Design operation method of renewable chemical production system capable of coping with supply side uncertainty and related device

Abstract

The invention provides a design operation method of a renewable chemical production system capable of coping with supply side uncertainty and a related device, and the method comprises the steps: obtaining the probability distribution of a representative random scene through a representative random scene generation method for representing the uncertainty of renewable energy; a system design operation optimization model based on probability distribution of a representative random scene and taking economical efficiency maximization as a target is solved, and a design scheme and an operation scheduling scheme of a renewable chemical production system under an uncertainty scene condition can be obtained under the condition that a renewable energy historical output curve is known. The invention provides an effective method for design optimization of a renewable chemical production system under the uncertain condition, and can provide suggestions for a system operation scheduling scheme.

Description

Technical Field

[0001] The present invention belongs to the field of chemical synthesis, and relates to a chemical production system that uses renewable energy sources such as wind power generation and photovoltaic power generation as energy sources, and specifically relates to a design and operation method and related devices for a renewable chemical production system that can cope with supply-side uncertainty. Background Art

[0002] At a time when reducing carbon emissions is the goal of green development, renewable energy, which is abundant in available resources and gradually mature in technology, is used as an alternative to traditional fossil energy in chemical, petrochemical and power supply. Renewable energy is used in wind power, photovoltaic power generation, hydropower generation, etc. The main direct utilization process is to connect it to the power grid to provide electricity for residents' lives and industrial needs. However, renewable energy has significant volatility and uncertainty. The high penetration rate of renewable energy has a negative impact on the stability and reliability of the connected power grid.

[0003] The integration of renewable energy with randomness and uncertainty into the power grid will aggravate the mismatch between the supply side and the demand side. In order to smooth out its volatility, a large-capacity energy storage unit, such as an energy storage battery, needs to be configured in the system. Its investment cost is high, which reduces the economic efficiency of renewable energy utilization. Power to X is an important way to convert and store renewable energy. Among them, the conversion of renewable energy into chemicals such as hydrogen, methanol and methane has been widely studied as an important energy storage path. Among them, methanol is not only an important chemical raw material and vehicle fuel, but also a high-quality energy storage medium with large market demand and broad prospects. There are various methanol synthesis pathways. Fossil raw materials such as coal, natural gas and shale gas and renewable resources such as biogas, biomass and hydrogenation of carbon dioxide can be used to synthesize methanol. Among them, technical routes such as power to liquid and power to gas have received widespread attention as chemical energy storage routes for the green economy.

[0004] Existing studies have clarified the important role and outstanding advantages of renewable energy methanol synthesis systems in absorbing renewable energy and reducing emissions from the methanol synthesis process. However, the research has ignored the impact of renewable energy uncertainty on system design and operation.

[0005] Renewable energy has characteristics such as randomness and intermittency. The randomness and volatility of renewable energy output will inevitably place higher demands on the design and operational flexibility of renewable energy synthesis methanol systems. Summary of the invention

[0006] In order to solve the above-mentioned problems of the prior art, the present invention provides a design and operation method and related devices for a renewable chemical production system that can cope with supply-side uncertainty. The method can be used to obtain a design scheme and an operation scheduling scheme for a renewable chemical production system under uncertain scenario conditions.

[0007] The present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty, comprising: Construct representative random scenarios of renewable energy output and calculate the probability distribution of each representative random scenario; The probability distribution of representative random scenarios and the operating parameters and economic parameters of the renewable chemical production system are input into the system design and operation optimization model, and the design variables and operating variables of the renewable chemical production system are obtained by solving the model; The renewable chemical production system includes a chemical synthesis unit, an energy storage unit, and a renewable energy power generation unit. The chemical synthesis unit includes multiple modular chemical production lines. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the chemical synthesis unit. The electricity from the energy storage battery is supplied to the chemical synthesis unit and the electrolyzer. The hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the chemical synthesis unit. The hydrogen stored in the hydrogen storage tank is supplied to the chemical synthesis unit. The system design and operation optimization model aims to minimize the annual total cost of the renewable chemical production system.

[0008] Preferably, the representative random scenarios of renewable energy output are constructed, and the probability distribution of each representative random scenario is calculated, and the specific steps are as follows: 1) Using the silhouette coefficient, Davies-Bouldin index and Calinski-Harabasz index as statistical error metrics, the number of representative random scenarios of renewable energy output is calculated; 2) Based on the number of representative random scenarios, the K-means clustering method is used to generate the initial representative random scenarios of renewable energy output and the corresponding typical day sets; 3) Using the mean and standard deviation as statistical indicators, perform secondary clustering on the initial representative random scenes to obtain the final representative random scenes and the corresponding typical day sets; 4) According to the proportion of typical days in each representative random scene, the probability of occurrence of each representative random scene is determined, and the probability distribution of each representative random scene is obtained.

[0009] Preferably, the objective function of the system design operation optimization model is:

[0010] In the formula, represents the total annual cost of the renewable chemical production system, CRF represents the investment return factor, represents the fixed investment cost of renewable chemical production systems, Represents the renewable chemical production system in a representative random scenario s The operating costs of Represents a representative random scene s The probability distribution of S is the set of typical days corresponding to the representative random scene.

