategy for the first stage and the worst-case scenario and its corresponding operational decision for the second stage; and to optimize the configuration of the integrated hydrogen production system using the investment strategy, the worst-case scenario and its operational decision.

[0037] According to another aspect of the present invention, a scheduling system for an integrated hydrogen production system is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the scheduling method.

[0038] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the scheduling method.

[0039] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0040] (1) The configuration optimization method for the integrated hydrogen production system provided by this invention establishes a two-stage configuration model of the integrated hydrogen production system that takes into account the uncertainty of photovoltaic output and biomass water content. The objective function is set as minimizing the investment cost and the worst-case operating cost corresponding to the uncertainty set. The optimized configuration scheme is obtained by solving the above two-stage configuration model, thereby improving the robustness of the configuration to ensure stable hydrogen production. This scheme can ensure a stable hydrogen supply while meeting the system's carbon emission requirements under photovoltaic and biomass access, and reduce the investment in system components. In addition, the technical parameters involved involve hydrogen production components and energy storage components, and sensitivity analysis can be performed on key parameters that affect the configuration results of system hydrogen production and energy storage.

[0041] (2) This plan will cover the investment costs of the first phase. The corresponding investment constraints include: configuration capacity constraints for each component. By reasonably setting the capacity range of system components, the economy and reliability of component configuration can be guaranteed.

[0042] (3) The operational constraints of the two-stage scheme include: material complementary utilization constraints, energy complementary utilization constraints and system stable hydrogen production constraints; fully explore the material and energy complementary utilization between photovoltaic water electrolysis, biomass and natural gas, and at the same time constrain the sum of hydrogen production of each component, so as to realize the complementary and balanced operation of photovoltaic water electrolysis device, biomass gasification device and natural gas reforming device according to hydrogen production demand, improve scheduling robustness and hydrogen production efficiency.

[0043] (4) The predicted value of photovoltaic power output used in this scheme The error from the true value is controlled within the range The predicted value of biomass moisture content The error from the true value is controlled within the range Photovoltaic adjustable robust parameters and biomass adjustable robust parameters Characterizing the uncertainty set simplifies the process of determining the uncertainty set corresponding to photovoltaic power output and biomass water content, thereby simplifying the solution process of the two-stage configuration model of the entire integrated hydrogen production system.

[0044] (5) In this scheme, the nonlinear constraints in the two-stage configuration model of the integrated hydrogen production system are piecewise linearized, which simplifies the solution process of the two-stage configuration model of the integrated hydrogen production system; the nested column and constraint generation algorithm is used to solve the mixed integer linear programming problem, which reduces the computational complexity and improves the efficiency of the entire scheduling process. Finally, a reasonable photovoltaic output scenario and biomass water content scenario can be found, which meets the planning configuration requirements under the most severe scenario (maximum system relaxation). Attached Figure Description

[0045] Figure 1 A flowchart illustrating the configuration optimization method for the integrated hydrogen production system provided by this invention.

[0046] Figure 2 The photovoltaic power generation prediction mode diagram provided by this invention.

[0047] Figure 3 The energy storage investment cost sensitivity analysis chart provided by this invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0049] The embodiments of the present invention are all described in the following scenarios, as detailed below:

[0050] The stable hydrogen demand is 1200 Nm 3 The carbon emission limit factor is 8.5 kgCO2 / kgH2, the curtailment penalty cost is $80 / MW, and the hydrogen production fluctuation penalty is $1000 / Nm³. 3 Robust adjustment coefficients for uncertainties in photovoltaic power generation and biomass water content. Г All values ​​are 6. The photovoltaic power generation prediction chart is attached. Figure 2 As shown, the maximum positive and negative prediction errors for photovoltaic power are both set to 0.15, and the predicted moisture content of biomass is 10%, with a fluctuation range of 5% to 15%. Table 1 shows the investment parameters for each piece of equipment. The maximum planned capacities for photovoltaic, water electrolysis, biomass gasification, and natural gas reforming units are assumed to be 12 MW, 12 MW, 2000 kg / h, and 500 Nm³, respectively. 3 / h, the maximum planned capacities for energy storage, oxygen storage, and gas storage are 20 MWh, 5000 Nm³, and 1000 Nm³, respectively. 3 300 Nm 3 The computing environment consisted of a Windows 10 system, an AMD Ryzen 7 PRO 4750U CPU with a clock speed of 1.7GHz, and 16GB of RAM. The proposed model was implemented using Matlab R2022b, and the commercial software Gurobi was used for solving.

