Multi-energy coupling process energy consumption optimization method and device

Abstract

The embodiment of the invention provides a multi-energy coupling process energy consumption optimization method and device, and the method comprises the steps: obtaining an equipment working parameter set of each equipment unit in a multi-energy coupling system, building a multi-energy coupling process energy consumption optimization model of each equipment unit based on the equipment working parameter set, determining a to-be-optimized decision variable according to the model, and carrying out the optimization of the to-be-optimized decision variable. Determining a target function according to the maximum benefit of the whole process and the minimum single-factor energy consumption, and determining constraint conditions according to the processing capacity of the device, the purchase quantity of raw materials, the product yield and the low-hydrocarbon parameters; generating a particle swarm containing a preset number of particles, and setting initial positions and speeds for the particles; the position of the particle is related to the value of the decision variable; and based on the multi-energy coupling process energy consumption optimization model, the objective function and the constraint condition, solving the multi-energy coupling process energy consumption optimization model by using a multi-objective particle swarm optimization algorithm, and determining an optimization result of the value of the decision variable. According to the embodiment of the invention, the energy consumption of the whole plant is reduced under the condition of ensuring the maximum benefit of the whole process.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of energy equipment, in particular to a multi-energy coupling process energy consumption optimization method and device. BACKGROUND

[0002] Traditional fossil energy processing process can cause high-intensity carbon emission, and multi-energy coupling can use low-carbon hydrogen prepared by wind energy, solar energy and biomass energy in the fossil energy processing process, adjust the hydrogen-carbon ratio, improve the carbon element utilization rate and reduce carbon emission.

[0003] However, the multi-energy coupling process is a complex superstructure process, which contains various energies, various processing methods, multiple processing devices and various final products. In the existing scheme, the optimization is mainly carried out to maximize the benefit, the optimization target is single, and the energy consumption cannot be optimized. Therefore, how to reduce the whole plant energy consumption under the condition of ensuring the maximum benefit of the whole process can realize resource saving and intensification, and reduce cost and increase benefit, which is a problem to be solved. SUMMARY

[0004] In view of the defects in the prior art, the embodiment of the present application provides a multi-energy coupling process energy consumption optimization method and device.

[0005] The embodiment of the present application provides a multi-energy coupling process energy consumption optimization method, comprising: acquiring device working parameter sets of each device unit in a multi-energy coupling system, constructing a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter working set; determining a decision variable to be optimized according to the multi-energy coupling process energy consumption optimization model of each device unit, determining a target function according to the maximum benefit of the whole process and the minimum energy consumption of a single factor, and determining a constraint condition according to the device processing capacity, the raw material purchase quantity, the product yield and the low-carbon hydrogen parameter; generating a particle swarm containing a preset number of particles, and setting an initial position and a speed for the particles in the particle swarm; wherein the position of the particle is related to the value of the decision variable; based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, the multi-energy coupling process energy consumption optimization model is solved by using a multi-objective particle swarm optimization algorithm, and the optimization result of the value of the decision variable is determined.

[0006] The embodiment of the present application also provides a multi-energy coupling process energy consumption optimization device, comprising: a construction module, configured to: acquire equipment working parameter sets of each equipment unit in a multi-energy coupling system, and construct a multi-energy coupling process energy consumption optimization model of each equipment unit based on the equipment parameter working sets; a determination module, configured to: determine decision variables to be optimized according to the multi-energy coupling process energy consumption optimization model of each equipment unit, determine a target function according to maximum overall process benefits and minimum single-factor energy consumption, and determine constraint conditions according to device processing capacity, raw material purchase quantity, product yield and low-carbon hydrogen parameters; an initialization module, configured to: generate a particle swarm containing a preset number of particles, and set initial positions and speeds of the particles in the particle swarm; wherein the positions of the particles are related to values of the decision variables; and an optimization module, configured to: solve the multi-energy coupling process energy consumption optimization model by using a multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint conditions, and determine an optimization result of the values of the decision variables.

[0007] The embodiment of the present application also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements steps of the multi-energy coupling process energy consumption optimization method according to any one of the above when executing the program.

[0008] The embodiment of the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program implements steps of the multi-energy coupling process energy consumption optimization method according to any one of the above when executed by a processor.

[0009] The embodiment of the present application also provides a computer program product, comprising a computer program, and the computer program implements steps of the multi-energy coupling process energy consumption optimization method according to any one of the above when executed by a processor.