[0011] Furthermore, the fixed investment cost of the renewable chemical production system is:

[0012]

[0013]

[0014]

[0015] In the formula, Indicates production equipment i Fixed investment costs; Is with production equipment i The capacity of the corresponding benchmark equipment; It is a production equipment in the chemical production line i Rated capacity; It is a production equipment in the chemical production line i Fixed investment costs, Is with production equipment i The corresponding benchmark equipment investment cost, Indicates production equipment in chemical production lines i The capacity scale economy index, Represents the total number of production equipment in the chemical synthesis unit, Indicates production equipment in chemical production lines i The standardized batch economic index, I, is the set of production equipment in a chemical production line; represents the fixed investment cost of the energy storage unit, represents the unit capacity investment cost of energy storage device j, represents the installed capacity of energy storage device j, where J is the collection of electrolyzers, energy storage batteries, and hydrogen storage tanks; represents the initial hydrogen purchase cost, Indicates the purchase price of hydrogen per unit mass, Indicates the initial hydrogen reserve required to be purchased for the hydrogen storage tank; The operating cost of the renewable chemical production system under the representative random scenario s is:

[0016] In the formula, Indicates the electricity price, Indicates the operation time. represents the production equipment at time t of the representative random scenario s i Virtual module capacity, Indicates production equipment in chemical production lines i The power or capacity actually required for operation at time t of a representative random scenario s; and Respectively represent production equipment i The rate of change of apparent efficiency when the efficiency is less than or greater than the rated state.

[0017] Preferably, the constraints of the system design and operation optimization model include: total energy balance constraints of the power generation of renewable energy power generation units, energy balance constraints of energy storage batteries, mass balance constraints and energy balance constraints of electrolytic cells, energy balance constraints of chemical synthesis units, raw material constraints of chemical production lines, material balance constraints, energy consumption constraints, state constraints and operation constraints of each production equipment in the chemical production line, and operation constraints of each storage device in the energy storage unit.

[0018] Furthermore, the chemical synthesis unit is a methanol synthesis unit, the chemical production line is a methanol production line, and the energy balance constraint of the chemical synthesis unit is:

[0019] In the formula, Represents the electric energy consumed by all production equipment in the methanol synthesis unit at time t; represents the number of methanol production lines running in the methanol synthesis unit at time t, represents the total energy consumption of a single methanol production line, Indicates the production equipment in the methanol production line i Energy consumption; The raw material constraints for the chemical production line are:

[0020] In the formula, represents the total flow rate of hydrogen required for a single methanol production line at the representative random scenario s at time t, represents the number of methanol production lines running in the methanol synthesis unit at the representative random scenario s at time t, represents the mass flow rate of stream k entering a single methanol production line at time t in a representative random scenario s; represents the CO in stream k at time t in a representative random scenario s 2 Mass flow rate into a single methanol line, H in stream k 2 The quality score, is the CO in stream k 2 quality score.

[0021] Furthermore, the feasible region boundary tightening algorithm is used to linearize the two bilinear constraints of the energy balance constraint of the chemical synthesis unit and the raw material constraint of the chemical production line, including: Step 0: Select a group , and The initial boundary value of the segment number P is in the range of [0, 10], and the convergence tolerance standard is specified. and ; p =1,2,…,P; Represents each segment p Hydrogen flow rate in methanol production line, where the subscript c represents hydrogen; Step 1: Initialize variables , and And solve it. If the solution is not feasible due to poor initialization, continue to change the initial value of the variable to solve until a feasible solution lower bound is obtained; Step 2: After the above step 1 is feasible, calculate the bilinear constraint equation Tolerances on both sides, if the tolerances do not meet the convergence tolerance criteria , then change the segment number P value and the variable and The upper and lower boundaries of the tolerance are determined and the next iteration is continued; if the tolerance meets the convergence tolerance criteria , it remains unchanged; Step 3: Calculate the bilinear constraint equation Tolerances on both sides, if the tolerances do not meet the convergence tolerance criteria , then change the variable The upper and lower boundaries of the tolerance are determined and the next iteration is continued; if the tolerance meets the convergence tolerance criteria , the iteration ends and a feasible solution is output.

[0022] In a second aspect, the present invention provides a design and operation system for a renewable chemical production system that can cope with supply-side uncertainty, comprising: A representative random scenario construction module is used to construct representative random scenarios of renewable energy output and calculate the probability distribution of each representative random scenario; A calculation module, for inputting the probability distribution of representative random scenarios and the operating parameters and economic parameters of the renewable chemical production system into the system design and operation optimization model, solving the model, and obtaining the design variables and operating variables of the renewable chemical production system; The renewable chemical production system includes a chemical synthesis unit, an energy storage unit, and a renewable energy power generation unit. The chemical synthesis unit includes multiple modular chemical production lines. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the chemical synthesis unit. The electricity from the energy storage battery is supplied to the chemical synthesis unit and the electrolyzer. The hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the chemical synthesis unit. The hydrogen stored in the hydrogen storage tank is supplied to the chemical synthesis unit. The system design and operation optimization model aims to minimize the annual total cost of the renewable chemical production system.

[0023] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for designing and operating a renewable chemical production system that can cope with supply-side uncertainties when executing the computer program.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the design and operation method of a renewable chemical production system that can cope with supply-side uncertainty as described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for designing and operating a system in the case of fluctuations on the supply side for a renewable chemical production system. The probability distribution of a representative random scenario is obtained by a representative random scenario generation method that represents the uncertainty of renewable energy. A system design and operation optimization model based on the probability distribution of the representative random scenario and with the goal of maximizing economic efficiency is solved. When the historical output curve of renewable energy is known, the design scheme and operation scheduling scheme of the renewable chemical production system under the condition of uncertainty scenario can be obtained. The present invention provides an effective method for design optimization of a renewable chemical production system under uncertainty conditions, and can provide suggestions for the system operation scheduling scheme.