[0051] Table 1 Annual Investment Costs of Equipment to be Planned

[0052]

[0053] In one embodiment, a configuration optimization method for an integrated hydrogen production system oriented towards stable hydrogen demand is provided, such as... Figure 1 As shown, it includes the following steps 1-4.

[0054] Step 1: Collect technical parameters of each component of the integrated hydrogen production system under study.

[0055] The integrated hydrogen production system includes components for water electrolysis, biomass gasification, natural gas reforming, and energy, oxygen, and gas storage.

[0056] The parameters of each component include:

[0057] 1) Maximum allowable construction capacity of water electrolysis unit Maximum climbing rate Unit capacity investment cost Operation and maintenance costs Minimum boot time Unit start-up cost Unit downtime costs .

[0058] 2) Maximum allowable construction capacity of biomass gasification plants Maximum climbing rate Unit capacity investment cost Operation and maintenance costs Minimum boot time Unit start-up cost Unit downtime costs Biomass raw material purchase cost .

[0059] 3) Maximum allowable construction capacity of natural gas reforming units Maximum climbing rate Unit capacity investment cost Operation and maintenance costs Minimum boot time Unit start-up cost Unit downtime costs Natural gas feedstock purchase cost .

[0060] 4) Investment cost per unit capacity of photovoltaic power Operation and maintenance costs Discard penalty coefficient Unstable hydrogen production penalty coefficient .

[0061] 5) Maximum allowable construction capacity of energy storage devices Energy storage device charging and discharging efficiency Energy storage device configuration cost coefficient Operating and maintenance costs of energy storage devices .

[0062] 6) Maximum allowable construction capacity of oxygen storage devices Oxygen storage device configuration cost coefficient Oxygen storage device operation and maintenance costs .

[0063] 7) Maximum allowable construction capacity of gas storage facilities Cost coefficient of gas storage device configuration Operating and maintenance costs of gas storage devices .

[0064] Step 2: Construct an uncertainty set considering the photovoltaic output of the photovoltaic water electrolysis device and the biomass moisture content of the biomass gasification device.

[0065] In one embodiment, the uncertainty set of photovoltaic and biomass water content

[0066] Assuming the photovoltaic output prediction error is within Within the interval, its predicted value is The water content of biomass is at a predicted value. interval Internal. Use adjustable robust parameters. and (from 0 to T The degree of conservatism in the proposed robust planning is adjusted (between). The uncertainty set of photovoltaic and biomass water content is described by the following equation:

[0067] .

[0068] Step 3: Establish a two-stage configuration model for the integrated hydrogen production system that takes into account the uncertainties of photovoltaic output and biomass moisture content.

[0069] The objective function of the two-stage configuration model of the integrated hydrogen production system is composed of the objective functions of the two stages, as shown in equation (1).

[0070] In one embodiment, the objective function of the first stage is the device investment cost. As shown in equation (2). The objective function for the second stage is the operating cost under the worst-case scenario. As shown in equation (4).

[0071] (1)

[0072] (2)

[0073] (3)

[0074] In one embodiment, the objective function for the second stage is the operating cost under the worst-case scenario. As shown in equation (4).

[0075] (4)

[0076] (5)

[0077] (6)

[0078] (7)

[0079] Will X The configuration equipment index includes photovoltaic, water electrolysis, biomass gasification, natural gas reforming, energy storage, oxygen storage, and gas storage. Equation (2) represents the annualized investment cost of the system. To install or not X 0-1 variables, Let X be the unit capacity investment cost of component X. Let X be the planned capacity of component X. Equation (3) is the annual cost conversion factor. r For interest rates, n The payback period is the investment recovery period. Equation (4) indicates that operating costs include maintenance costs, raw material costs, and penalty costs. The number of days in a year. Equation (5) represents the operation and maintenance cost. Let X be the unit operating and maintenance cost of component X. / , / , / These represent the start-up / shutdown costs of the water electrolysis unit, biomass gasification unit, and natural gas reforming unit, respectively. Equation (6) represents the system input raw material cost, including the purchase cost of biomass and natural gas raw materials. , These are the prices per unit of biomass and natural gas, respectively. N BG , N gas_ex These represent the purchase quantities of biomass and natural gas, respectively. Equation (7) represents the penalty costs, including the cost of curtailment of solar power and the cost of fluctuations in hydrogen production. and These are the prices per unit of abandoned light and the price for hydrogen fluctuation penalties. For the amount of light discarded, This refers to the fluctuation in hydrogen production.