[0010] The embodiments of the present invention provide a multi-energy coupling process energy consumption optimization method and device. By acquiring a device working parameter set of each device unit in a multi-energy coupling system, a multi-energy coupling process energy consumption optimization model for each device unit is constructed based on the device parameter working set. Decision variables to be optimized are determined according to the multi-energy coupling process energy consumption optimization model of each device unit. An objective function is determined based on maximizing the benefit of the entire process and minimizing the single-factor energy consumption. Constraints are determined based on the device processing capacity, raw material purchase volume, product output, and low-carbon hydrogen parameters. A particle swarm containing a preset number of particles is generated, and initial positions and velocities are set for the particles in the particle swarm. The positions of the particles are related to the values ​​of the decision variables. Based on the multi-energy coupling process energy consumption optimization model, the objective function, and the constraints, a multi-objective particle swarm optimization algorithm is used to solve the multi-energy coupling process energy consumption optimization model, determine the optimization results of the values ​​of the decision variables, and achieve the reduction of plant-wide energy consumption while ensuring the maximum benefit of the entire process. This can achieve resource conservation and intensive use, reduce costs, and increase efficiency, and solve the problem of a single optimization target for the entire process and the inability to optimize energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. 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 any creative work.

[0012] Figure 1 1 is a flow chart of a method for optimizing energy consumption in a multi-energy coupling process according to an embodiment of the present invention;

[0013] Figure 2 is a schematic structural diagram of a multi-energy coupling system provided by an embodiment of the present invention;

[0014] Figure 3 Schematic diagram of the structure of the multi-energy coupling process energy consumption optimization device provided by an embodiment of the present invention;

[0015] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0017] Figure 1is a flowchart of a multi-energy coupling process energy consumption optimization method provided by an embodiment of the present application. As shown in Figure 1 , the method comprises:

[0018] Step S1, obtaining a device working parameter set of each device unit in a multi-energy coupling system, and constructing a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter working set.

[0019] The multi-energy coupling system comprises a low-carbon hydrogen production system and a fossil energy processing system. The device working parameter set corresponding to each device unit in the low-carbon hydrogen production system and the fossil energy processing system is obtained, and the multi-energy coupling process energy consumption optimization model corresponding to each device unit is constructed based on the device working parameter set. The device unit that needs to be processed includes at least one device.

[0020] Each device unit in the multi-energy coupling system comprises a low-carbon hydrogen supply unit, a primary processing unit, a secondary processing unit, and a product blending unit, etc. The low-carbon hydrogen supply unit comprises one or more of solar power generation hydrogen production, wind power generation hydrogen production, and biomass gasification hydrogen production; the low-carbon hydrogen supply unit supplies hydrogen to the hydrogen device; the primary processing unit comprises one or more of crude oil distillation and coal gasification.

[0021] Figure 2 is a structural diagram of a multi-energy coupling system provided by an embodiment of the present application. As shown in Figure 2As shown, the multi-energy coupling system includes a photovoltaic power generation water electrolysis hydrogen production unit 110, a wind power generation water electrolysis hydrogen production unit 120, a crude oil unit 130, a primary processing device unit 140, a secondary processing device unit 150, a utility unit 160, a product blending device unit 170, and a product unit 170. Specifically, the photovoltaic power generation water electrolysis hydrogen production unit 110 uses photovoltaic power generation to power the electrolyzer for water electrolysis, providing an unstable low-carbon hydrogen supply. The wind power generation water electrolysis hydrogen production unit 120 uses wind turbines to generate power to power the electrolyzer for water electrolysis, providing an unstable low-carbon hydrogen supply. The crude oil unit 130 provides crude oil to the primary processing device unit 140. The primary processing device unit 140 includes an atmospheric pressure tower and a vacuum tower to realize the distillation treatment of crude oil. Primary processing unit 140 is connected to secondary processing unit 150, which includes a reforming pre-hydrogenation unit, a continuous reforming unit, a hydrocracking unit, a gasoline hydrofining unit, a jet fuel hydrotreating unit, a diesel hydrotreating unit, a wax oil hydrotreating unit, a residual oil hydrotreating unit, a delayed coking unit, and a pressure swing adsorption unit. Products from secondary processing unit 150 enter product blending unit 170. Product blending unit 170 blends multiple product streams into products 180 that meet regulatory requirements. Products 180 specifically include gasoline, kerosene, diesel, bunker fuel, benzene, liquefied petroleum gas, dry gas, and sulfur. The utility unit provides fuel, electricity, steam, water, gas, air, and auxiliary materials for the primary and secondary processing units.

[0022] The device operating parameter set defines the device operating parameters of each unit, such as the device operating parameters of 110-170. These parameters can be the time series hydrogen supply flow rate of the photovoltaic power generation water electrolysis hydrogen production unit 110, the time series hydrogen supply flow rate of the wind power generation water electrolysis hydrogen production unit 120, the properties, type, and flow rate of the crude oil in the crude oil unit 120, etc.

