Layered planning method based on CCUS supercritical carbon dioxide pipe network

Abstract

The invention relates to the technical field of carbon capture, utilization and storage, and particularly discloses a hierarchical planning method based on a CCUS supercritical carbon dioxide pipe network. The method comprises the following steps: establishing a hierarchical model composed of a source-sink matching planning layer and a pipe network design optimization layer; in the upper layer optimization, the minimum connection distance between a carbon source node and a carbon sink node is taken as a target, and a matching scheme and a carbon dioxide conveying amount are determined through node self-configuration prohibition constraint, a carbon source uniqueness constraint, a flow balance constraint and the like; in the lower-layer optimization, the pipeline design parameters are optimized with the minimum total investment cost of the pipe network as the target, and the optimal design scheme of the supercritical carbon dioxide pipe network is obtained. A wild oat algorithm is adopted to solve the model, an upper layer outputs a matching scheme and distance parameters and inputs the matching scheme and the distance parameters into a lower layer, and finally a pipe network construction scheme and cost data are generated. According to the method, through two-stage decoupling optimization, the collaboration problem of source-sink matching and pipeline design is solved, the investment cost of a pipe network is effectively reduced, and the economical efficiency of a CCUS system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon capture, utilization and storage pipeline network systems, and in particular to a hierarchical planning method based on CCUS supercritical carbon dioxide pipeline networks. Background Art

[0002] Carbon capture, utilization, and storage (CCUS) technology is a core path to achieving deep decarbonization. Its pipeline network bears the crucial task of transporting CO2 from the capture point to the storage or utilization point. Supercritical CO2, due to its high density and low viscosity, is an ideal phase for pipeline transportation, but pipeline network planning presents significant challenges. On the one hand, multiple dispersed carbon sources (such as thermal power plants and steel mills) must be matched with limited geological storage sites (such as depleted oil fields and saline aquifers). This matching strategy directly impacts pipeline topology and transmission distance. Traditional empirical matching methods can easily lead to increased pipeline circuitry, increasing construction costs.

[0003] Supercritical pipeline design must meet multiple engineering constraints: the pressure must be continuously higher than the critical pressure, and the temperature must be higher than the critical temperature to avoid water hammer accidents caused by gas-liquid two-phase flow; the pipeline structural parameters must comply with the ASME B31.4 standard to ensure explosion safety under high pressure. Existing technologies usually separate source-sink matching from pipeline design, first generating a matching solution based on distance minimization, and then independently designing pipeline parameters. This split approach ignores the strong coupling relationship between the two stages - the pipeline flow determined by the matching solution directly affects the optimal pipe diameter selection, and the pipeline cost constraints are not fed back to the matching layer, which may result in the selection of long-distance low-flow paths, resulting in worse economic performance.

[0004] Therefore, a hierarchical planning method for collaborative optimization matching and design is urgently needed to minimize the full life cycle cost while ensuring the safety of supercritical carbon dioxide transportation. Summary of the Invention