[0080] In one embodiment, the first-phase investment constraints take into account device selection and capacity setting, limiting the capacity of the configured equipment.

[0081] (8)

[0082] (9)

[0083] In equations (8) and (9) c X_min , c X_max For equipment X Configure minimum and maximum capacity.

[0084] In one embodiment, the second-stage operational constraints include: material complementarity utilization constraints, energy complementarity utilization constraints, and system-stable hydrogen production constraints.

[0085] In one embodiment, the operational constraints of the second stage also include: hydrogen production constraints for photovoltaic water electrolysis, hydrogen production constraints for biomass gasification, hydrogen production constraints for natural gas reforming, operational constraints for the three types of hydrogen production devices, minimum start-up and shutdown time constraints for the three types of hydrogen production devices, operational constraints for energy storage devices, and carbon emission constraints.

[0086] 1) Constraints on hydrogen production from water electrolysis

[0087] (10)

[0088] (11)

[0089] (12)

[0090] (13)

[0091] (14)

[0092] Equation (10) represents the power balance constraint for hydrogen production via water electrolysis. The output power of the photovoltaic system is given by equation (11), which represents the electrical power consumed by the photovoltaic water electrolysis device. P WE With operating voltage U cell and operating current I cell The relationship. Equation (12) represents the operating voltage as the Nernst potential. E Ohmic overpotential U ohm Activation overpotential U act sum. It is a reversible voltage. R Let be the ideal gas constant. The operating temperature of the electrolytic cell. These are the charge transfer coefficients of the anode and cathode, respectively. F For Faraday coefficients, These are the anode / cathode current densities, respectively. N cell Number of electrolysis chambers h F The Faraday efficiency is given by equation (13), which expresses the operating current as the effective reaction area. A cell and current density i cell The product of . Equation (14) is the hydrogen production of the photovoltaic water electrolysis device. and oxygen consumption . k 1 represents the unit conversion factor (W→MW). k 2 is the unit conversion factor (mol / s → Nm) 3 / s).

[0093] 2) Constraints on hydrogen production from biomass gasification

[0094] Using wood as biomass, the global gasification reaction formula is as follows:

[0095] (15)

[0096] In the formula w It is the moisture content per mole of wood. It is the oxygen consumption per mole of wood. , , , , These coefficients correspond to the products hydrogen, carbon monoxide, carbon dioxide, water, and methane, respectively. If oxygen, a byproduct of water electrolysis for hydrogen production, is used as the gasifying agent, then nitrogen is not involved in the overall gasification reaction.

[0097] In one embodiment, the relationship between biomass feed, oxygen consumption, and gas production is calculated using a thermodynamic equilibrium model:

[0098] (16)

[0099] (17)

[0100] (18)

[0101] (19)

[0102] (20)

[0103] (twenty one)

[0104] Equation (16) indicates that a portion of the feedstock entering the biomass hydrogen production unit undergoes a reaction in an oxygen-rich environment. Some of them react in the air. Equations (17)-(20) respectively calculated the hydrogen production capacity of the biomass gasification unit. oxygen consumption Methane production Carbon dioxide production Equation (17) indicates that the hydrogen production capacity is represented by two parts: one part is the hydrogen production capacity when oxygen is used as the vaporizing agent, and the other part is the hydrogen production capacity when air is used as the vaporizing agent. Equations (18)-(20) are similar. Definition Y For the index of substances related to biomass hydrogen production, Equation (21) reflects the effect of biomass water content on hydrogen production and carbon dioxide coefficient. and The hydrogen production coefficients are respectively for oxygen and air as gasifying agents; and These are the oxygen consumption coefficients when oxygen and air are used as vaporizing agents, respectively. and These are the hydrocarbon fuel production coefficients when oxygen and air are used as gasifying agents, respectively. and The coefficients for carbon dioxide production when oxygen and air are used as vaporizing agents, respectively.

[0105] 3) Constraints on hydrogen production from natural gas reforming

[0106] The natural gas reforming unit employs chemical looping autothermal reforming for hydrogen production. Its advantage lies in its ability to achieve near-zero energy consumption in-situ separation of products during hydrogen production. Similar to biomass gasification, a reaction environment is constructed with pure oxygen as the primary component and air as a secondary component. The product composition is calculated based on reaction equilibrium, mass balance, and heat balance equations, yielding the output constraints for the natural gas reforming unit.

