Refinery flexible hydrogen network determination method and device, medium and electronic equipment

Abstract

The invention relates to the field of petroleum processing, in particular to a refinery flexible hydrogen network determination method and device, a medium and electronic equipment. The refinery flexible hydrogen network determination method comprises the following steps: constructing a mathematical programming model of a refinery flexible hydrogen network; determining a flexible constraint condition of the mathematical programming model according to a parameter fluctuation range of the uncertain parameters of the hydrogen network; determining an optimal solution of a target function of the mathematical programming model according to the flexible constraint condition; according to the optimal solution of the target function, a target design scheme of the hydrogen network is determined, and the target design scheme comprises a target topological structure and a target flexible operation strategy of the hydrogen network.

Description

Technical Field

[0001] The present disclosure relates to the field of petroleum processing, and in particular, to a method, device, medium and electronic equipment for determining a flexible hydrogen network in a refinery. Background Art

[0002] As an important resource network system in refining and chemical enterprises, the hydrogen system has become increasingly important under the background of increasingly stringent environmental protection requirements and the continuous upgrading of oil quality. In recent years, the trend of heavy and inferior imported crude oil has intensified, energy conservation, environmental protection and oil cleanliness indicators have become increasingly stringent, and hydrogenation technology has been widely used in refineries. The demand for hydrogen in refining and chemical enterprises has increased year by year, and the cost of hydrogen has continued to rise. The cost of hydrogen has become the second largest cost factor in the raw material cost of refining and chemical enterprises after crude oil. However, the cost of hydrogen production is high, with the cost of hydrogen per ton being about 10,000 to 20,000 yuan. The energy consumption of hydrogen production equipment is huge, with the average comprehensive energy consumption of natural gas hydrogen production being 1000kgEO / t H2 and the average comprehensive energy consumption of coal hydrogen production being 5600kgEO / t H2). In addition, the carbon emissions are large, with the average carbon emissions of natural gas hydrogen production being 10t CO2 / t H2 and the average carbon emissions of coal hydrogen production being 22t CO2 / t H2. Therefore, integrated design and optimization of the refinery hydrogen system to improve hydrogen utilization is an important way for refining and chemical companies to save energy, reduce carbon emissions, tap potential and increase efficiency. Summary of the invention

[0003] The purpose of the present disclosure is to provide a refinery flexible hydrogen network determination method, device, medium and electronic equipment to solve the technical problems in the related art.

[0004] According to a first aspect of the present disclosure, a method for determining a refinery flexible hydrogen network is provided, comprising:

[0005] Constructing a mathematical programming model for a refinery flexible hydrogen network;

[0006] Determining flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network;

[0007] Determining an optimal solution to the objective function of the mathematical programming model according to the flexible constraint conditions;

[0008] According to the optimal solution of the objective function, a target design scheme of the hydrogen network is determined, and the target design scheme includes a target topology structure and a target flexible operation strategy of the hydrogen network.

[0009] Optionally, the mathematical programming model for constructing a refinery flexible hydrogen network includes:

[0010] Obtain fixed parameters and uncertain parameters of a refinery hydrogen system, each of the uncertain parameters corresponding to a parameter fluctuation range and a probability distribution;

[0011] Determine a target topology of a refinery flexible hydrogen network according to the fixed parameters and the uncertain parameters, and determine an operation strategy corresponding to the target topology in each uncertain scenario;

[0012] According to the historical data of the uncertain parameters, the discrete probability of each uncertain scenario is determined to construct the mathematical programming model.

[0013] Optionally, determining the optimal solution of the objective function of the mathematical programming model according to the flexible constraint condition includes:

[0014] Determining an objective function of the mathematical programming model;

[0015] Performing mathematical transformation on the mathematical programming model to simplify the mathematical programming model;

[0016] According to the simplified mathematical programming model and the flexible constraint conditions, iterative calculation is performed through an iterative optimization algorithm to determine the optimal solution of the objective function.

[0017] Optionally, performing mathematical transformation on the mathematical programming model to simplify the mathematical programming model includes:

[0018] Determining the overall variation space corresponding to the uncertain parameters of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters;

[0019] Discretizing the overall change space to obtain a plurality of operation strategies corresponding to the target topological structure, each operation strategy corresponding to a discrete probability;

[0020] The feasibility constraints of the mathematical programming model after discretization are reconstructed to obtain a mathematical programming model in the form of max-min-max constraints.

[0021] Optionally, performing iterative calculations by an iterative optimization algorithm according to the simplified mathematical programming model and the flexible constraint conditions to determine an optimal solution of the objective function includes:

[0022] Determining a corresponding initial critical point set of the uncertain parameters;

[0023] Determining an initial design scheme of the hydrogen network according to the initial critical point set and the flexible constraint condition;

[0024] When the initial design scheme meets the preset feasibility requirements, the target flexible operation strategy of the hydrogen network is determined according to the optimal solution of the objective function.

[0025] Optionally, performing iterative calculations by an iterative optimization algorithm according to the simplified mathematical programming model and the flexible constraint conditions to determine an optimal solution of the objective function further includes:

[0026] In the case that the initial design solution does not meet the preset feasibility requirements, the initial critical point set is updated according to the vertices of the overall variation space of the uncertain parameters.

[0027] Optionally, the uncertain parameters include operating layer parameters of the hydrogen network, and the overall variation space of the uncertain parameters can be expressed as:

[0028] T={θ|θ N -Δθ≤θ≤θ N +Δθ}

[0029] Where T represents the overall variation space of the uncertain parameters, θ represents the operating layer parameters, and θ N represents the nominal value of the uncertain parameter, and Δθ represents the deviation value of the uncertain parameter from the nominal point.

[0030] According to a second aspect of the present disclosure, a refinery flexible hydrogen network determination device is provided, comprising:

[0031] A building block for constructing a mathematical programming model for a refinery’s flexible hydrogen network;

[0032] A first determination module, configured to determine the flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network;

[0033] A second determination module is used to determine the optimal solution of the objective function of the mathematical programming model according to the flexible constraint condition;

[0034] The third determination module is used to determine a target design scheme of the hydrogen network according to the optimal solution of the objective function, wherein the target design scheme includes a target topology structure and a target flexible operation strategy of the hydrogen network.

[0035] According to a third aspect of the present disclosure, there is provided a non-temporary computer-readable storage medium having a computer program stored thereon, which implements the steps of any one of the methods described in the first aspect when executed by a processor.