[0005] In response to the limitations of current CCUS system source-sink matching and supercritical pipeline design optimization, the present invention discloses a hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network, including the following specific steps: S1: Establishing a hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network, the hierarchical planning model includes a CCUS system source-sink matching planning layer and a supercritical carbon dioxide pipeline network design optimization layer; S2: Obtain basic data of the CCUS system, establish an upper-layer optimization model based on the CCUS system source-sink matching planning layer, and determine the decision variables of the optimization model; The objective function of the upper optimization model is to minimize the distance between the matching carbon source nodes and carbon sink nodes in the CCUS system. The objective function expression is as follows: Where i is the carbon source node; I is the set of carbon source nodes; j is the carbon sink node; J is the set of carbon sink nodes; l is the distance between the matching carbon source node i and carbon sink node j in the CCUS system; x i，j is the matching variable between carbon source node i and carbon sink node j. If carbon source node i matches carbon sink node j, then x i，j is 1, otherwise it is 0; a i is the horizontal coordinate of carbon source node i; a j is the horizontal coordinate of carbon sink node j; b i is the vertical coordinate of carbon source node i; b j is the ordinate of carbon sink node j; The upper optimization model constraints include node self-allocation prohibition constraints, carbon source node uniqueness constraints, carbon source node capture constraints, carbon sink node injection constraints, node flow balance constraints, non-negativity constraints and carbon sink node demand constraints; The decision variable of the upper optimization model is the matching variable x between the carbon source node i and the carbon sink node j. i，j , the amount of carbon dioxide q transported from the matching carbon source node i to the carbon sink node j i，j ; S3: Obtaining basic data of the supercritical carbon dioxide pipeline network, establishing a lower-layer optimization model based on the supercritical carbon dioxide pipeline network design optimization layer, and determining decision variables of the optimization model; The lower optimization model is to minimize the total investment cost of the supercritical carbon dioxide pipeline network. The total investment cost of the supercritical carbon dioxide pipeline network includes the pipe purchase cost, pipeline construction cost, pipeline maintenance cost and pipeline pressurization cost. The objective function expression is: Where, f pu The purchase cost of pipes; co is the pipeline construction cost; f ma is the pipeline maintenance cost; f pr the cost of pressurizing the pipeline; The lower-level optimization model constraint nodes - pipeline flow balance constraint, pipeline flow velocity constraint, pipeline wall thickness constraint, pipeline outer diameter constraint, pipeline length constraint, pipeline uniqueness constraint, pipeline hydraulic constraint and pipeline thermal constraint; The decision variable of the lower optimization model is the construction variable X between the carbon source node i and the carbon sink node j. i，j，m ; The length l of the pipe m between the carbon source node i and the carbon sink node j i，j，m ; The outer diameter of the pipe m between the carbon source node i and the carbon sink node j D i , j，m ; The wall thickness δ of the pipe m between the carbon source node i and the carbon sink node j i，j，m ; S4: using the Wild Oats algorithm to solve the hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network; The solution of the upper optimization model includes the matching scheme of carbon source node i and carbon sink node j in the CCUS system, the connection distance between the matched carbon source node i and carbon sink node j, and the amount of carbon dioxide transported from the matched carbon source node i to the carbon sink node j; The solution of the upper optimization model is input into the lower optimization model as a known parameter; The solution results of the lower-level optimization model include the pipeline construction plan between the carbon source node i and the carbon sink node j, the pipeline length between the carbon source node i and the carbon sink node j, the outer diameter of the pipeline between the carbon source node i and the carbon sink node j, the wall thickness of the pipeline between the carbon source node i and the carbon sink node j, and the total investment cost of the supercritical carbon dioxide pipeline network.

[0006] Preferably, the basic data of the CCUS system described in S2 include the coordinates of the carbon source node, the coordinates of the carbon sink node, the economic capture capacity of the carbon source node, the maximum capture capacity of the carbon source node, the economic injection capacity of the carbon sink node, the maximum injection capacity of the carbon sink node and the total demand of the carbon sink node.

[0007] Preferably, the node self-configuration prohibition constraint expression described in S2 is: Where x i，i is the matching variable of carbon source node i itself; x j，j is the matching variable of carbon sink node j itself; qi,i is the amount of carbon dioxide captured by carbon source node i itself; q j，j is the amount of carbon dioxide transported to the carbon sink node j; The unique constraint expression of the carbon source node is: The carbon source node capture constraint expression is: Where S i min is the economic capture capacity of carbon source node i; S i max is the maximum capture capacity of carbon source node i; The carbon sink node injection constraint expression is: Where, Cmin j is the economic capture capacity of carbon sink node j; Cmax j is the maximum capture capacity of carbon sink node j; The node traffic balance constraint expression is: Where q in no Inject traffic into the node;q out no The outflow traffic of the node; no is the CCUS system node; Node is the collection of CCUS system nodes; The non-negativity constraint expression is: The carbon sink node demand constraint expression is: Where Z j is the carbon dioxide demand of carbon sink node j.