[0023] Step S2: Determine the decision variables to be optimized based on the multi-energy coupling process energy consumption optimization model of each equipment unit, determine the objective function based on maximizing the benefits of the entire process and minimizing the single-factor energy consumption, and determine the constraints based on the processing capacity of the device, the purchase amount of raw materials, the product output, and the low-carbon hydrogen parameters.

[0024] The decision variables to be optimized are determined based on the multi-energy coupling process energy consumption optimization model of each equipment unit, that is, the decision variables can be determined based on the multi-energy coupling process energy consumption optimization model. Among them, the decision variables are the variables to be optimized in the multi-energy coupling process energy consumption optimization method.

[0025] The objective function is determined based on maximizing the overall process benefit and minimizing the energy consumption of a single factor. Separate expressions can be determined to represent the maximum overall process benefit and the minimum energy consumption of a single factor. The objective function includes both expressions for maximizing the overall process benefit and minimizing the energy consumption of a single factor.

[0026] The constraint conditions are determined according to the device processing capacity, raw material purchase quantity, product yield and low carbon hydrogen parameters. The constraint conditions include constraint information related to the device processing capacity, raw material purchase quantity, product yield and low carbon hydrogen parameters.

[0027] In step S3, a particle swarm containing a preset number of particles is generated, and initial positions and velocities of the particles in the particle swarm are set. The positions of the particles are related to the values of the decision variables.

[0028] The multi-energy coupling process energy consumption optimization is performed by using the multi-objective particle swarm optimization algorithm. Before the multi-energy coupling process energy consumption optimization is performed by using the multi-objective particle swarm optimization algorithm, the related algorithm parameters can be set, such as the number of particles, the number of iterations, the inertia weight and the acceleration factor.

[0029] The decision variables to be optimized are constructed into an n-dimensional vector, and the dimensions of the vector are the same as the number of decision variables. The numerical value of the n-dimensional vector represents the position of the particle.

[0030] A particle swarm containing a preset number of particles is generated according to the number of particles, and the particles in the particle swarm are set with initial positions and velocities in the feasible region. The feasible region can be determined by the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions.

[0031] In step S4, the multi-energy coupling process energy consumption optimization model is solved by using the multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions, and the optimization result of the values of the decision variables is determined.

[0032] Under the conditions of meeting the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions, the multi-energy coupling process energy consumption optimization model is solved by using the multi-objective particle swarm optimization algorithm, the values of the decision variables are determined, and the multi-energy coupling process energy consumption optimization result is obtained. By solving the optimal solution of each decision variable, the production scheme optimal solution with the maximum benefit and the minimum energy consumption is determined.

[0033] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application can realize the reduction of the whole plant energy consumption under the condition of ensuring the maximum whole process benefit, can realize resource saving and intensification, reduce cost and increase benefit, and solves the problems of single whole process optimization target and the incapability of optimizing energy consumption.

[0034] According to the multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application, the multi-energy coupling process energy consumption optimization model is solved by using the multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, and the optimization result of the value of the decision variable is determined, which includes the following steps: the fitness value of each particle in the particle swarm is calculated according to the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, the dominance relationship is judged according to the Pareto principle, and the Pareto non-inferior solution set is formed; for each particle, if the current solution of the particle is better than the individual historical optimal solution in the value of the target function, the individual optimal solution is updated; all non-dominated solutions are found out by traversing the particle swarm, and the current global optimal solution is formed; the speed and position of each particle are updated according to the individual optimal solution and the global optimal solution, and the preset weighting coefficient and the inertia weight; the above steps are iteratively executed, and if the preset maximum iteration number is reached or the preset stop iteration condition is met, the iteration is stopped, the global optimal solution is output, and the optimization result of the value of the decision variable is determined according to the global optimal solution.