[0036] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0037] a memory having a computer program stored thereon;

[0038] A processor is used to execute the computer program in the memory to implement the steps of any one of the methods in the first aspect.

[0039] Through the above technical scheme, a mathematical programming model containing flexible design constraints for the refinery's flexible hydrogen network is constructed to cope with various uncertainties in the refinery's hydrogen system. Stochastic programming is used to deal with the uncertain parameters of the hydrogen network related to external market changes, and then the target topology and target flexible operation strategy of the hydrogen network are determined by determining the optimal solution of the objective function of the mathematical programming model. Furthermore, by using flexible constraints to deal with uncertain parameters at the operating layer, the robustness of process operations can be ensured within the entire uncertain parameter space. Compared with the traditional deterministic hydrogen network optimization model, the target design scheme obtained by this technical scheme can flexibly recycle and cascade match hydrogen resources between complex hydrogen network topologies. The designed hydrogen network structure has a flexible operation scheme for multiple scenarios. While tapping the potential and increasing the efficiency of the hydrogen network, it ensures the smooth and safe operation of the hydrogen network, with significant economic benefits.

[0040] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0042] Figure 1 The present invention is a flowchart of a method for determining a flexible hydrogen network for a refinery according to an exemplary embodiment.

[0043] Figure 2 1 is a comparison chart of annual investment costs and operating costs of three optimization models according to an exemplary comparative example.

[0044] Figure 3 1 is a comparison chart of the total annual costs of three optimization models according to an exemplary comparative example.

[0045] Figure 4 It is the optimal hydrogen network design calculated according to the deterministic optimization model shown in an exemplary comparative example.

[0046] Figure 5 The optimal hydrogen network design is calculated according to a stochastic programming model shown in an exemplary comparative example.

[0047] Figure 6 The invention is an operation scheme of a hydrogen network under a scenario of high hydrogen price, low electricity price and low fuel gas price according to an exemplary comparative example.

[0048] Figure 7The operating scheme of the hydrogen network under the scenario of low hydrogen price, high electricity price and medium fuel gas price is shown according to an exemplary comparative example.

[0049] Figure 8 The optimal hydrogen network design is calculated based on a stochastic programming model including flexible constraints shown in an exemplary comparative example.

[0050] Fig. 9 The present invention is a block diagram of a refinery flexible hydrogen network determination device according to an exemplary embodiment.

[0051] Fig.10 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0053] In the related technology, the operation of the refinery hydrogen network is affected by many uncertain factors, such as changes in the properties of the feed crude oil, fluctuations in the market demand for oil and gas products, fluctuations in utility prices, and changes in operating conditions.

[0054] Specifically, the uncertainties in the design and optimization of hydrogen networks can be divided into two categories according to their sources: the first category is the uncertainty related to external market changes, including fluctuating electricity prices and hydrogen utility prices on the cost side, and changing oil and gas prices on the profit side; the second type of uncertainty is directly related to process operations, usually referring to changes in hydrogen supply from hydrogen sources and fluctuations in hydrogen demand from hydrogen sinks.

[0055] On the one hand, when the properties of the feed crude oil change (such as changes in sulfur content), it will directly affect the demand for hydrogen in the hydrogenation unit; on the other hand, due to the strict restrictions on the aromatic content of gasoline in recent years, the amount of by-product hydrogen in the catalytic reforming unit has decreased, and the supply of hydrogen from the hydrogen source has fluctuated to a certain extent. The imbalance between the supply and demand of hydrogen between the hydrogen source and the hydrogen sink will have serious consequences, such as substandard oil and gas product quality, and even emergency shutdown of the unit.

[0056] Therefore, on the one hand, the integrated design and operational optimization of the refinery hydrogen network should focus on "economic optimization" and "operational stability", so as to comprehensively consider various uncertainties in the modeling of the hydrogen network and make the established model closer to the actual operation of the hydrogen network. On the other hand, with the proposal of the carbon neutrality goal, green hydrogen refining demonstration projects have begun to be gradually laid out, and the introduction of green hydrogen into the plant will have a certain impact on the economy and stability of the hydrogen system. In the design and optimization of the hydrogen network, more attention should be paid to the operational flexibility of the hydrogen network, and the uncertainties such as the economy and stability that may be brought about by the introduction of green hydrogen into the plant should be fully considered, so as to ensure the efficient, safe and stable operation of the hydrogen network.

[0057] In the related technologies, the optimization of hydrogen network is mostly aimed at the optimization problem of hydrogen network under certain working conditions, assuming that all hydrogen production devices and hydrogen consumption devices are operating normally, and the hydrogen production and consumption of hydrogen production and consumption devices are fixed, and the external demand and market price of related raw materials remain unchanged, and the economic optimization is achieved by optimizing the hydrogen supply strategy and purification strategy of the hydrogen system. However, in the actual production process, production plans, market demand, and the quality of process products often change, and the availability of hydrogen production devices and hydrogen consumption devices also has certain adjustments and changes. The flexible design and robust operation of the hydrogen network are crucial.

[0058] In view of this, the present disclosure provides a refinery flexible hydrogen network determination method, device, medium and electronic equipment to solve the technical problems in the related art.

[0059] Figure 1 FIG. 1 is a flow chart showing a method for determining a refinery flexible hydrogen network according to an exemplary embodiment. Figure 1 As shown, the refinery flexible hydrogen network determination method may include the following steps:

[0060] Step S11, constructing a mathematical programming model for the refinery flexible hydrogen network.

[0061] Step S12, determining the flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network.

[0062] Step S13, determining the optimal solution of the objective function of the mathematical programming model according to the flexible constraint conditions.

[0063] Step S14, determining a target design scheme for the hydrogen network according to the optimal solution of the objective function, wherein the target design scheme includes a target topology structure and a target flexible operation strategy for the hydrogen network.

[0064] It should be understood that the mathematical programming model of the refinery flexible hydrogen network is used to output a target design scheme for the hydrogen network, wherein the target design scheme includes a target topology and a target flexible operation strategy of the hydrogen network.