[0107] (twenty two)

[0108] (twenty three)

[0109] (twenty four)

[0110] (25)

[0111] (26)

[0112] Equation (22) indicates that a portion of the input natural gas undergoes a reaction in an oxygen-rich environment. Some of them react in the air. Equations (23)-(25) respectively calculated the hydrogen production capacity of the natural gas reforming unit. oxygen consumption and water consumption .definition Z For the index of substances related to hydrogen production from natural gas, equation (26) will include coefficients. Indicated as intake air temperature T NGR_in The function reflects the effect of intake temperature on oxygen consumption and gas production coefficient. Equation (23) indicates that the hydrogen production capacity consists of the hydrogen production capacity under oxygen environment and the hydrogen production capacity under air environment. Equations (24) and (25) are similar. and These are the hydrogen production coefficients from natural gas under oxygen and air environments, respectively. and These are the oxygen consumption coefficients under oxygen and air environments, respectively; and These are the water consumption coefficients under oxygen and air environments, respectively.

[0113] 4) Constraints on the complementary use of materials

[0114] In one embodiment, complementary utilization of materials includes the utilization of oxygen and hydrocarbon fuels.

[0115] (27)

[0116] (28)

[0117] Equation (27) represents the complementary utilization relationship of oxygen, corresponding to the composition relationship of the amount of oxygen generated by the photovoltaic water electrolysis device, the amount of oxygen consumed by the biomass gasification device and the natural gas reforming device, respectively; Equation (28) represents the complementary utilization relationship of hydrocarbon fuel, corresponding to the composition relationship of the amount of hydrocarbon fuel generated by the biomass gasification device and the amount consumed by the natural gas reforming device, respectively. This refers to the amount of oxygen entering the oxygen storage device; , These refer to the amount of oxygen directly supplied to the biomass gasification unit and the natural gas reforming unit after hydrogen is generated by electricity; , These refer to the amount of oxygen supplied by the oxygen storage device to the biomass gasification unit and the natural gas reforming unit. It is hydrocarbon fuel produced by biomass gasification units and supplied to natural gas reforming units; Hydrocarbon fuel to be stored in gas storage devices; Hydrocarbon fuel supplied from gas storage to natural gas reforming units.

[0118] 5) Constraints on complementary energy utilization

[0119] Energy complementarity occurs in the form of heat transfer. The high-temperature hydrocarbons and water vapor produced by biomass gasification can preheat the feed gas of the natural gas reforming unit, achieving a global thermal balance between the two. Equation (29) indicates that the heat provided by the biomass gasification unit must be greater than the heat obtained by the natural gas reforming unit. Equation (30) limits the range of feed temperature variation.

[0120] (29)

[0121] (30)

[0122] In the formula C Y and C Z They are substances Y The molar specific heat capacity of Z; N BG_Y and N NGR_Z Hydrogen production products from biomass gasification units Y Hydrogen production feedstock for natural gas reforming unit Z mass flow rate; and The outlet gas temperature of the biomass gasification unit and the inlet gas temperature of the natural gas reforming unit before heat exchange; and The temperatures are the outlet gas temperature of the biomass gasification unit and the inlet gas temperature of the natural gas reforming unit after heat exchange.

[0123] 6) Operational constraints of the three hydrogen production units

[0124] (31)

[0125] (32)

[0126] (33)

[0127] Equation (31) represents the upper and lower limits of the device's output; Equation (32) represents the output variation constraint of the device over time; Equation (33) represents the maximum power constraint at the device's start-up and shutdown times. M is a relatively large constant. In the formula... s t For the device in t The on / off status at all times; R This refers to the feed rate of the unit; This represents the maximum ramp vector of the device; R min and R max These are the maximum and minimum outputs of the device; S U and S D This represents the maximum start-up and maximum shutdown power of the device.

[0128] 7) Minimum start-up and shutdown time constraints for three types of hydrogen production units

[0129] (34)

[0130] In the formula This is the vector of the minimum power-on time that the device needs to maintain; This represents the minimum downtime vector that the unit needs to maintain. The minimum start-up and shutdown time constraints at the beginning of the simulation period need to be constrained in conjunction with the initial start-up and shutdown states of the unit. If the unit is at the initial time... t =0 indicates the device is powered on. The minimum start / stop constraints are as follows:

[0131] (35)

[0132] (36)

[0133] In the formula The duration during which the device has been running before the simulation period is given. Equation (36) is the minimum downtime constraint after degradation, ensuring that the preceding... The internal device cannot be restarted.