[0035] The following is an embodiment step of solving the multi-energy coupling process energy consumption optimization model by using the multi-objective particle swarm optimization algorithm:

[0036] Step 1: determine the multi-energy coupling process energy consumption optimization model, the target function, the constraint condition, and the range of the decision variable;

[0037] Step 2: Set the number of particles, the number of iterations, the inertia weight and the acceleration factor of the algorithm; for example, set the number of particles of the algorithm to 50, the number of iterations to 100, the inertia weight to 0.5, and the acceleration factor to 1.6, which can be adjusted according to actual conditions;

[0038] Step 3: Construct the parameters to be optimized into an n-dimensional vector, denoted as the position of the particle. A certain number of particles are randomly generated and each particle is assigned a random initial position and velocity in the feasible region;

[0039] Step 4: Calculate the fitness value of each particle according to the multi-energy coupling process energy consumption optimization model, the objective function and the constraint condition, and judge the dominance relationship according to the Pareto principle to form a Pareto non-inferior solution set;

[0040] Step 5: Update the individual optimal solution: for each particle, if its current solution is better than the individual historical optimal solution in the objective function value, update the individual optimal solution;

[0041] Step 6: Update the global optimal solution: traverse the entire population to find all non-dominated solutions to form the current global optimal solution set;

[0042] Step 7: According to the individual optimal solution and the global optimal solution, update the speed and position of each particle according to the preset weighting coefficient and inertia weight;

[0043] Step 8: If the maximum number of iterations is reached or other stopping conditions (such as convergence of the solution, diversity, etc.) are met, stop the search and output the global optimal solution set; otherwise, return to step 4 and continue iteration.

[0044] Step 9: After the iteration is completed, output the final solution set as the approximate Pareto optimal solution, and determine the optimization result of the value of the decision variable accordingly.

[0045] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application can calculate the fitness value of each particle in the particle swarm according to the multi-energy coupling process energy consumption optimization model, the objective function and the constraint condition, perform dominance relationship judgment according to the Pareto principle, form a Pareto non-inferior solution set, for each particle, in response to the fact that the current solution of the particle is better than the individual historical optimal solution in the objective function value, update the individual optimal solution, traverse the particle swarm, find all non-dominated solutions, form the current global optimal solution, according to the individual optimal solution and the global optimal solution, update the speed and position of each particle according to the preset weighting coefficient and the inertia weight, iteratively execute the above steps, in response to the fact that the preset maximum iteration number is reached or the preset stop iteration condition is met, stop iteration, output the global optimal solution, and determine the optimization result of the value of the decision variable according to the global optimal solution, so that the multi-energy coupling process energy consumption optimization is realized through the multi-objective particle swarm optimization algorithm, and the accuracy of the optimization result of the decision variable is improved.

[0046] According to the multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application, the decision variable includes device unit processing load, multi-energy coupling scheme, multi-energy coupling ratio, raw material procurement quantity, product output, single device energy consumption, whole plant energy factor and single factor energy consumption.

[0047] According to the multi-energy coupling process energy consumption optimization model of each device unit, the decision variable to be optimized includes device unit processing load, multi-energy coupling scheme, multi-energy coupling ratio, raw material procurement quantity, product output, single device energy consumption, whole plant energy factor and single factor energy consumption. Then, the multi-energy coupling process energy consumption optimization model is solved by using the multi-objective particle swarm optimization algorithm, and the purpose is to solve the optimal solution of each decision variable in the multi-energy coupling process energy consumption optimization model, so as to correspondingly determine the optimization result of the load, multi-energy coupling scheme, multi-energy coupling ratio, raw material procurement quantity, product output, single device energy consumption, whole plant energy factor and single factor energy consumption of each device unit. The multi-objective particle swarm optimization algorithm is used for iterative solving, and after the iteration is completed, the optimization result of the device unit processing load, multi-energy coupling scheme, multi-energy coupling ratio, raw material procurement quantity, product output, single device energy consumption, whole plant energy factor and single factor energy consumption can be obtained, that is, the value of each decision variable after optimization is obtained.

[0048] The multi-energy coupling scheme can include the selection of a low-carbon hydrogen supply unit. The multi-energy coupling ratio can include the allocation of the hydrogen supply amount of various low-carbon hydrogen supply units.

[0049] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application realizes the optimization setting of the decision variable by setting the decision variable to include the device unit processing load, the multi-energy coupling scheme, the multi-energy coupling ratio, the raw material procurement quantity, the product output, the single device energy consumption, the whole plant energy factor and the single factor energy consumption, which is beneficial to improving the effect of multi-energy coupling process energy consumption optimization control.

[0050] According to the multi-energy coupling process energy consumption optimization method provided by the embodiment of the application, the multi-energy coupling process energy consumption optimization model comprises a mass transfer model, a value transfer model and an energy consumption model; wherein the mass transfer model defines the relationship between the feed and the discharge of the device in the equipment unit; the value transfer model defines the transfer relationship of the raw material price in the logistics; and the energy consumption model defines the relationship between the energy consumption of the device in the equipment unit and the consumption amount of different types of energy.

[0051] The multi-energy coupling process energy consumption optimization model of each equipment unit is constructed based on the equipment parameter working set, and the multi-energy coupling process energy consumption optimization model comprises a mass transfer model, a value transfer model and an energy consumption model.