[0026] Furthermore, the feasible region boundary tightening algorithm is used to linearize the two bilinear term constraints in the system design operation optimization model, so that the above model can be successfully solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 This is a conceptual diagram of the renewable energy methanol production system of the present invention; Figure 2 This is a conceptual diagram of the modular methanol production line of the present invention; Figure 3 is a flow chart of the method of the present invention; Figure 4 is a representative random scene obtained in the simulation of the present invention; Figure 5 is the distribution of system photovoltaic power in the representative random scenario S5 in the simulation of the present invention; Figure 6 The power input and hydrogen output operation of the electrolyzer in the simulation of the present invention; Figure 7 Simulating the storage capacity and power input and output operations of the energy storage battery in the present invention; Figure 8 The hydrogen storage capacity and hydrogen input and output operations of the hydrogen storage tank in the simulation of the present invention; Fig. 9 This is the single group capacity and quantity scheduling of the modular methanol production line in the simulation of the present invention. DETAILED DESCRIPTION

[0029] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0030] It should be noted that the process equipment or devices not specifically specified in the following embodiments are all conventional equipment or devices in the art.

[0031] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. Moreover, unless otherwise specified, the numbering of each method step is only a convenient tool for identifying each method step, and is not intended to limit the order of arrangement of each method step or to define the scope of the present invention. Changes or adjustments in their relative relationships should also be regarded as the scope of the present invention without substantially changing the technical content.

[0032] In the embodiment of the present invention, the renewable chemical production system is specifically a methanol synthesis system, the chemical synthesis unit is a methanol synthesis unit, and the chemical production line is a methanol production line. The methanol synthesis system includes a methanol synthesis unit, an energy storage unit, and a renewable energy power generation unit, and the methanol synthesis unit includes a plurality of modular methanol production lines. The production equipment in the methanol production line includes six types: a compressor, a heater, a cooler, a separator, a reactor, and a distillation tower. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the methanol synthesis unit, the electric energy of the energy storage battery is supplied to the methanol synthesis unit and the electrolyzer, the hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the methanol synthesis unit, and the hydrogen stored in the hydrogen storage tank is supplied to the methanol synthesis unit.

[0033] The heater includes a first heater, a second heater and a third heater, and the compressor includes a first compressor and a second compressor; the input of the first compressor is stream 1, and the output of the first compressor is stream 2; the input of the first heater is stream 2, and the output is stream 3; the input of the reactor is stream 4, and the output is stream 5; the input of the cooler is stream 5, and the output is stream 6; the input of the separator is stream 6, the bottom output is stream 11, and the top output is stream 7; the input of the second compressor is stream 8, and the output is stream 9; the input of the second heater is stream 9, and the output is stream 10; stream 10 and stream 3 merge into stream 4; the input of the third heater is stream 11, and the output is stream 12; the input of the distillation tower is stream 12, the top output is stream 13, and the bottom output is stream 14.

[0034] For renewable chemical production systems, the present invention comprehensively considers the distribution characteristics of uncertainty scenarios on the supply side, and provides an optimization method for the design and operation of renewable chemical production systems under uncertainty through a random multi-scenario optimization method, providing guidance for the equipment design and process operation of the system.

[0035] This method assumes the following: 1) When analyzing the economics of the system, only the costs of major equipment such as electrolyzers, batteries, hydrogen storage tanks, and modular methanol production lines are considered; 2) Only the output of renewable energy fluctuates, while other equipment operating parameters remain unchanged.

[0036] Based on the above assumptions, a modular design and operation optimization method for renewable chemical production systems under uncertainty is proposed. The structure of the synthesis system is as follows: Figure 1 As shown, the process of producing methanol by hydrogenation of carbon dioxide used in the present invention is as follows Figure 2 shown.

[0037] The method flow is as follows Figure 3 As shown, it includes the following three steps: (1) Construct a system design and operation optimization model and determine the system design and operation optimization method; (2) Construct representative random scenarios of renewable energy and calculate the probability of distribution of uncertain scenarios; (3) Construct a boundary tightening algorithm to linearize the bilinear terms in the model.

[0038] If the above assumptions are not true, it will affect the generation of representative random scenarios of renewable energy and the optimal design of the system in the present invention, but the technical solution of the present invention is still applicable. The advantages and novelty of the method will be demonstrated through the following detailed description and related drawings.

[0039] 1. Build a system design and operation optimization model and determine the system design and operation optimization method Given representative random scenarios of renewable energy output, operating parameters and economic parameters of major equipment in renewable chemical production systems, a mathematical programming model is established with the goal of minimizing the average annual total investment cost of renewable chemical production systems to determine the economically optimal system configuration design. The operating parameters include the upper and lower limits of the power and capacity of the electrolyzer, energy storage battery and hydrogen storage tank, and the pressure, temperature and upper and lower limit factors of the operating window of the methanol synthesis unit; the economic parameters include the unit investment cost of the energy storage unit, the benchmark investment cost of the methanol synthesis unit and the economic index.

[0040] This can be achieved in three steps: 1) Determine the optimization goal The goal is to minimize the expected total annual cost of the renewable chemical production system, that is, the sum of the fixed investment cost of the system and the expected operating costs of each scenario is minimized, as shown in the following formula: (1) In the formula, represents the total annual cost of the renewable chemical production system, CRF represents the investment return factor, represents the fixed investment cost of renewable chemical production systems, Represents the renewable chemical production system in a representative random scenario s The operating costs of Represents a representative random scene s The probability distribution of S is the set of typical days corresponding to the representative random scene.

[0041] CRF can be expressed as the interest rate r and production equipment life f Function: (2) The fixed investment costs of the renewable chemical production system are mainly the fixed investment costs of the methanol synthesis unit and the energy storage unit and the initial hydrogen purchase cost, namely: (3) Production equipment in methanol synthesis unit i The fixed investment cost is calculated as follows: (4) In the formula, Is with production equipment i The capacity of the corresponding benchmark equipment; It is a production equipment in the methanol production line i Rated capacity; It is a production equipment in the methanol production line i Fixed investment costs, Is with production equipment i The corresponding benchmark equipment investment cost, Indicates production equipment i The capacity scale economy index, Represents the production equipment in the methanol synthesis unit i The total number of Indicates production equipment i The normalized batch economic index, I, is the aggregate of compressors, heaters, coolers, separators, reactors, and distillation columns.