[0065] Through the above technical scheme, a mathematical programming model containing flexible design constraints for the refinery's flexible hydrogen network is constructed to cope with various uncertainties in the refinery's hydrogen system. Stochastic programming is used to deal with the uncertain parameters of the hydrogen network related to external market changes, and then the target topology and target flexible operation strategy of the hydrogen network are determined by determining the optimal solution of the objective function of the mathematical programming model. Furthermore, by using flexible constraints to deal with uncertain parameters at the operating layer, the robustness of process operations can be ensured within the entire uncertain parameter space. Compared with the traditional deterministic hydrogen network optimization model, the target design scheme obtained by this technical scheme can flexibly recycle and cascade match hydrogen resources between complex hydrogen network topologies. The designed hydrogen network structure has a flexible operation scheme for multiple scenarios. While tapping the potential and increasing the efficiency of the hydrogen network, it ensures the smooth and safe operation of the hydrogen network, with significant economic benefits.

[0066] Among the possible approaches, the mathematical programming model for constructing a refinery flexible hydrogen network can be:

[0067] Obtain fixed parameters and uncertain parameters of the refinery hydrogen system, each uncertain parameter corresponds to a parameter fluctuation range and probability distribution;

[0068] Determine the target topology of the refinery's flexible hydrogen network based on fixed parameters and uncertain parameters, and determine the corresponding operation strategy of the target topology under each uncertain scenario;

[0069] Based on the historical data of uncertain parameters, the discrete probability of each uncertain scenario is determined to construct a mathematical programming model.

[0070] It should be understood that the refinery hydrogen system consists of six important units, namely: hydrogen source, hydrogen sink, hydrogen utility, hydrogen purification and recovery unit, compression unit and fuel gas system. The uncertain parameters of the refinery flexible hydrogen network include external uncertain parameters such as changing electricity prices, hydrogen utility prices and fuel gas prices, as well as uncertain parameters at the operational level such as the supply flow rate, hydrogen purity and stream pressure of hydrogen source, the minimum demand for hydrogen sink, the minimum hydrogen purity requirement and hydrogen pressure requirement.

[0071] For example, the six important units of the refinery hydrogen system can be represented as hydrogen source (i = 1, 2, ..., |I|), hydrogen sink (j = 1, 2, ..., |J|), hydrogen utility (h = 1, 2, ..., |H|), hydrogen purification and recovery unit (m = 1, 2, ..., |M|), compression unit (c = 1, 2, ..., |C|) and fuel gas system (f = 1, 2, ..., |F|). The changing electricity price, hydrogen utility price and fuel gas price are represented by different price scenarios predicted based on historical market data (s = 1, 2, ..., |S|). The probability distribution of the parameter fluctuation range corresponding to each uncertain parameter can be determined based on existing data such as historical data. The parameter fluctuation range of the operation layer is represented by the vertex of the uncertainty set (v = 1, 2, ..., |V|).

[0072] It should also be understood that the target topology of the refinery flexible hydrogen network can be designed from scratch based on fixed parameters and uncertain parameters, or the existing topology can be adjusted and optimized, and the embodiments of the present disclosure do not specifically limit this.

[0073] For example, the stochastic programming problem of the mathematical programming model can include two stages. The decision of the first stage is related to the configuration of the network structure, which constitutes the investment cost of the hydrogen network; the decision of the second stage is related to the actual operation of the hydrogen network under multiple scenarios, which determines the operating cost of the hydrogen network. Specifically, the decision variables of the first stage are design variables, which determine the topological structure of the hydrogen network, that is, the target topological structure of the refinery flexible hydrogen network is determined based on fixed parameters and uncertain parameters. The decision variables of the second stage are operating variables, which are related to the operating performance of the hydrogen network, and represent the optimal operating conditions of the hydrogen network under each scenario. They include state variables such as the flow rate, purity and pressure of the hydrogen flow stream. The actual operation of the hydrogen network can be adjusted according to the gradual realization of uncertain parameters, that is, the operating strategy corresponding to the target topological structure under each uncertain scenario is determined.

[0074] Based on the historical data of the external market, discrete price scenarios can be used to describe the fluctuations in hydrogen utility prices, electricity prices and fuel gas prices, and corresponding discrete probabilities can be set for each scenario based on historical data.

[0075] In a possible approach, the uncertain parameters include the operational layer parameters of the hydrogen network, and the overall variation space of the uncertain parameters can be expressed as:

[0076] T={θ|θ N -Δθ≤θ≤θ N +Δθ}

[0077] Among them, T represents the overall variation space of uncertain parameters, θ represents the operation layer parameters, and θ Nrepresents the nominal value of the uncertain parameter, and Δθ represents the deviation of the uncertain parameter from the nominal point.

[0078] It should be understood that for the uncertainty parameters of the operation layer, such as the changes in the hydrogen supply of the hydrogen source and the hydrogen demand of the hydrogen sink, the hyperrectangle in the flexible analysis can be used to describe the overall change space T of the uncertain parameters, which can be expressed as T = {θ|θ N -Δθ≤θ≤θ N +Δθ}, and determine the optimal design d in the overall variation space T of uncertain parameters, so that the expected value of the cost of the hydrogen network in all scenarios is minimized. At the same time, in this design d, for any point in the uncertain parameter set T of the operation layer, a feasible operation strategy can always be achieved by adjusting an operation variable z.

[0079] In a possible way, the optimal solution of the objective function of the mathematical programming model determined according to the flexible constraints can be:

[0080] Determine the objective function of the mathematical programming model;

[0081] Perform mathematical transformation on the mathematical programming model to simplify the mathematical programming model;

[0082] According to the simplified mathematical programming model and flexible constraints, iterative calculation is performed through iterative optimization algorithm to determine the optimal solution of the objective function.

[0083] For example, the annual total cost TAC of the hydrogen network can be used as the objective function, and the optimal solution of the objective function can be the optimal solution of the annual total cost, which is expressed as follows:

[0084]

[0085] in, represents the annual consumption cost of hydrogen utilities, C power represents the annual electricity cost of the compressor, C fuel represents the annual consumption cost of fuel gas, Af(C comp +C purifier +C pipe ) represents the annual investment cost of equipment and pipelines, Af represents the conversion factor (equipment annual depreciation factor), C comp represents the compressor investment cost, C purifier represents the investment cost of hydrogen purification and recovery equipment, C pipe Represents the cost of laying pipelines.