[0052] The mass transfer model defines the relationship between the feed and the discharge of the device in the equipment unit, and the mass transfer model of the device can be expressed as:

[0053]

[0054] wherein, indicates the kth side line mass flow rate of the device i, indicates the input stream mass flow rate of the rth feed of the device i, indicates the yield of the kth side line of the device i.

[0055] The value transfer model defines the transfer relationship of the raw material price in the logistics, and the value transfer model can be expressed as:

[0056]

[0057] wherein, indicates the kth side line value of the device i, indicates the input stream value of the rth feed of the device i, indicates the output stream flow rate of the kth side line of the device i, indicates the input stream flow rate of the rth feed of the device i.

[0058] The energy consumption model defines the relationship between the energy consumption of the device in the equipment unit and the consumption amount of different types of energy, such as the relationship between the fuel, electricity, steam, water, gas, wind and auxiliary materials consumed by the device. The energy consumption model can be expressed as:

[0059]

[0060] wherein, E i indicates the equivalent oil energy consumption of the device i, indicates the consumption amount of the jth energy consumed by the device i, and Factorj Indicates the equivalent standard oil coefficient of the jth energy source.

[0061] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the present invention improves the accuracy of the multi-energy coupling process energy consumption optimization model by setting the multi-energy coupling process energy consumption optimization model to include a mass transfer model, a value transfer model and an energy consumption model.

[0062] According to an embodiment of the present invention, a multi-energy coupling process energy consumption optimization method is provided. The expression of the whole process benefit defines the relationship between the whole plant benefit and the production raw material cost, the consumption material cost, the consumption energy cost and the product benefit. The whole process benefit is expressed as:

[0063]

[0064] Among them, G represents the overall process benefit, m p 、m r 、m u are the mass flow rates of products, raw materials and utilities, respectively, C p 、C r 、C u are the prices of products, raw materials and utilities respectively.

[0065] The expression of single-cause energy consumption defines the relationship between single-cause energy consumption, device energy consumption and energy factor. The single-cause energy consumption is expressed as:

[0066] e ef =e / E f

[0067] Among them, e ef represents the single-cause energy consumption, e represents the device energy consumption, E f Represents the energy factor.

[0068] The objective function is determined based on the maximum benefit of the entire process and the minimum energy consumption of a single cause. That is, the objective function is determined by maximizing the benefit of the entire process and minimizing the energy consumption of a single cause. The objective function can be expressed as:

[0069]

[0070] Min e ef =e / E f

[0071] Single cause energy consumption e ef In the expression of f Calculated by the following formula:

[0072]

[0073] Among them, C iis the processing capacity coefficient of the device i; k i is the energy coefficient of the device i, E c , E w , E sl , E el , E q , F t is the energy factor of the storage and transportation system, the energy factor of the sewage treatment plant, the energy factor of the heat loss, the energy factor of the power transmission and transformation loss, the energy factor of other auxiliary systems and the temperature correction factor, respectively. f The expression of the energy factor E ef defines the relationship between the operating load, the design processing capacity and the energy factor of each device in the multi-energy coupling system. Among them, the energy coefficient is the ratio of the operating load and the design processing capacity.

[0074] The expression of the single-factor energy consumption e ef is calculated by the following formula:

[0075]

[0076] Among them, E j represents the equivalent standard oil consumption of the device consuming the jth energy, and M represents the device feed quantity. The expression of the device energy consumption defines the relationship between the material and energy consumption and the device energy consumption in the multi-energy coupling system.

[0077] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the application improves the accuracy of the target function setting by giving the expressions of the overall process benefit and the single-factor energy consumption.

[0078] According to the multi-energy coupling process energy consumption optimization method provided by the embodiment of the application, the constraint conditions include device processing capacity constraint conditions, raw material purchase quantity constraint conditions, product yield constraint conditions and low-carbon hydrogen parameter constraint conditions; wherein: the device processing capacity constraint conditions define the upper limit and the lower limit of the device processing capacity; the raw material purchase quantity constraint conditions define the upper limit and the lower limit of the raw material purchase quantity; the product yield constraint conditions define the upper limit and the lower limit of the product yield; and the low-carbon hydrogen parameter constraint conditions define the range of the low-carbon hydrogen supply quantity.

[0079] The constraint conditions are determined according to the device processing capacity, the raw material purchase quantity, the product yield and the low-carbon hydrogen parameter. The device processing capacity constraint conditions define the upper limit and the lower limit of the device processing capacity, and the device processing capacity constraint conditions can be expressed as:

[0080]

[0081] Among them, Capacity i represents the processing capacity of the device i, represents the lower limit of the processing capacity of the device i, An upper limit of processing capacity of the device i is represented.