[0042] The fixed investment cost of the energy storage unit can be expressed as the unit capacity investment cost of the energy storage equipment With its installed capacity The product of, that is: (5) Among them, j is the energy storage device, J is the collection of electrolyzers, energy storage batteries, and hydrogen storage tanks, and j∈J.

[0043] The initial hydrogen acquisition cost calculation is as follows: (6) In the formula, Indicates the purchase price of hydrogen per unit mass, Indicates the initial hydrogen reserves required to be purchased for the hydrogen storage tank.

[0044] The operating costs of the renewable chemical production system under a representative stochastic scenario s are calculated as follows: (7) In the formula, Indicates the electricity price, For operation time. is the virtual module capacity of production equipment i at time t in the representative random scenario s, which is used to penalize the state of the methanol production line that deviates from the operating constraints. It is calculated as follows: (8) In the formula, It represents the power or capacity actually required for the operation of production equipment i in the methanol production line at the representative random scenario s at the tth moment. and It indicates the rate of change of apparent efficiency of production equipment i when it is less than or greater than the rated state.

[0045] 2) Determine the constraints The renewable energy power generation unit supplies the electric energy required for the operation of the production equipment of each methanol production line in the electrolyzer and the methanol synthesis unit. If the renewable energy power generation unit has sufficient power generation, it can also choose to charge or discard the energy storage battery. The total energy balance of the power generation of the renewable energy power generation unit can be expressed as: (9) In the formula, represents the power generation of renewable energy generation units at the tth moment in the representative random scenario s, represents the amount of electricity supplied to the electrolyzer by the renewable energy generation unit at the tth moment in the representative random scenario s, represents the amount of electricity supplied by the renewable energy power generation unit to the methanol synthesis unit at the tth moment in the representative random scenario s, represents the amount of electricity supplied to the energy storage battery by the renewable energy generation unit at the tth moment in the representative random scenario s, It represents the amount of electricity that is abandoned or used for other purposes by the renewable energy generation unit at the tth moment in the representative random scenario s.

[0046] The energy storage battery mainly supplies power to the electrolyzer and methanol synthesis unit, namely: (10) In the formula, represents the total transmission power of the energy storage battery, Indicates the power transmitted from the energy storage battery to the electrolyzer, Represents the power transmitted by the energy storage battery to the production equipment in the methanol synthesis unit.

[0047] The energy required for the production equipment in the methanol synthesis unit can be provided by renewable energy generation units and energy storage batteries, namely: (11) In the formula, Represents the electric energy consumed by all production equipment in the methanol synthesis unit at time t; represents the number of methanol production lines running in the methanol synthesis unit at time t, represents the total energy consumption of a single methanol production line, Indicates the production equipment in the methanol production line i Energy consumption, i ∈I.

[0048] Hydrogen flow rate from electrolyzer It can be calculated as follows: (12) In the formula, It represents the electrolysis efficiency of the electrolytic cell. It indicates the amount of electricity consumed by the electrolyzer to produce 1 mol of hydrogen.

[0049] In order to determine the start and stop status of the electrolytic cell, the following constraints can be added: (13) In the formula, is the maximum power value of the electrolytic cell, Indicates the open state of the electrolytic cell.

[0050] The upper and lower limits of the electrolyzer input power can be defined as follows: (14) In the formula, Indicates the rated power of the electrolyzer, and They respectively represent the lower and upper limits of the power input to the electrolyzer during operation.

[0051] The hydrogen produced by the electrolyzer can be directly used to synthesize methanol or stored in a hydrogen storage tank. At the same time, the hydrogen required by the methanol synthesis system can be directly provided by the electrolyzer or by a hydrogen storage tank. The hydrogen flow rate balance is: (15) In the formula, and They represent the hydrogen flow rate from the electrolyzer to the methanol synthesis unit and the hydrogen storage tank at the tth moment in the representative random scenario s, represents the total flow rate of hydrogen required by the methanol synthesis unit at the representative random scenario s at time t, represents the hydrogen flow rate delivered from the hydrogen storage tank to the methanol synthesis unit at time t in the representative random scenario s.

[0052] After hydrogen enters the methanol synthesis unit, it is evenly distributed among the methanol production lines in operation. 2 The flow rate can be configured according to the flow rate of hydrogen. At any time, the total flow rate of the raw materials in the methanol production line, the hydrogen flow rate of a single methanol production line, and the required CO 2 The mass flow rate can be calculated as follows: (16) In the formula, represents the total flow rate of hydrogen required for a single methanol production line at the representative random scenario s at time t, represents the number of methanol production lines running in the methanol synthesis unit at the representative random scenario s at time t, represents the mass flow rate of stream k entering a single methanol production line at time t in a representative random scenario s; represents the CO in stream k at time t in a representative random scenario s 2 Mass flow rate into a single methanol line, H in stream k 2 The quality score, is the CO in stream k 2 quality score.

[0053] (1) Material balance constraints of each production equipment in the methanol production line In the production process, all production equipment in the methanol synthesis unit complies with the conservation of materials. For compressors, heaters, and coolers, only state changes occur when the stream flows through such production equipment. The material balance relationship of each component can be expressed as: (17) in, represents the component in stream k at time t in the representative random scenario s c The mass flow rate of H 2 , CO 2 , CO, H 2 O and CH 3 A collection of OH, c ∈C.