[0086] The objective function includes the expected cost and annual investment and construction cost under the multi-scenario objective function for one year, achieving the optimal trade-off between investment cost and operating cost. Specifically, the annual consumption cost of hydrogen utilities, the annual electricity consumption cost of compressors and the annual profit of fuel gas are comprehensively considered in the operating cost. The specific calculation formula is as follows:

[0087]

[0088] in, represents the annual consumption cost of hydrogen utilities, t h Indicates annual operating time (hydrogen utility), F h,s represents the flow of hydrogen utilities under the sth scenario, e h,s represents the price of hydrogen utility under the s-th scenario, and Prob(s) represents the probability of the s-th scenario occurring.

[0089] C power =epower s ∑ c∈C,s∈s t c POWER c,s Prob(s) (3)

[0090] Among them, C power represents the annual electricity consumption cost of the compressor, epower s represents the electricity price in the sth scenario, t c Indicates annual operating time (compressor), POWER c,s It represents the power of the compressor in the sth scenario.

[0091]

[0092] Among them, F c,s represents the flow rate of the compressor in the sth scenario, C p,c is the isobaric heat capacity of the gas, η c represents the compressor efficiency, Compressor outlet pressure, represents the compressor inlet pressure, γ c represents the ratio of the isobaric heat capacity to the isochoric heat capacity of a gas, ρ c represents the compressed gas density, ρ c,o Indicates the density of compressed gas under standard conditions.

[0093]

[0094] Among them, C fuel represents the annual consumption cost of fuel gas, efuel s represents the fuel gas price under the sth scenario, F fuel,srepresents the flow rate of fuel gas in the sth scenario, represents the standard heat of combustion of hydrogen, y fuel,s represents the hydrogen purity of the fuel gas in the sth scenario, Indicates the standard heat of combustion of CH4.

[0095] The investment cost is composed of the annual investment and construction costs of the compressor, purification unit and pipeline, and the specific calculation formula is as follows:

[0096]

[0097] C comp =∑ c∈C α c x c +β c POWER c,s (7)

[0098]

[0099]

[0100] Among them, fi represents the annual interest rate, ny represents the investment cost recovery period, α c represents the fixed cost coefficient of compressor investment cost, x c Indicates whether there is a compressor, β c Represents the variable cost coefficient of compressor investment cost. m represents the fixed cost coefficient of the investment cost of the hydrogen purification and recovery device, x m Indicates whether there is a hydrogen purification and recovery device, β m It represents the variable cost coefficient of investment cost of hydrogen purification and recovery device. Indicates the maximum flow rate of hydrogen produced by the hydrogen purification unit. C represents the compressor set, and M represents the purifier set.

[0101] L p,q Indicates the length of the connecting pipe between points p and q, Y p,q Indicates whether there is a pipeline connection between points p and q, α pipe represents the fixed cost coefficient of pipeline investment and construction cost, β pipe represents the variable cost coefficient of pipeline investment and construction cost, F p,q represents the pipe flow rate between points p and q, ρ p,o represents the gas density at point p under standard conditions, ρ p represents the gas density at point p, u represents the gas flow rate in the pipeline, and O represents the set of all possible connections between the separator and the mixer.

[0102] Since the operating cost is affected by external market price fluctuations, it is related to the second-stage operating variables, that is, the cost of the operating strategy part. The investment cost only involves the design variables of the first stage, that is, the cost of the topological structure, and does not depend on the multi-scenario operating plan. Therefore, the objective function can be to minimize the sum of the mathematical expectations of the first-stage investment cost and the second-stage operating cost.

[0103] In a possible manner, a mathematical transformation is performed on the mathematical programming model to simplify the mathematical programming model to be:

[0104] According to the parameter fluctuation range of the uncertain parameters, the overall variation space corresponding to the uncertain parameters of the mathematical programming model is determined;

[0105] Discretize the overall change space to obtain multiple operation strategies corresponding to the target topological structure, and each operation strategy corresponds to a discrete probability;

[0106] The feasibility constraints of the discretized mathematical programming model are reconstructed to obtain a mathematical programming model in the form of max-min-max constraints.

[0107] For example, the tight constraint method can be used to perform flexibility testing on a two-stage stochastic model with flexibility constraints to ensure the robustness of process operation. Using the tight constraint method for flexibility evaluation will produce a mixed integer nonlinear programming problem as shown in the following calculation formula.

[0108]

[0109] Among them, s j is the slack variable of the inequality constraint, λ j and μ i denote the Lagrange multipliers corresponding to inequality constraint j and equality constraint i, respectively, y j represents a binary 0-1 variable (if constraint j is a tight constraint, then y j =1; otherwise y j =0).

[0110] The feasibility conditions are shown in the following calculation formula:

[0111] stg j (d,z,x,θ)+s j =uj∈J

[0112] h i (d,z,x,θ)=0,i∈I

[0113] The steady-state conditions of the Lagrangian function for the control variable z and the state variable x are shown in the following calculation formula.

[0114]

[0115]

[0116]

[0117] The complementary relaxation condition is shown in the following calculation formula.

[0118]

[0119]

[0120]

[0121] y j ∈{0,1},γ j ≥0,s j ≥0,j∈J,μ i ∈R,i∈I

[0122] It should be understood that, based on the range of variation of uncertain parameters, the mathematical programming model includes infinite uncertain scenarios. The flexibility of the infinite inequality constraints of the infinite uncertain scenarios needs to be tested in the entire operational uncertain parameter space. The optimal design including the flexible constraints can be expressed as the following two-stage nonlinear infinite programming mathematical form, which indicates that for any uncertain parameter θ∈T, there is at least one set of operational variables z that can make all constraints satisfy f j (d,z,θ)≤0.

[0123]

[0124] For example, to simplify the problem, the uncertain parameter space can be discretized to approximate the expectation of the cost by weighted averaging of discrete scenarios.

[0125] The original problem is shown in the following calculation formula:

[0126]

[0127] After discretizing the uncertain parameter space, the original problem is simplified to a nonlinear semi-infinite programming problem with finite decision variables and infinite constraints as shown in the following formula.

[0128]

[0129] Among them, S represents the set of possible scenarios. is the discrete probability corresponding to each scenario or represents a finite number of parameter points θ s The weight of .

[0130] For example, the problem can be further simplified by reconstructing the feasibility constraints. That is, the constraints in logical form can be equivalently converted into the max-min-max constraint form as shown in the following calculation formula:

[0131] max θ∈T min z max j∈J f j (d,z,θ)≤0 (14)

[0132] Then, through the approximation of the objective function and the equivalent expression of the feasibility constraint, the optimal design problem including the flexible constraint can be finally expressed as the following calculation formula:

[0133]

[0134] Among them, z s represents the operating variables under different scenarios, θ s Represents the uncertain parameters under different scenarios.