[0134] If the unit is at the initial moment t =0 indicates a stopped state, then the minimum start / stop constraints are as follows:

[0135] (37)

[0136] (38)

[0137] In the formula Equation (38) represents the duration the device has been shut down before the simulation period, and Equation (38) represents the minimum startup time constraint after degradation, ensuring that the preceding... The internal device cannot be restarted.

[0138] 8) Operational constraints of energy storage devices

[0139] (39)

[0140] In the formula, This represents the current capacity of the energy storage device. This represents the upper limit of the energy storage device's capacity. A 0-1 variable representing the state of an energy storage device; This represents the upper limit of the energy storage / discharge power of the energy storage device.

[0141] (40)

[0142] In the formula, This represents the current capacity of the oxygen storage device. This represents the upper limit of the oxygen storage device's capacity. A 0-1 variable representing the state of the oxygen storage device; This is the upper limit for the rate at which an oxygen storage device stores / releases oxygen.

[0143] (41)

[0144] In the formula, This represents the current capacity of the gas storage device. This is the upper limit of the gas storage device's capacity; A 0-1 variable representing the state of the gas storage device; This represents the upper limit of the gas storage / release rate of the gas storage device.

[0145] 9) Carbon emission constraints

[0146] To increase the supply of green hydrogen and reduce the amount of hydrogen produced from natural gas, carbon emission constraints are introduced.

[0147] (42)

[0148] The first term in the numerator of the formula represents the standard carbon emissions corresponding to the input raw materials, and the second term represents the total carbon emissions from the hydrogen production process products; the denominator represents the total hydrogen production. T This is the total scheduling period; , These are the densities of hydrogen and carbon dioxide, respectively. , These are the carbon emission factor for natural gas and the carbon emission limit.

[0149] 10) Constraints on stable hydrogen production in the system

[0150] The total hydrogen production capacity of all units should meet the stable hydrogen demand. .

[0151] (43)

[0152] Step 4: Transform the original problem into a mixed-integer linear programming problem using a linearization method, and solve the two-stage robust min max-min optimization problem using a nested column and constraint generation algorithm.

[0153] The existence of nonlinear constraints in the integrated hydrogen production system planning model leads to computational difficulties. Therefore, the nonlinear terms are linearized, including constraints on hydrogen production from biomass gasification, complementary energy utilization, and hydrogen production from water electrolysis, transforming the original nonlinear nonconvex optimization model into a mixed-integer linear programming model.

[0154] Piecewise linearization is performed on the terms involving the multiplication of continuous variables in the constraints of biomass gasification for hydrogen production and complementary energy utilization. For example:

[0155] (44)

[0156] In the formula, the symbols “+” and “-” represent the two quadratic terms in the first row of formula (44); δ λ,t and Δ λ,t These are the introduced continuous and binary auxiliary variables; ψ λ , a λ and b λ It is a constant parameter.

[0157] Piecewise linearization is applied to the constraints on hydrogen production from water electrolysis.

[0158] (45)

[0159] In the formula, L This represents the total number of segments; gn and h n These are the principal coefficients and constant coefficients of each segment; δ n,t These are the state variables corresponding to each segment; These are the inflection points of the independent variables in each segment.

[0160] The proposed two-stage configuration model for the integrated hydrogen production system can be described as follows:

[0161] (46)

[0162] In the formula, A , B , H , Z , h , W , X , Q , Y It is a constant matrix; I b It is a collection of 0-1 integer variables in a single phase; C It is a set of continuous variables in one stage; I u It is the set of two-stage 0-1 integer variables; P It is the set of two-stage continuous variables; Φ It is a set of uncertainties; α For random variables that reflect the uncertainty of water content in photovoltaic and biomass materials; The first stage constraint includes formula (8); The second stage of constraints includes (9)-(43).

[0163] The second-stage optimization problem of the obtained mixed-integer linear programming model contains 0-1 decision variables, and a nested column and constraint generation algorithm is used to solve this problem. The first stage determines the investment status and capacity of each investment device; the second stage optimizes the system's operational decisions under different scenarios and obtains the worst-case scenario. The two-stage robust optimization, using a max-min form objective function, can find a scenario with the highest photovoltaic output and biomass water content, where the system requires the maximum relaxation, which is the worst-case scenario. If the planning scheme in the first stage can meet the operational requirements under the worst-case scenario, then the planning scheme has good robustness.