[0054] The streams in the reactor, separator and distillation column not only undergo state changes but also composition changes. The material balance relationships of their components can be described as follows: In the methanol synthesis unit, the inlet and outlet flow rates of the reactor, separator, distillation tower and other production equipment change as follows: (18) in, represents the component in stream k at time t in the representative random scenario s c The mass flow rate of H 2 , CO 2 , CO, H 2 O and CH 3 A collection of OH, c ∈C; C\CH 3 OH means except CH 3 Components other than OH.

[0055] The separator in the methanol synthesis unit is mainly used to separate the gas and liquid of the crude product. It is assumed that all the methanol in the distillation tower flows out from the top of the distillation tower, and other impurities flow out from the bottom.

[0056] In order to accurately describe the relationship between the mass flow rate of each component in the stream and the mass flow rate of the raw material, the present invention uses Aspen Plus to simulate the methanol synthesis process. The physical property method selected in the simulation is RK-SOAVE, the reactor selects the RPLUG module, and the reaction kinetics data comes from the work of Bussche et al. The simulation results under different feed mass flow rates are fitted to obtain the linear relationship between the mass flow rate of each component in the circulating stream of the methanol synthesis process and the feed amount, as shown in formula (19).

[0057] (19) In the formula, represents the component in stream k at time t in the representative random scenario s c The mass flow rate, C H 2 , CO 2 , CO, H 2 O and CH 3 A collection of OH, c ∈C; represents the mass flow rate of stream k entering a single methanol production line at time t in the representative random scenario s, a and b are the linear fit coefficients, which vary depending on the stream and component.

[0058] Production equipment at time t in a representative random scenario s i Required power or capacity It is related to the flow rate of each production equipment in the methanol production line and is calculated as follows: (20) In the formula, Indicates components c The molar mass of Indicates production equipment i The inlet temperature, Indicates production equipment i The outlet temperature, Indicates production equipment i The inlet pressure, Indicates production equipment i Outlet pressure; represents the heat capacity ratio of the mixed feed entering the compressor, take ; Indicates the efficiency of the motor in the compressor, represents the efficiency of the compressor, is the ideal gas constant, Indicates components c The specific heat capacity at constant pressure, Indicates components c The latent heat of vaporization. compressor means compressor, heater means heater, reactor means reactor, cooler means cooler, flash means separator, and distillation means distillation tower.

[0059] (2) Energy consumption constraints of each production equipment in the methanol production line Compressor energy consumption: (twenty one) Energy consumption of heaters and coolers: (twenty two) (twenty three) Energy consumption of the reactor: (twenty four) Energy consumption of distillation tower: (25) In the formula, Represents the representative random scenario s production equipment at time t i The apparent efficiency in operation, Indicates the inlet and outlet temperature difference of cooling water, Indicates the unit power driving the cooling water circulation; Indicates the efficiency of the electric heating boiler in the distillation tower, Indicates the components at the reactor inlet c The reaction enthalpy change, Represents the components at the reactor outlet c The reaction enthalpy change, represents the standard heat of reaction, It represents the reflux ratio of the condenser in the distillation tower. It represents the reflux ratio of the reboiler in the distillation tower, represents the efficiency of the condenser in the distillation tower, Indicates the efficiency of the reboiler in the distillation tower.

[0060] (3) Status constraints of each production equipment in the methanol production line In a methanol production line, the temperature of the material changes as it passes through all the production equipment; the pressure of the material remains unchanged after passing through the heater, cooler, and reactor. Therefore, the state constraints of the methanol production line can be described as: (26) (27) (28) In the formula, Indicates production equipment i The inlet and outlet pressures of the compressor and distillation tower are different, and the operating pressure is the outlet pressure; the inlet and outlet pressures of other production equipment remain unchanged, and the operating pressure is the same as the inlet pressure; Indicates production equipment i The operating temperature of the equipment produced here i It is a heater, cooler, reactor or distillation column.

[0061] (4) Operational constraints of each production equipment in the methanol production line and each storage equipment in the energy storage unit The production equipment in the methanol production line needs to operate within a given operating window, that is, the range within which the production equipment deviates from the rated state.

[0062] (29) In the formula, and It is the upper and lower limit factors by which production equipment can deviate from the rated power or capacity.

[0063] The present invention uses apparent efficiency to represent the relative operating efficiency of a production equipment when the operating state of the production equipment deviates from the rated state.

[0064] (30) In the formula, Represents the representative random scenario s production equipment at time t i The apparent efficiency is the equipment efficiency relative to the rated state when the actual power or flow of the production equipment deviates from its rated power or flow. In existing studies, when the operating window is small, the efficiency can be considered to be approximately 1.

[0065] The energy storage battery is charged by the renewable energy generation unit on the power generation side and discharged to the electrolyzer and methanol synthesis unit. The input-output power change and charge-discharge operation constraints of the energy storage battery at any time are as follows: (31) In the formula, represents the instantaneous power of the energy storage battery at the tth moment in the representative random scenario s, represents the initial capacity of the energy storage battery in the representative random scenario s, represents the amount of electricity supplied to the energy storage battery by the renewable energy generation unit at the tth moment in the representative random scenario s, represents the total transmission power of the energy storage battery, and They represent the charging efficiency and discharging efficiency of the energy storage battery respectively. is the charging and discharging time interval; to ensure the cyclic operation of the system, the initial charge of the energy storage battery in different representative random scenes s is the same, and the charge at the beginning and end of the same representative random scene is the same.

[0066] (33) (33) (34) In the formula, the instantaneous power of the energy storage battery meets the charge state limit of the energy storage battery. and Respectively represent the minimum and maximum state of charge of the energy storage battery. Indicates the rated capacity of the energy storage battery. The energy storage battery has a maximum power limit during the charging and discharging process. Indicates the maximum charging or discharging power of the energy storage battery; binary variable and Respectively represent the charging and discharging status of the energy storage battery: Indicates that the energy storage battery is in charging state. Indicates that the energy storage battery is in a discharging state, and charging and discharging are not allowed to occur at the same time.