[0082] The upper limit and the lower limit of the processing capacity of the device can be determined according to the design processing capacity of the device.

[0083] Table 1: Design processing capacity data of the device

[0084]

[0085]

[0086] The raw material purchase quantity constraint condition can be represented as:

[0087]

[0088] wherein, Oil k represents the purchase quantity of crude oil k, represents the lower limit of the purchase quantity of crude oil k, represents the upper limit of the purchase quantity of crude oil k.

[0089] The product yield constraint condition can be represented as:

[0090]

[0091] wherein, Product p represents the product yield, represents the lower limit of the product yield, represents the upper limit of the product yield.

[0092] The low-carbon hydrogen parameter constraint condition can be represented as:

[0093]

[0094] wherein, m·μ(i) represents a constant term of the low-carbon hydrogen supply device i, n·λ(i) represents a variable term of the low-carbon hydrogen supply device i, m and n are coefficients, μ(i) represents an invariable part of hydrogen gas supplied by the low-carbon hydrogen supply device i, and λ(i) represents a variable part of hydrogen gas supplied by the low-carbon hydrogen supply device i, H max represents the total low-carbon hydrogen supply quantity.

[0095] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the application improves the accuracy of the constraint conditions by giving the device processing capacity constraint condition, the raw material purchase quantity constraint condition, the product yield constraint condition and the low-carbon hydrogen parameter constraint condition.

[0096] According to the multi-energy coupling process energy consumption optimization method provided by the embodiment of the application, the method further comprises: converting the uncertainty expression of the low-carbon hydrogen parameter constraint condition into a deterministic expression by using a triangular membership function.

[0097] In the above embodiment, the low-carbon hydrogen parameter constraint condition is an uncertain expression, in order to improve the accuracy of optimization of the low-carbon hydrogen related decision variable, the embodiment of the present application utilizes the fuzzy theory to describe the uncertainty of the low-carbon hydrogen flow rate, and converts the uncertain constraint into a certain constraint. The uncertain constraint of the low-carbon hydrogen is converted into a certain constraint through the fuzzy theory, and specifically, the uncertain expression of the low-carbon hydrogen parameter constraint condition can be converted into a certain expression through a triangular membership function.

[0098] The expression of the triangular membership function is as follows:

[0099]

[0100] Wherein, ω represents the value of the triangular membership function, h represents the uncertain parameter, h a represents the lower limit value of the uncertain parameter, h b represents the middle value of the uncertain parameter, h c represents the upper limit value of the uncertain parameter.

[0101] The uncertain expression of the low-carbon hydrogen parameter constraint condition is converted into a certain expression through the triangular membership function, and the certain expression is expressed as:

[0102]

[0103] Wherein, μ a , μ b , μ c is the weight value, and μ a + μ b + μ c = 1. is the boundary point of the triangular membership function of the uncertain parameter (the flow rate of hydrogen in this embodiment) under the alpha cut.

[0104] The multi-energy coupling process energy consumption optimization method provided by the embodiment of the present application converts the uncertain expression of the low-carbon hydrogen parameter constraint condition into a certain expression through the triangular membership function, converts the uncertain constraint of the low-carbon hydrogen parameter constraint condition into a certain constraint, overcomes the defect of inaccurate optimization of the low-carbon hydrogen with strong uncertainty, and improves the accuracy of optimization of the low-carbon hydrogen related decision variable.

[0105] It should be noted that the plurality of preferred embodiments given in the embodiment can be freely combined on the premise that the logic or structure does not conflict with each other, and the present application does not limit this.

[0106] The multi-energy coupling process energy consumption optimization device provided by the embodiment of the present application is described below, and the multi-energy coupling process energy consumption optimization device described below can be correspondingly referred to the multi-energy coupling process energy consumption optimization method described above.

[0107] Figure 3 is a structural schematic diagram of a multi-energy coupling process energy consumption optimization device provided by an embodiment of the present application. Figure 3 As shown in the figure, the device comprises a construction module 10, a determination module 20, an initialization module 30 and an optimization module 40, wherein: the construction module 10 is configured to: acquire a device working parameter set of each device unit in a multi-energy coupling system, and construct a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter working set; the determination module 20 is configured to: determine a decision variable to be optimized according to the multi-energy coupling process energy consumption optimization model of each device unit, determine a target function according to the maximum overall process benefit and the minimum single-factor energy consumption, and determine a constraint condition according to the device processing capacity, the raw material purchase quantity, the product yield and the low-carbon hydrogen parameter; the initialization module 30 is configured to: generate a particle swarm containing a preset number of particles, and set an initial position and a speed for the particles in the particle swarm; wherein the position of the particle is related to the value of the decision variable; and the optimization module 40 is configured to: solve the multi-energy coupling process energy consumption optimization model by using a multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, and determine an optimization result of the value of the decision variable.