[0164] The following three examples illustrate the rationality of the model presented in this paper.

[0165] Example 1: Deterministic programming of a comprehensive hydrogen production system considering the complementarity of matter and energy.

[0166] Example 2: Robust planning of a comprehensive hydrogen production system that considers only photovoltaic uncertainties and simultaneously takes into account the complementarity of matter and energy.

[0167] Example 3: Robust planning of an integrated hydrogen production system considering uncertainties in the water content of photovoltaic and biomass and the complementarity of matter and energy.

[0168] The component configuration scheme of the integrated hydrogen production system obtained by the solution is shown in Table 2.

[0169] Table 2 Programming Results for Each Example

[0170]

[0171] To demonstrate the superiority of the optimized configuration scheme presented in this paper, 1000 random real-time scenarios were generated using the Monte Carlo method to compare the configuration results of the four case scenarios. Table 3 summarizes the operating results of different scenarios under the three case scenarios. Fluctuations exceeding 2% of hydrogen production demand are considered as unstable hydrogen production. Case 1 and Case 2 have a large number of solar power curtailment and unstable hydrogen production scenarios. After considering photovoltaic uncertainties, the operating results of Case 2 are significantly improved compared to Case 1, but there are still more than 1 / 4 of the solar power curtailment scenarios and more than 1 / 4 of the unstable hydrogen production scenarios. Case 3 shows no solar power curtailment or unstable hydrogen production in any scenario.

[0172] Table 3. Statistics on the number of scenarios under different case scenarios

[0173]

[0174] To analyze the impact of investment cost parameters on planning results, optimization calculations were performed under different energy storage investment costs. (See attached diagram) Figure 3 As can be seen, with the increase in investment costs for energy storage, the total planning cost rises, while the capacity of energy storage configuration decreases.

[0175] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of an integrated hydrogen production system, characterized in that, include: S1: Obtain the technical parameters of each component of the integrated hydrogen production system, including: a photovoltaic water electrolysis device, a biomass gasification device, a natural gas reforming device, an energy storage device, an oxygen storage device, and a gas storage device; S2: Construct an uncertainty set considering the photovoltaic output corresponding to the photovoltaic water electrolysis device and the biomass moisture content corresponding to the biomass gasification device; S3: Construct a two-stage configuration model for the integrated hydrogen production system using the technical parameters of each component. The objective function is the sum of the objective functions of the first stage and the second stage. The objective function of the first stage is to minimize the investment cost. The objective function of the second stage is to minimize the operating cost under the worst-case scenario corresponding to the uncertainty set. S4: Solve the two-stage configuration model of the integrated hydrogen production system to obtain the investment strategy for the first stage and the worst-case scenario and its corresponding operational decision for the second stage; use the investment strategy and the worst-case scenario and its operational decision to optimize the configuration of the integrated hydrogen production system; S2 includes: predicting the photovoltaic output value. The error from the true value is controlled within the range Internally; the predicted value of biomass moisture content The error from the true value is controlled within the range Internal; utilizing photovoltaic adjustable robust parameters and biomass adjustable robust parameters Adjust the conservative level of robust planning; represent the uncertainty set as: ； in, and These are 0-1 variables representing the upper and lower limits of the random fluctuations in photovoltaic data; The photovoltaic output per unit capacity is a random variable; and These are 0-1 variables representing the upper and lower limits of the random fluctuation of biomass moisture content; The water content of biomass is a random variable.

2. The configuration optimization method for the integrated hydrogen production system as described in claim 1, characterized in that, The objective function for the first stage is: The investment costs for: ； in, Whether to install components X 0-1 variables, element X Unit capacity investment cost For the planned capacity of component X, X ∈{PV photovoltaic device, WE water electrolysis device, BG biomass gasification device, NGR natural gas reforming device, ES energy storage device, OS oxygen storage device, GS gas storage device}; This is the annual cost conversion factor. , r For interest rates, n This refers to the investment payback period.

3. The configuration optimization method for the integrated hydrogen production system as described in claim 2, characterized in that, The investment constraints for the first phase include: the configuration capacity constraints for each of the components.