[0067] The hydrogen storage tank is supplied with hydrogen by the electrolyzer and outputs hydrogen to the downstream methanol synthesis unit. The storage capacity of the hydrogen storage tank at any time and the operational constraints of hydrogen storage and release are as follows: (35) In the formula, and are the hydrogen storage capacity of the hydrogen storage tank at the representative random scenario s at time t and time t-1, Represents the initial hydrogen reserves required by the hydrogen storage tank in the representative random scenario s; to ensure the cyclic operation of the system, the initial hydrogen reserves of the hydrogen storage tanks in different representative random scenarios are the same, and the hydrogen reserves at the beginning and end of the same representative random scenario are the same.

[0068] (36) (37) (38) In the formula, the instantaneous hydrogen capacity of the hydrogen storage tank meets the upper and lower capacity requirements, represents the hydrogen flow rate from the electrolyzer to the hydrogen storage tank at time t in the representative random scenario s, represents the hydrogen flow rate from the hydrogen storage tank to the methanol synthesis unit at time t in the representative random scenario s, is the rated capacity of the hydrogen storage tank, and They represent the lower limit factor and upper limit factor of the hydrogen storage tank storage capacity respectively. The hydrogen flow rate of the hydrogen storage tank input-output meets the flow rate limit. Indicates the maximum input or output flow rate of the hydrogen tank; binary variable and Respectively represent the input-output status of the hydrogen storage tank: Indicates that the hydrogen tank is in the input state. Indicates that the hydrogen storage tank is in output state. The input and output states cannot be 1 at the same time.

[0069] 2. Calculate the initial configuration The system design and operation optimization model of the renewable chemical production system proposed in the present invention is a mixed integer nonlinear programming model (MINLP). The calculation is implemented on the GAMS platform. The selected global solver is the DICOPT solver, in which the MIP subproblem is solved by the CPLEX solver, and the NLP subproblem is solved by the CONOPT solver.

[0070] (1) Construct representative random scenarios of renewable energy and calculate the distribution probability of uncertain random scenarios In order to effectively reduce the number of scenarios, the present invention obtains representative random scenarios of the required renewable energy output through two clusterings. The specific scenario generation process is as follows: 1) Using the silhouette coefficient, Davies-Bouldin index and Calinski-Harabasz index as statistical error metrics, calculate the number of representative random scenarios that can be generated by renewable energy; 2) Use K-means clustering method to generate initial representative random scenarios of renewable energy and their corresponding typical day sets; 3) Using the mean and standard deviation as statistical indicators, perform secondary clustering on the initial representative random scenes to obtain the final representative random scenes and their corresponding typical day sets; 4) According to the proportion of typical days in each representative random scenario, the probability of occurrence of each representative random scenario is determined, the probability distribution of each representative random scenario is obtained and substituted into the system design operation optimization model.

[0071] (2) Construct a boundary tightening algorithm to linearize the bilinear constraints in the system design operation optimization model.

[0072] To eliminate the bilinear term constraints in the system design operation optimization model (in Eq. (11) And in formula (16) ), the tightened piecewise McCormick relaxation method is used to linearize the bilinear term constraints in the constraints, as shown below.

[0073] (39) (40) (41) (42) (43) Where: P is the number of segments, p =1,2,…,P; and Respectively indicate flow The upper and lower boundaries of and Respectively represents energy consumption The upper and lower boundaries of and Represents the number of methanol production lines The upper and lower boundaries of Represents each segment p Hydrogen flow rate in methanol production line, Represents each segment p The number of methanol production lines scheduled in , and Explained above. Binary variables Indicates whether the variable value is in the segment during the solution process. When , the variable is in the segment, otherwise it is not; in formulas (39)-(42) c Indicates H 2 .

[0074] When the tightened piecewise McCormick relaxation method is used for linearization, the feasible region is given by P The segmented cutting is performed, and equation (43) is only relaxed using the standard McCormick envelope method, which is equivalent to P = 1, the solution obtained by optimization may not fall into the feasible domain. To this end, the present invention proposes a feasible domain boundary tightening algorithm with bilinear term constraints. The detailed process is shown in Figure 3 .

[0075] During the iterative calculation process, the upper and lower bounds of the variables in the bilinear term constraints are continuously updated until the bounds converge to the specified tolerance. The steps of the algorithm are as follows: Step 0: Choose a large set of initial boundary values, including the ones mentioned above , , , , , , the number of segments P is in the range [0, 10], specifying the convergence tolerance standard and ; Step 1: Initialize variables , and And solve it. If the solution is not feasible due to poor initialization, continue to change the initial values ​​of the variables to solve until the lower bound of the feasible solution of the model after McCormick relaxation is obtained; Step 2: After the above steps are feasible, calculate the bilinear term constraints Tolerance on both sides of the equation, if the tolerance does not meet the convergence requirements < , then change the segment number P value and the upper and lower boundaries of the variable , , , , continue to the next iteration; if feasible, the upper and lower bounds of the variables in the bilinear term remain unchanged; Step 3: Calculate the bilinear constraints The tolerances on both sides of the equation are compared to see if they meet the tolerance criteria for convergence < If it is not satisfied, change the upper and lower boundaries of the variable , , continue to the next iteration; if satisfied, the iteration ends and a feasible solution is output.