[0135] The critical point θ that is most likely to violate the inequality constraint can be determined based on the max-min-max constraint c , and impose feasibility restrictions on the worst constraints at the critical points. If all critical points are located at the vertices of the hyper-rectangular region formed by uncertain parameters, then for any θ∈T, the feasibility of the operation can be guaranteed by considering the feasibility of the design at all vertices of the hyper-rectangle. Therefore, the flexibility constraints of all vertices can be added to the calculation formula (15) as the feasibility judgment condition to replace the max-min-max constraint, and it can be further transformed into the following optimization problem (16):

[0136]

[0137] Where V = {1, 2, ..., n θ} is the set of vertices of the hyperrectangle formed by the uncertainty of hydrogen supply from the hydrogen source and hydrogen demand from the hydrogen sink, and S is the set of price scenarios with different combinations of electricity prices, hydrogen prices, and fuel gas prices.

[0138] In a possible way, according to the simplified mathematical programming model and the flexible constraints, an iterative optimization algorithm is used to perform iterative calculations to determine the optimal solution of the objective function, which can be:

[0139] Determine the corresponding initial critical point set of uncertain parameters;

[0140] Determine the initial design of the hydrogen network based on the initial critical point set and flexible constraints;

[0141] When the initial design scheme meets the preset feasibility requirements, the target flexible operation strategy of the hydrogen network is determined according to the optimal solution of the objective function.

[0142] In a possible manner, when the initial design solution does not meet the preset feasibility requirements, the initial critical point set can be updated according to the vertices of the overall variation space of the uncertain parameters.

[0143] For example, we can first select a finite number of critical points θ in the overall variation space of the uncertain parameters of the operational layer. k The initial set is a subset of the entire vertex set V, and the feasibility constraints on these selected vertices are added to the mathematical programming model. Then, a feasible initial design solution d is obtained by solving the stochastic programming problem with flexible constraints. k :

[0144]

[0145] Then fix the design variables, that is, determine the target topology, and judge the feasibility of the design in the entire uncertain parameter space through flexible evaluation:

[0146]

[0147] Among them, χ(d) is the feasibility function of a given design d. The flexible evaluation adopts a tight constraint strategy to directly solve the max-min-max three-level optimization problem to avoid the combinatorial explosion problem caused by analyzing vertices one by one.

[0148] If the feasibility function χ(d)≤0, the model passes the flexible evaluation, which means that the initial design has a feasible operation for any θ∈T. The current design is feasible and the algorithm terminates. If the feasibility function x(d)>0, the new critical point obtained in the flexible evaluation problem is added to the current vertex set to update the initial critical point set and re-determine the initial design d k .

[0149] Since the number of hyperrectangle vertices is limited, the algorithm can converge within a finite number of iterations. If the initial vertex set can well predict the extreme cases of the model, the algorithm only needs one iteration to converge.

[0150] For example, the symbolic parameters in the above calculation formula are shown in Table 1-4.

[0151] Table 1 Comparison table of the meaning of hydrogen network model set

[0152]

[0153] Table 2 Comparison table of hydrogen network model variables and parameter meanings

[0154]

[0155]

[0156] Table 1 Comparison table of superscript meanings of hydrogen network model

[0157]

[0158] Table 2 Comparison table of subscript meanings of hydrogen network model

[0159]

[0160]

[0161] The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0162] Example 1

[0163] In order to illustrate the universality of the modeling method proposed in the present invention and the high efficiency of the iterative optimization algorithm, the present invention is demonstrated and verified through an industrial case of integrated design and optimization of a refinery hydrogen network. In the refinery, there are 6 important hydrogen sinks, including catalytic diesel hydrotreater (CDHT), straight-run diesel hydrotreater (SDHT), diesel hydrotreater (DHT), kerosene hydrotreater (KHT), wax oil hydrotreater (WHT) and hydrocracker (HC). The hydrogen sources in the refinery include continuous catalytic reformer (CCR), semi-regenerated catalytic reformer (SCR) and hydrogen-containing tail gas discharged by 6 hydrogen sinks.

[0164] In addition, external hydrogen utilities come from two hydrogen plants (Hydrogen plant, HP) and a fertilizer plant (Fertilizer plant, FER) that produces hydrogen as a byproduct. Since the hydrogen from the fertilizer plant contains CO, it will cause the deactivation of the catalyst in the wax oil hydrogenation unit and the hydrocracking unit. Therefore, the hydrogen from the fertilizer plant is prohibited from being transported to these two hydrogenation units. Affected by the uncertainty of the external market, the price of hydrogen, electricity price and fuel gas are all fluctuating. Based on the historical data of the market, these three prices are described by three discrete price scenarios (low price, medium price and high price scenario).

[0165] Therefore, in the stochastic programming model, 27 combined price scenarios related to external market changes will be generated. In addition, affected by factors such as feed crude oil quality, catalyst activity and refinery production plan, in actual operation, the supply of hydrogen from the hydrogen source and the demand for hydrogen from the hydrogen sink fluctuate by ±10% around the nominal value. The uncertainties of these operating layers are also comprehensively considered in the stochastic programming model with flexible constraints to ensure robust operation within the fluctuation space of the entire uncertain parameter. Specific data on this industrial case are given in Tables 5-7.

[0166] Table 5 Nominal values ​​of hydrogen source data in industrial hydrogen systems

[0167]

[0168] Table 6 Nominal values ​​of hydrogen trap data in industrial hydrogen systems

[0169]

[0170]

[0171] Table 7 27 price combination scenarios and their corresponding probabilities in the stochastic programming problem

[0172]

[0173] Table 7 27 price combination scenarios and their corresponding probabilities in the stochastic programming problem

[0174]

[0175]

[0176] Example 1 considers the influence of uncertainty in the design stage, so as to obtain the optimal network topology and multi-scenario operation plan. Here, three different optimization models are compared: a deterministic optimization model (uncertain parameters are replaced by means), a stochastic programming model (only considering uncertainties related to external market changes) and a stochastic programming model with flexible constraints (using stochastic programming to deal with uncertainties related to external market changes, and using flexible constraints to deal with fluctuations in operating layer parameters). The comparison of the annual total cost and the hydrogen network design structure calculated by the three optimization models proves the advantages of the modeling method disclosed in the present invention. In order to analyze and compare these three optimization models more scientifically, the value of stochastic solution (Value of Stochastic Solution, VSS) is used here for quantitative evaluation. VSS evaluates the additional benefits that can be obtained by applying the stochastic programming method compared to the deterministic model. It is an important measurement indicator to illustrate the significance of considering parameter uncertainty. In this industrial case, the definitions of VSS and relative VSS are shown in the following calculation formula.