[0108] The multi-energy coupling process energy consumption optimization device provided by the embodiment of the present application can realize the reduction of the overall plant energy consumption under the condition of ensuring the maximum overall process benefit, can realize resource saving and intensification, reduce cost and increase benefit, and solves the problems of single optimization target of the overall process and the inability to optimize energy consumption.

[0109] Figure 4 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, as Figure 4As shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communications bus 440. The processor 410 can invoke a logic instruction in the memory 430 to execute a multi-energy coupling process energy consumption optimization method, which includes: obtaining a device parameter set of each device unit in a multi-energy coupling system, and constructing a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter set; determining a decision variable to be optimized according to the multi-energy coupling process energy consumption optimization model of each device unit, determining a target function according to maximum overall process benefit and minimum single-factor energy consumption, and determining a constraint condition according to device processing capacity, raw material purchase quantity, product yield, and low-carbon hydrogen parameters; generating a particle swarm containing a preset number of particles, and setting an initial position and a speed for the particles in the particle swarm; wherein the position of the particle is related to the value of the decision variable; based on the multi-energy coupling process energy consumption optimization model, the target function, and the constraint condition, the multi-energy coupling process energy consumption optimization model is solved by using a multi-objective particle swarm optimization algorithm to determine the optimization result of the value of the decision variable.

[0110] In addition, the logic instruction in the memory 430 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0111] In another aspect, the embodiments of the present application also provide a computer program product, which comprises a computer program, the computer program being stored in a non-transitory computer readable storage medium, and the computer program being executable by a processor to enable a computer to perform the multi-energy coupling process energy consumption optimization method provided by the above method, the method comprising: obtaining a device working parameter set of each device unit in a multi-energy coupling system, and constructing a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter working set; determining a decision variable to be optimized according to the multi-energy coupling process energy consumption optimization model of each device unit, determining a target function according to the maximum overall process benefit and the minimum single-factor energy consumption, and determining a constraint condition according to the device processing capacity, the raw material purchase quantity, the product yield and the low-carbon hydrogen parameter; generating a particle swarm comprising a preset number of particles, and setting an initial position and a speed for the particles in the particle swarm; wherein the position of the particle is related to the value of the decision variable; and solving the multi-energy coupling process energy consumption optimization model by using a multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, to determine an optimization result of the value of the decision variable.

[0112] In another aspect, the embodiments of the present application also provide a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executable by a processor to implement the multi-energy coupling process energy consumption optimization method provided by the above method, the method comprising: obtaining a device working parameter set of each device unit in a multi-energy coupling system, and constructing a multi-energy coupling process energy consumption optimization model of each device unit based on the device parameter working set; determining a decision variable to be optimized according to the multi-energy coupling process energy consumption optimization model of each device unit, determining a target function according to the maximum overall process benefit and the minimum single-factor energy consumption, and determining a constraint condition according to the device processing capacity, the raw material purchase quantity, the product yield and the low-carbon hydrogen parameter; generating a particle swarm comprising a preset number of particles, and setting an initial position and a speed for the particles in the particle swarm; wherein the position of the particle is related to the value of the decision variable; and solving the multi-energy coupling process energy consumption optimization model by using a multi-objective particle swarm optimization algorithm based on the multi-energy coupling process energy consumption optimization model, the target function and the constraint condition, to determine an optimization result of the value of the decision variable.

[0113] The device embodiments described above are only schematic, wherein the units illustrated as separate components may or may not be physically separate, and the components illustrated as a unit may or may not be physical units, that is, they may be located in one place or distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. Those skilled in the art can understand and implement without creative labor.

[0114] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary general hardware platforms through the description of the above embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing energy consumption in a multi-energy coupling process, characterized in that: include: Acquire a device operating parameter set of each device unit in the multi-energy coupling system, and construct a multi-energy coupling process energy consumption optimization model for each device unit based on the device parameter working set; Determine the decision variables to be optimized based on the multi-energy coupling process energy consumption optimization model of each equipment unit, determine the objective function based on maximizing the benefits of the entire process and minimizing the single-factor energy consumption, and determine the constraints based on the processing capacity of the device, the purchase amount of raw materials, the product output and the low-carbon hydrogen parameters; generating a particle swarm comprising a preset number of particles, and setting initial positions and velocities for the particles in the particle swarm; wherein the positions of the particles are related to the values ​​of the decision variables; Based on the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions, the multi-objective particle swarm optimization algorithm is used to solve the multi-energy coupling process energy consumption optimization model to determine the optimization results of the values ​​of the decision variables.