[0076] After linearizing the bilinear constraints of equations (11) and (16), the resulting constraints (39)-(43) replace the constraints in equation (11) And in formula (16) , and obtain a new system design and operation optimization model. Substitute the probability distribution and output power parameters of each representative random scenario into the new system design and operation optimization model, and use the DICOPT solver to solve it to obtain the design variables and operation variables. The design variables include the rated power of the electrolyzer , Rated capacity of energy storage battery and hydrogen tank rated capacity , and the rated capacity of the methanol synthesis unit and total quantity ; The operating variables include the dispatch power of the renewable energy generation unit, the input and output power of the electrolyzer, energy storage battery and hydrogen storage tank, and the hydrogen flow rate (such as , , , , , , , , , ), production equipment in methanol production line i Energy consumption , input and output components and components c Mass flow rate ,as well as and .

[0077] The renewable chemical production system is designed according to the design variables, and the renewable chemical production system is operated according to the operating variables. During the operation of the renewable chemical production system, when the output of renewable energy changes, the new system design and operation optimization model is used again under the condition that the design variables are fixed to solve the operating variables, thereby adjusting the operating variables of the renewable chemical production system.

[0078] In order to verify the feasibility of the present invention and demonstrate the use of the present invention, the method proposed in the present invention is used to simulate the photovoltaic resources in a certain place. The random scene generation method proposed in the present invention is used to generate representative random scenes for the historical data of local photovoltaics, and the representative random scenes are obtained as follows: Figure 4 As shown, the representative random scenarios are substituted into the model of the present invention for optimization calculation to obtain the system design configuration and the operation process of each representative random scenario. The distribution of the system photovoltaic power in the representative random scenario S5 is shown in Figure 5 The power input and hydrogen output of the electrolyzer are shown in Figure 6 As shown, the storage capacity of the energy storage battery and the power input and output operations are as follows Figure 7 As shown in the figure, the hydrogen storage capacity of the hydrogen storage tank and the hydrogen input and output operations are as follows Figure 8As shown in the figure, the capacity and quantity scheduling of a single group of modular methanol production lines are as follows: Fig. 9 shown.

[0079] The energy distribution in the representative random scenario S5 has a clear distribution hierarchy. The upper limit of the power supply to the electrolyzer is 457MW. The excess is first supplied to the methanol synthesis unit, and the excess power is stored in the energy storage battery. The power required by the electrolyzer in the random optimization is mainly supplied by the renewable energy power generation unit, and the energy storage battery is used as a supplement only when the renewable energy power generation unit is insufficient. The charging and discharging of the energy storage battery follows the trend of peak charging and valley discharge, and the charging time is slightly delayed. The hydrogen storage tank is stored at the 8th to 11th moment and the 17th to 20th moment, and is supplied to the methanol synthesis unit at the peak moment of the photovoltaic output to increase the amount of power directly supplied to the methanol synthesis unit by the renewable energy power generation unit. The methanol production line of the representative random scenario S5 has a higher capacity at the peak than its rated capacity, which reduces the energy storage demand in this scenario.

[0080] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0081] In yet another embodiment of the present invention, a system for designing and operating a renewable chemical production system capable of coping with supply-side uncertainty is provided, comprising: A representative random scenario construction module is used to construct representative random scenarios of renewable energy output and calculate the probability distribution of each representative random scenario; A calculation module, for inputting the probability distribution of representative random scenarios and the operating parameters and economic parameters of the renewable chemical production system into the system design and operation optimization model, solving the model, and obtaining the design variables and operating variables of the renewable chemical production system; The renewable chemical production system includes a chemical synthesis unit, an energy storage unit, and a renewable energy power generation unit. The chemical synthesis unit includes multiple modular chemical production lines. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the chemical synthesis unit. The electricity from the energy storage battery is supplied to the chemical synthesis unit. The hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the chemical synthesis unit. The hydrogen stored in the hydrogen storage tank is supplied to the chemical synthesis unit. The system design and operation optimization model aims to minimize the annual total cost of the renewable chemical production system.

[0082] In another embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a design operation method for a renewable chemical production system that can cope with supply-side uncertainty.

[0083] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the design and operation method of a renewable chemical production system that can cope with supply-side uncertainty in the above embodiment.

[0084] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty, characterized in that: include: Construct representative random scenarios of renewable energy output and calculate the probability distribution of each representative random scenario; The probability distribution of representative random scenarios and the operating parameters and economic parameters of the renewable chemical production system are input into the system design and operation optimization model, and the design variables and operating variables of the renewable chemical production system are obtained by solving the model; The renewable chemical production system includes a chemical synthesis unit, an energy storage unit, and a renewable energy power generation unit. The chemical synthesis unit includes multiple modular chemical production lines. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the chemical synthesis unit. The electricity from the energy storage battery is supplied to the chemical synthesis unit and the electrolyzer. The hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the chemical synthesis unit. The hydrogen stored in the hydrogen storage tank is supplied to the chemical synthesis unit. The system design and operation optimization model aims to minimize the annual total cost of the renewable chemical production system.

2. The method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty according to claim 1, characterized in that: The representative random scenarios of renewable energy output are constructed, and the probability distribution of each representative random scenario is calculated. The specific steps are as follows: 1) Using the silhouette coefficient, Davies-Bouldin index and Calinski-Harabasz index as statistical error metrics, the number of representative random scenarios of renewable energy output is calculated; 2) Based on the number of representative random scenarios, the K-means clustering method is used to generate the initial representative random scenarios of renewable energy output and the corresponding typical day sets; 3) Using the mean and standard deviation as statistical indicators, perform secondary clustering on the initial representative random scenes to obtain the final representative random scenes and the corresponding typical day set; 4) According to the proportion of typical days in each representative random scene, the probability of occurrence of each representative random scene is determined, and the probability distribution of each representative random scene is obtained.

3. The method for designing and operating a renewable chemical production system capable of coping with supply-side uncertainty according to claim 1, characterized in that: The objective function of the system design operation optimization model is: In the formula, represents the total annual cost of the renewable chemical production system, CRF represents the investment return factor, represents the fixed investment cost of renewable chemical production systems, Represents the renewable chemical production system in a representative random scenario s The operating costs of Represents a representative random scene s The probability distribution of S is the set of typical days corresponding to the representative random scene.