[0177] VSS=TAC det -TAC sto

[0178]

[0179] Among them, TAC sto represents the total annual cost calculated by two-stage stochastic programming; TAC det It represents the annual total cost obtained by fixing the first-stage design variables to the optimal solution of the deterministic optimization model and then solving the same stochastic programming problem. This means that the hydrogen network topology calculated by the deterministic model needs to be evaluated in a changing environment. VSS represents the cost that can be saved each year by the solution of the stochastic programming model compared to the solution of the deterministic method. Tables 8 and 9 show the model scale and solution results of three different optimization models.

[0180] Table 8 Model scale and solution results of three optimization models

[0181]

[0182]

[0183] Table 9 Comparison of annual total cost, investment cost and operating cost of three optimization models

[0184]

[0185] By comparing the three optimization models, it can be found that the annual total cost obtained by applying the deterministic optimization model is the highest, which is $63876379.01, including the annual investment cost of $28935014.3 in the first phase and the expected annual operating cost of $34941364.63 in the second phase. The annual total cost calculated by the stochastic programming model is the lowest among the three models, and the annual total cost of the stochastic programming model with flexible constraints is slightly higher than that of the stochastic programming model that only considers external market uncertainty. Figure 2 is a comparison chart of annual investment costs and operating costs of three optimization models according to an exemplary comparative example. Figure 2 As shown in the figure, the vertical axis represents the annual cost, and the horizontal axis represents the deterministic model, the stochastic programming model, and the stochastic programming model with flexible constraints from left to right. In each model, the left side represents the investment cost, and the right side represents the operating cost.

[0186] Figure 3 The annual total cost comparison diagram of three optimization models is shown in an exemplary comparative example. Figure 3 As shown in the figure, the vertical axis represents the annual total cost, and the horizontal axis represents the deterministic model, the stochastic programming model and the stochastic programming model with flexible constraints from left to right.

[0187] from Figure 2 and Figure 3It can be found that compared with the deterministic model, the investment and construction costs in the stochastic programming model and the stochastic programming model with flexible constraints are slightly higher, but they can save a lot of annual operating costs and reduce the annual total cost. This is because some pipeline connections are added to the design results of the latter two models, and the design capacity of the purification unit and the compressor is correspondingly expanded. When the external market price scenario changes, agile adjustments can be made to optimize production. The increase in investment and construction costs makes the design structure of the hydrogen network more flexible, which can further tap the potential and increase the efficiency of the actual operation of the hydrogen network, thereby reducing the annual operating cost. By comparing the solutions of the deterministic optimization model and the stochastic programming model, this case calculates VSS = $767634.33. This means that for a large-scale modern refinery, the hydrogen network designed by the stochastic programming model can save a total cost of $767634.33 each year, bringing considerable economic benefits. In addition, compared with the stochastic programming model, the stochastic programming model including flexible constraints also takes into account the uncertainty of the operating layer. Although the annual total cost is slightly increased, it can ensure that the hydrogen network can still operate smoothly when the hydrogen supply of the hydrogen source and the hydrogen demand of the hydrogen sink fluctuate. For the stochastic programming model that only considers the combined price scenario, when extreme operating conditions occur (such as high hydrogen demand in the hydrogen sink and low hydrogen supply in the hydrogen source), it will cause the hydrogen network to have an unfeasible operating condition, that is, it is impossible to satisfy all process constraints by adjusting the operating variables. In summary, the stochastic programming model including flexible constraints disclosed in the present invention not only shows higher economic benefits, but also can ensure the feasibility of operation at extreme operating points, avoiding serious consequences such as substandard oil and gas product quality.

[0188] Figure 4-Figure 8 The optimal network design structure of the hydrogen system calculated by three optimization models and the multi-scenario operation scheme obtained by the stochastic programming model are shown. Here, by analyzing the hydrogen network topology obtained by different optimization models, it is explained how to tap the potential and increase the efficiency of the hydrogen network while ensuring the robustness of the process operation.

[0189] like Figure 4 As shown, the hydrogen network topology of the hydrogen system includes a fertilizer plant FER, a continuous catalytic reforming unit CCR, a semi-regenerative catalytic reforming unit SCR, two hydrogen production plants HP1 and HP2, a pressure swing adsorption separation unit PSA, three compressors CP1, CP2 and CP3, a fuel gas system, and a catalytic diesel hydrogenation unit CDHT, a straight-run diesel hydrogenation unit SDHT, a diesel hydrogenation unit DHT, a jet fuel hydrogenation unit KHT, a wax oil hydrogenation unit WHT and a hydrocracking unit HC.

[0190] Figure 4is the optimal hydrogen network design calculated according to a deterministic optimization model shown in an exemplary comparative example. Figure 4 As shown in the figure, in this design, measures are taken to reduce the operating cost of the hydrogen network, such as making full use of the hydrogen produced by the catalytic reforming unit in the refinery as much as possible, making full use of the high-pressure and cheap hydrogen from the fertilizer plant, and recycling the tail hydrogen discharged from the hydrogenation unit, thereby minimizing the use of external hydrogen utilities. The tail hydrogen discharged from the catalytic diesel hydrogenation unit (CDHT) and the wax oil hydrogenation unit (WHT) has a relatively high hydrogen purity, which is recycled after being purified by the pressure swing adsorption separation unit (PSA); while the low-purity purge gas from the remaining hydrogenation units is directly discharged into the fuel gas system for combustion, thereby saving the power consumption of the circulating compressor.

[0191] Figure 5 The optimal hydrogen network design calculated by the stochastic programming model is shown in an exemplary comparative example. As shown in Table 8, compared with the deterministic model, the annual total cost of applying the stochastic programming model can save $767,634.33. This is because the design results obtained by the stochastic programming model can implement a flexible operation strategy for multiple scenarios. The design obtained by the stochastic programming model adds three pipelines to improve process operation flexibility. The difference in topology structure from the design calculated by the deterministic model is Figure 5 The dashed box is used to mark it. Although the investment and construction cost is slightly increased, the actual operation cost can be greatly reduced. The stochastic programming model not only provides the optimal design of the hydrogen network topology, but also provides the optimal production strategy under different price scenarios, making it more flexible to cope with changes in the external market and flexibly adjust the operation strategy to obtain the maximum economic benefit.