2. The method for optimizing energy consumption in a multi-energy coupling process according to claim 1, characterized in that: The method of solving the multi-energy coupling process energy consumption optimization model based on the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions using a multi-objective particle swarm optimization algorithm to determine the optimization result of the value of the decision variable includes: Calculating the fitness value of each particle in the particle swarm according to the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions, and determining the dominance relationship according to the Pareto principle to form a Pareto non-inferior solution set; For each particle, in response to the current solution of the particle being better than the individual historical optimal solution in terms of the objective function value, updating the individual optimal solution; Traverse the particle swarm, find all non-dominated solutions, and form the current global optimal solution; According to the individual optimal solution and the global optimal solution, the speed and position of each particle are updated according to a preset weighting coefficient and inertia weight; The above steps are iteratively performed. In response to reaching a preset maximum number of iterations or satisfying a preset stopping condition for iteration, the iteration is stopped, the global optimal solution is output, and the optimization result of the value of the decision variable is determined according to the global optimal solution.

3. The method for optimizing energy consumption in a multi-energy coupling process according to claim 1, characterized in that: The decision variables include equipment unit processing load, multi-energy coupling scheme, multi-energy coupling ratio, raw material procurement volume, product output, single device energy consumption, plant-wide energy factor and single-factor energy consumption.

4. The method for optimizing energy consumption in a multi-energy coupling process according to claim 1, characterized in that: The multi-energy coupling process energy consumption optimization model includes a mass transfer model, a value transfer model and an energy consumption model; wherein: The mass transfer model defines the relationship between the feed and discharge of the device in the equipment unit; The value transfer model defines the transfer relationship of raw material prices in logistics; The energy consumption model defines the relationship between the energy consumption of the devices in the equipment unit and the consumption of different types of energy.

5. The method for optimizing energy consumption in a multi-energy coupling process according to claim 1, characterized in that: The whole process benefit is expressed as: Among them, G represents the overall process benefit, m p 、m r 、m u are the mass flow rates of products, raw materials and utilities, respectively, C p 、C r 、C u are the prices of products, raw materials and utilities respectively; The single-cause energy consumption is expressed as: And ef =e / E f Among them, e ef represents the single-cause energy consumption, e represents the device energy consumption, E f Represents the energy factor.

6. The method for optimizing energy consumption in a multi-energy coupling process according to claim 1, characterized in that: The constraints include device processing capacity constraints, raw material purchase quantity constraints, product output constraints, and low-carbon hydrogen parameter constraints; among which: The device processing capacity constraint defines the upper and lower limits of the device processing capacity; The raw material purchase quantity constraint condition defines the upper and lower limits of the raw material purchase quantity; The product output constraint defines the upper and lower limits of the product output; The low-carbon hydrogen parameter constraint defines the range of the low-carbon hydrogen supply.

7. The method for optimizing energy consumption in a multi-energy coupling process according to claim 6, characterized in that: The method further comprises: The uncertainty expression of the low carbon hydrogen parameter constraint condition is converted into a deterministic expression using a triangle membership function.

8. A multi-energy coupling process energy consumption optimization device, characterized in that: include: A construction module is used to: obtain a device working parameter set of each device unit in the multi-energy coupling system, and construct an energy consumption optimization model of the multi-energy coupling process of each device unit based on the device parameter working set; a determination module, configured to: determine the decision variables to be optimized based on the multi-energy coupling process energy consumption optimization model of each equipment unit, determine the objective function based on maximizing the benefits of the entire process and minimizing the energy consumption of a single factor, and determine the constraint conditions based on the processing capacity of the device, the purchase amount of raw materials, the product output, and the low-carbon hydrogen parameters; An initialization module is used to: generate a particle swarm including a preset number of particles, and set initial positions and velocities for the particles in the particle swarm; wherein the positions of the particles are related to the values ​​of the decision variables; The optimization module is used to solve the multi-energy coupling process energy consumption optimization model based on the multi-energy coupling process energy consumption optimization model, the objective function and the constraint conditions using a multi-objective particle swarm optimization algorithm to determine the optimization result of the value of the decision variable.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the multi-energy coupling process energy consumption optimization method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-energy coupling process energy consumption optimization method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the multi-energy coupling process energy consumption optimization method according to any one of claims 1 to 7 are implemented.

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