4. The method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty according to claim 3, characterized in that: The fixed investment cost of the renewable chemical production system is: In the formula, Indicates production equipment i Fixed investment costs; Is with production equipment i The capacity of the corresponding benchmark equipment; It is a production equipment in the chemical production line i Rated capacity; It is a production equipment in the chemical production line i Fixed investment costs, Is with production equipment i The corresponding benchmark equipment investment cost, Indicates production equipment in chemical production lines i The capacity scale economy index, Represents the total number of production equipment in the chemical synthesis unit, Indicates production equipment in chemical production lines i The standardized batch economic index, I, is the set of production equipment in a chemical production line; represents the fixed investment cost of the energy storage unit, represents the unit capacity investment cost of energy storage device j, represents the installed capacity of energy storage device j, where J is the collection of electrolyzers, energy storage batteries, and hydrogen storage tanks; represents the initial hydrogen purchase cost, Indicates the purchase price of hydrogen per unit mass, Indicates the initial hydrogen reserve required to be purchased for the hydrogen storage tank; The operating cost of the renewable chemical production system under the representative random scenario s is: In the formula, Indicates the electricity price, Indicates the operation time. represents the production equipment at time t of the representative random scenario s i Virtual module capacity, Indicates production equipment in chemical production lines i The power or capacity actually required for operation at time t of a representative random scenario s; and Respectively represent production equipment i The rate of change of apparent efficiency when the efficiency is less than or greater than the rated state.

5. The method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty according to claim 1, characterized in that: The constraints of the system design and operation optimization model include: total energy balance constraints on the power generation of renewable energy power generation units, energy balance constraints on energy storage batteries, mass balance constraints and energy balance constraints on electrolyzers, energy balance constraints on chemical synthesis units, raw material constraints on chemical production lines, material balance constraints, energy consumption constraints, state constraints and operation constraints of each production equipment in the chemical production line, and operation constraints of each storage device in the energy storage unit.

6. The method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty according to claim 5, characterized in that: The chemical synthesis unit is a methanol synthesis unit, the chemical production line is a methanol production line, and the energy balance constraint of the chemical synthesis unit is: In the formula, Represents the electric energy consumed by all production equipment in the methanol synthesis unit at time t; represents the number of methanol production lines running in the methanol synthesis unit at time t, represents the total energy consumption of a single methanol production line, Indicates the production equipment in the methanol production line i Energy consumption; The raw material constraints for the chemical production line are: In the formula, represents the total flow rate of hydrogen required for a single methanol production line at the representative random scenario s at time t, represents the number of methanol production lines running in the methanol synthesis unit at the representative random scenario s at time t, represents the mass flow rate of stream k entering a single methanol production line at time t in a representative random scenario s; represents the mass flow rate of CO2 in stream k entering a single methanol production line at time t in a representative random scenario s, is the mass fraction of H2 in stream k, is the mass fraction of CO2 in stream k.

7. The method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty according to claim 6, characterized in that: The feasible region boundary tightening algorithm is used to linearize the energy balance constraint of the chemical synthesis unit and the raw material constraint of the chemical production line. Specifically, it includes: Step 0: Select a group , and The initial boundary value of the segment number P is in the range of [0, 10], and the convergence tolerance standard is specified. and ; p =1,2,…,P; Represents each segment p Hydrogen flow rate in methanol production line, where the subscript c represents hydrogen; Step 1: Initialize variables , and And solve it. If the solution is not feasible due to poor initialization, continue to change the initial value of the variable to solve until a feasible solution lower bound is obtained; Step 2: After the above step 1 is feasible, calculate the bilinear constraint equation Tolerances on both sides, if the tolerances do not meet the convergence tolerance criteria , then change the segment number P value and the variable and The upper and lower boundaries of the tolerance are determined and the next iteration is continued; if the tolerance meets the convergence tolerance criteria , it remains unchanged; Step 3: Calculate the bilinear constraint equation Tolerances on both sides, if the tolerances do not meet the convergence tolerance criteria , then change the variable The upper and lower boundaries of the tolerance are determined and the next iteration is continued; if the tolerance meets the convergence tolerance criteria , the iteration ends and a feasible solution is output.

8. A system for designing and operating a renewable chemical production system that can cope with supply-side uncertainty, characterized in that: include: A representative random scenario construction module is used to construct representative random scenarios of renewable energy output and calculate the probability distribution of each representative random scenario; A calculation module, for inputting the probability distribution of representative random scenarios and the operating parameters and economic parameters of the renewable chemical production system into the system design and operation optimization model, solving the model, and obtaining the design variables and operating variables of the renewable chemical production system; The renewable chemical production system includes a chemical synthesis unit, an energy storage unit, and a renewable energy power generation unit. The chemical synthesis unit includes multiple modular chemical production lines. The energy storage equipment in the energy storage unit includes three types: an electrolyzer, an energy storage battery, and a hydrogen storage tank. The renewable energy power generation unit generates electricity to supply the electrolyzer, the energy storage battery, and the chemical synthesis unit. The electricity from the energy storage battery is supplied to the chemical synthesis unit and the electrolyzer. The hydrogen generated by the electrolyzer is supplied to the hydrogen storage tank and the chemical synthesis unit. The hydrogen stored in the hydrogen storage tank is supplied to the chemical synthesis unit. The system design and operation optimization model aims to minimize the annual total cost of the renewable chemical production system.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for designing and operating a renewable chemical production system that can cope with supply-side uncertainty as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for designing and operating a renewable chemical production system capable of coping with supply-side uncertainty as described in any one of claims 1 to 7 is implemented.

Images