[0192] Figure 6 and Figure 7 Two typical price scenarios are used to illustrate the advantages of the multi-scenario flexible operation strategy obtained by the stochastic programming model.

[0193] Figure 6 This is an operation scheme of the hydrogen network under the scenario of high hydrogen price, low electricity price and low fuel gas price according to an exemplary comparative example. Figure 6As shown in the figure, in the external market environment with high hydrogen prices and low electricity and fuel gas prices, full recovery and utilization of hydrogen has become a key measure to reduce operating costs. More hydrogen-containing relaxation tail gas from hydrogenation units (i.e., CDHT, WHT, SDHT, and HC) will be recycled instead of being directly discharged into the fuel gas system. The hydrogen produced as a by-product of the catalytic reforming unit also needs to be further purified and transported to the downstream hydrogen sink. The dotted arrows in the figure indicate that there is no gas flow in certain pipelines in actual operation under this price scenario. Since the use of compressors is relatively economical in a scenario with low electricity prices, the hydrogenation unit relaxation gas with relatively high hydrogen purity can be recycled as much as possible.

[0194] On the contrary, in a price scenario where hydrogen prices are low and electricity prices are high, it is obviously uneconomical to recycle a large amount of low-purity hydrogen-containing purge gas, because this will lead to a large amount of power consumption of the compressor. Figure 7 This is an operation scheme of the hydrogen network under the scenario of low hydrogen price, high electricity price and medium fuel gas price according to an exemplary comparative example. Figure 7 As shown in the figure, the most economical operation strategy is to discharge the medium-purity hydrogen-containing tail gas from the SDHT unit and the HC unit directly into the gas system, and purchase low-priced hydrogen from the external market as a supplement. It can be seen that the newly built pipelines and the expanded capacity of some units can enable this flexible production operation plan to be implemented, thereby responding to market changes in a timely manner and obtaining higher economic benefits.

[0195] In addition to changes in external market prices, the stochastic programming model with flexible constraints also considers fluctuations in hydrogen supply from hydrogen sources and hydrogen demand from hydrogen sinks. The imbalance in hydrogen supply and demand will have some serious consequences: excessive hydrogen supply will cause overloaded pipeline transportation and waste of hydrogen resources; insufficient hydrogen supply will lead to unstable production of hydrogenation units, thereby deteriorating the quality of oil and gas products after hydrogenation. Severe imbalance in hydrogen supply and demand may even cause shutdown of the unit.

[0196] Figure 8 is the optimal hydrogen network design calculated by a stochastic programming model including flexible constraints shown in an exemplary comparative example. Figure 8As shown in the figure, in the design results obtained by the stochastic programming model with flexible constraints, a new pipeline is added from the hydrogen plant HP2 to the PSA unit (marked with a regular dash-dot-dot arrow in the figure) to ensure a stable supply of hydrogen under extreme operating conditions. For example, the feed is high-sulfur and high-acid crude oil, which leads to a sharp increase in hydrogenation, and the strict restrictions on the aromatic content in the oil and gas products lead to a decrease in the hydrogen production of the catalytic reforming unit. In these extreme cases, it is difficult to maintain the balance of hydrogen supply and demand by relying solely on the hydrogen circulation within the hydrogen network. Under extreme operating conditions, it is necessary to use the hydrogen plant HP2 to supply additional hydrogen to ensure the stable operation of the hydrogen network and the quality of oil and gas products. This design can be operated in a feasible manner over the entire range of uncertain operating parameter fluctuations. Compared with the stochastic programming model, although its total cost is slightly higher, it can ensure the robustness of process operation.

[0197] In view of various uncertain factors such as external market changes and operating parameter fluctuations in the design of large-scale hydrogen networks in refineries, the present invention discloses a two-stage stochastic programming modeling method including flexible design and an efficient iterative optimization solution algorithm. In this model, two-stage stochastic programming is used to deal with uncertain factors related to the external market such as changing hydrogen prices, electricity prices and fuel gas prices, and flexible constraints are applied to deal with uncertain parameters at the operating level in a more robust manner, such as fluctuating hydrogen supply from hydrogen sources and changing hydrogen demand from hydrogen sinks. The objective function adopts the expectation based on multiple scenarios in the stochastic programming problem, and the flexible constraints in the form of max-min-max in the constraints ensure that the actual operation of the hydrogen network is feasible within the entire range of uncertain operating parameter changes. This modeling method that integrates two uncertainty optimization theories is compared with traditional deterministic models and standard stochastic programming models. The target design scheme obtained by the integrated optimization model can be flexibly recycled and cascaded between complex hydrogen network topological structures, while tapping the potential and increasing the efficiency of the hydrogen system, ensuring smooth and safe production and operation.

[0198] Fig. 9 FIG. 1 is a block diagram of a refinery flexible hydrogen network determination device according to an exemplary embodiment. Fig. 9 As shown, the refinery flexible hydrogen network determination device 200 includes:

[0199] A construction module 201 is used to construct a mathematical programming model of a refinery flexible hydrogen network;

[0200] A first determination module 202 is used to determine the flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network;

[0201] A second determination module 203 is used to determine the optimal solution of the objective function of the mathematical programming model according to the flexible constraint condition;

[0202] The third determination module 204 is used to determine a target design scheme of the hydrogen network according to the optimal solution of the objective function, wherein the target design scheme includes a target topology structure and a target operation strategy of the hydrogen network.

[0203] Optionally, the construction module 201 includes:

[0204] Obtain fixed parameters and uncertain parameters of a refinery hydrogen system, each of the uncertain parameters corresponding to a parameter fluctuation range and a probability distribution;

[0205] Determine a target topology of a refinery flexible hydrogen network according to the fixed parameters and the uncertain parameters, and determine an operation strategy corresponding to the target topology in each uncertain scenario;

[0206] According to the historical data of the uncertain parameters, the discrete probability distribution of each uncertain scenario is determined to construct the mathematical programming model.