4. The configuration optimization method for the integrated hydrogen production system as described in claim 1, characterized in that, The objective function for the second stage is: Operating costs for: ;in, For maintenance costs, For raw material costs, To incur penalties, Let be the set of uncertainties.

5. The configuration optimization method for the integrated hydrogen production system as described in claim 4, characterized in that, The operational constraints of the second stage include: material complementary utilization constraints, energy complementary utilization constraints, and system stable hydrogen production constraints. The constraints on the complementary use of materials include: The oxygen complementarity utilization relationship constraint is expressed as: ; The constraint of complementary utilization of hydrocarbon fuels is expressed as follows: ; Where the subscript t represents time, This refers to the amount of oxygen entering the oxygen storage device; , These refer to the amount of oxygen directly supplied to the biomass gasification unit and the natural gas reforming unit after the hydrogen is generated by electricity; , These refer to the amount of oxygen supplied by the oxygen storage device to the biomass gasification device and the natural gas reforming device. The biomass gasification unit produces hydrocarbon fuel that is supplied to the natural gas reforming unit. Hydrocarbon fuel to enter the gas storage device; The hydrocarbon fuel supplied from the gas storage device to the natural gas reforming unit; This indicates the amount of oxygen produced by the photovoltaic water electrolysis device; This indicates the oxygen consumption of the biomass gasification unit; This indicates the oxygen consumption of a natural gas reforming unit; This indicates the amount of hydrocarbon fuel produced by the biomass gasification unit. This indicates the hydrocarbon fuel consumption of a natural gas reforming unit; The energy complementary utilization constraints include: the heat provided by the biomass gasification unit is greater than the heat obtained by the natural gas reforming unit and the feed temperature is within a preset variation range; The system's stable hydrogen production constraint is that the sum of the hydrogen production capacities of all the aforementioned components should meet the stable hydrogen demand. .

6. The configuration optimization method for the integrated hydrogen production system as described in claim 5, characterized in that, The constraint on stable hydrogen production in the system is expressed as follows: ; in, Unit conversion factor , expressed as: mol / s → Nm 3 / s; The amount of hydrogen produced by the photovoltaic water electrolysis device; The hydrogen production capacity of the biomass gasification device; The amount of hydrogen produced by the natural gas reforming unit.

7. The configuration optimization method for the integrated hydrogen production system as described in claim 5, characterized in that, The operational constraints of the second phase also include: hydrogen production constraints for photovoltaic water electrolysis, hydrogen production constraints for biomass gasification, hydrogen production constraints for natural gas reforming, operational constraints for the three types of hydrogen production devices, minimum start-up and shutdown time constraints for the three types of hydrogen production devices, operational constraints for energy storage devices, and carbon emission constraints.

8. The configuration optimization method for the integrated hydrogen production system as described in claim 1, characterized in that, S4 includes: The nonlinear constraints in the two-stage configuration model of the integrated hydrogen production system are piecewise linearized to transform the problem of solving the two-stage configuration model of the integrated hydrogen production system into a mixed integer linear programming problem. The mixed-integer linear programming problem is solved by using a nested column and constraint generation algorithm to obtain the investment strategy for the first stage and the worst-case scenario and its operation decision for the second stage, thereby performing configuration optimization.

9. A configuration optimization device for an integrated hydrogen production system, characterized in that, The configuration optimization method for executing the integrated hydrogen production system of claim 1 includes: The acquisition module is used to acquire the technical parameters of each component of the integrated hydrogen production system, including: a photovoltaic water electrolysis device, a biomass gasification device, a natural gas reforming device, an energy storage device, an oxygen storage device, and a gas storage device. The construction module is used to construct an uncertainty set considering the photovoltaic output corresponding to the photovoltaic water electrolysis device and the biomass moisture content corresponding to the biomass gasification device; The modeling module is used to construct a two-stage configuration model of the integrated hydrogen production system using the technical parameters of each component. Its objective function is the sum of the objective functions of the first stage and the second stage. The objective function of the first stage is to minimize the investment cost. The objective function of the second stage is to minimize the operating cost under the worst-case scenario corresponding to the uncertainty set. The configuration module is used to solve the two-stage configuration model of the integrated hydrogen production system to obtain the investment strategy for the first stage and the worst-case scenario and its corresponding operational decision for the second stage; and to optimize the configuration of the integrated hydrogen production system using the investment strategy, the worst-case scenario and its operational decision.

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