[0207] Optionally, the second determining module 203 includes:

[0208] Determining an objective function of the mathematical programming model;

[0209] Performing mathematical transformation on the mathematical programming model to simplify the mathematical programming model;

[0210] According to the simplified mathematical programming model and the flexible constraint conditions, iterative calculation is performed through an iterative optimization algorithm to determine the optimal solution of the objective function.

[0211] Optionally, the second determining module 203 includes:

[0212] Determining the overall variation space corresponding to the uncertain parameters of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters;

[0213] Discretizing the overall change space to obtain a plurality of operation strategies corresponding to the target topological structure, each operation strategy corresponding to a discrete probability;

[0214] The feasibility constraints of the mathematical programming model after discretization are reconstructed to obtain a mathematical programming model in the form of max-min-max constraints.

[0215] Optionally, the second determining module 203 includes:

[0216] Determining a corresponding initial critical point set of the uncertain parameters;

[0217] Determining an initial design scheme of the hydrogen network according to the initial critical point set and the flexible constraint condition;

[0218] When the initial design scheme meets the preset feasibility requirements, the target operation strategy of the hydrogen network is determined according to the optimal solution of the objective function.

[0219] Optionally, the second determining module 203 further includes:

[0220] When the initial design solution does not meet the preset feasibility requirements, the initial critical point set is updated according to the overall change space.

[0221] Optionally, the uncertain parameters include operating layer parameters of the hydrogen network, and the overall variation space of the uncertain parameters can be expressed as:

[0222] T={θ|θ N -Δθ≤θ≤θ N +Δθ}

[0223] Where T represents the overall variation space of the uncertain parameters, θ represents the operating layer parameters, and θ N represents the nominal value of the uncertain parameter, and Δθ represents the deviation of the uncertain parameter from the nominal point.

[0224] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0225] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the refinery flexible hydrogen network determination method provided by the present disclosure.

[0226] The present disclosure also provides an electronic device, comprising:

[0227] a memory having a computer program stored thereon;

[0228] The processor is used to execute the computer program in the memory to implement the steps of the refinery flexible hydrogen network determination method provided by the present disclosure.

[0229] Fig.10 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Fig.10 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0230] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned storage method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. These data may include instructions for any application or method used to operate on the electronic device 700, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0231] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned refinery flexible hydrogen network determination method.

[0232] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the refinery flexible hydrogen network determination method described above are implemented. For example, the computer-readable storage medium may be the memory 702 including the program instructions, and the program instructions may be executed by the processor 701 of the electronic device 700 to complete the refinery flexible hydrogen network determination method described above.

[0233] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the refinery flexible hydrogen network determination method when executed by the programmable device.

[0234] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0235] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0236] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for determining a refinery flexible hydrogen network, characterized in that: include: Constructing a mathematical programming model for a refinery flexible hydrogen network; Determining flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network; Determining an optimal solution to the objective function of the mathematical programming model according to the flexible constraint conditions; According to the optimal solution of the objective function, a target design scheme of the hydrogen network is determined, and the target design scheme includes a target topology structure and a target flexible operation strategy of the hydrogen network.

2. The method according to claim 1, characterized in that The mathematical programming model for constructing a refinery flexible hydrogen network includes: Obtain fixed parameters and uncertain parameters of a refinery hydrogen system, each of the uncertain parameters corresponding to a parameter fluctuation range and a probability distribution; Determine a target topology of a refinery flexible hydrogen network according to the fixed parameters and the uncertain parameters, and determine an operation strategy corresponding to the target topology in each uncertain scenario; According to the historical data of the uncertain parameters, the discrete probability of each uncertain scenario is determined to construct the mathematical programming model.

3. The method according to claim 1, characterized in that Determining the optimal solution of the objective function of the mathematical programming model according to the flexible constraint condition includes: Determining an objective function of the mathematical programming model; Performing mathematical transformation on the mathematical programming model to simplify the mathematical programming model; According to the simplified mathematical programming model and the flexible constraint conditions, iterative calculation is performed through an iterative optimization algorithm to determine the optimal solution of the objective function.

4. The method according to claim 2, characterized in that: The mathematical transformation of the mathematical programming model to simplify the mathematical programming model includes: Determining the overall variation space corresponding to the uncertain parameters of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters; Discretizing the overall change space to obtain a plurality of operation strategies corresponding to the target topological structure, each operation strategy corresponding to a discrete probability; The feasibility constraints of the mathematical programming model after discretization are reconstructed to obtain a mathematical programming model in the form of max-min-max constraints.

5. The method according to claim 3, characterized in that: The iterative calculation is performed according to the simplified mathematical programming model and the flexible constraint condition by an iterative optimization algorithm to determine the optimal solution of the objective function, including: Determining a corresponding initial critical point set of the uncertain parameters; Determining an initial design scheme of the hydrogen network according to the initial critical point set and the flexible constraint condition; When the initial design scheme meets the preset feasibility requirements, the target flexible operation strategy of the hydrogen network is determined according to the optimal solution of the objective function.

6. The method according to claim 5, characterized in that The iterative calculation is performed by an iterative optimization algorithm according to the simplified mathematical programming model and the flexible constraint condition to determine the optimal solution of the objective function, and further includes: In the case that the initial design solution does not meet the preset feasibility requirements, the initial critical point set is updated according to the vertices of the overall variation space of the uncertain parameters.

7. The method according to any one of claims 1 to 6, characterized in that: The uncertain parameters include the operating layer parameters of the hydrogen network, and the overall variation space of the uncertain parameters can be expressed as: T={θ|θ N -Δθ≤θ≤θ N +Δθ} Where T represents the overall variation space of the uncertain parameters, θ represents the operating layer parameters, and θ N represents the nominal value of the uncertain parameter, and Δθ represents the deviation value of the uncertain parameter from the nominal point.

8. A refinery flexible hydrogen network determination device, characterized in that: include: A building block for constructing a mathematical programming model for a refinery’s flexible hydrogen network; A first determination module, configured to determine the flexible constraint conditions of the mathematical programming model according to the parameter fluctuation range of the uncertain parameters of the hydrogen network; A second determination module is used to determine the optimal solution of the objective function of the mathematical programming model according to the flexible constraint condition; The third determination module is used to determine a target design scheme of the flexible hydrogen network according to an optimal solution of the objective function, wherein the target design scheme includes a target topology structure and a target flexible operation strategy of the hydrogen network.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

Images