[0008] Preferably, the basic data of the supercritical carbon dioxide pipeline network described in S3 include: pipeline economic parameters, pipeline operating condition boundary data, pipeline design parameters and ambient temperature; The pipeline economic parameters include the purchase cost of pipe per unit length, the construction cost per unit length of pipeline, the maintenance cost per unit length of pipeline, the maintenance cost of compressor and the pipeline pressurization cost; The pipeline operating condition boundary data includes pipeline economic flow rate, pipeline safety flow rate, carbon source node pressure, and carbon sink node delivery pressure; The pipeline design parameters include pipeline steel density, pipeline design pressure, pipeline pipe minimum yield strength, pipeline weld coefficient and pipeline strength design coefficient.

[0009] Preferably, the pipe purchase cost expression in S3 is: Where, X i，j，m is the construction variable of pipeline m between carbon source node i and carbon sink node j. If pipeline m needs to be built between carbon source node i and carbon sink node j, the construction variable value is 1, otherwise it is 0; k is the purchase cost of pipe per unit length; ρm is the density of pipeline steel; π is the circumference of pi; D i，j，m is the outer diameter of the pipe m between the carbon source node i and the carbon sink node j; δ i，j，m is the outer diameter of the pipe m between the carbon source node i and the carbon sink node j; L i，j，m is the length of the pipe m between the carbon source node i and the carbon sink node j; The pipe construction cost expression is: Where γ is the construction cost per unit length of pipeline; The pipeline maintenance cost expression is: Where θ is the maintenance cost per unit length of pipeline; λ is the maintenance cost of compressor; the boost variable Y of pipeline m between carbon source node i and carbon sink node j is i，j，m , if the pipeline m between the carbon source node i and the carbon sink node j needs to be pressurized, the pressurization variable value is 1, otherwise it is 0; The pipeline pressurization cost expression is: Where, τ is the pipeline pressurization cost; Preferably, the node-pipeline flow balance constraint expression described in S3 is: Where Q i，j，m is the carbon dioxide flow rate of pipeline m between carbon source node i and carbon sink node j; The pipeline flow rate constraint expression is: Where V i，j，m is the flow rate of the pipe m between the carbon source node i and the carbon sink node j; V min i,j,m is the economic flow rate of pipeline m between carbon source node i and carbon sink node j; V max i,j,m is the safe flow rate of pipeline m between carbon source node i and carbon sink node j; The pipeline wall thickness constraint expression is: Where, P m is the design pressure of pipeline m; σ m is the minimum yield strength of the pipe material of pipe m; ω m is the weld coefficient of pipe m; E m is the strength design factor of the pipeline; The pipeline inner diameter constraint expression is: Where, d i，j，m is the inner diameter of the pipe m between the carbon source node i and the carbon sink node j; The pipeline length constraint expression is: The pipeline uniqueness constraint expression is: The pipeline hydraulic constraint expression is: Where, ρ CO2 is the density of supercritical carbon dioxide; g is the acceleration of gravity; R is the gas constant; M air is the relative molecular mass of air; Z0 is the compression factor of carbon dioxide under standard working conditions; T0 is the standard working temperature; P0 is the standard working pressure; P i is the carbon source node pressure; P Z i,j,m is the terminal pressure of the pipeline m between the carbon source node i and the carbon sink node j; Z i，j，m is the compression factor of carbon dioxide in the pipeline m between the carbon source node i and the carbon sink node j under the operating conditions; T i，j，m is the operating temperature of the pipeline m between the carbon source node i and the carbon sink node j; ρ r is the relative density of carbon dioxide; f i，j，m is the hydraulic friction factor of the pipe m between the carbon source node i and the carbon sink node j; Re i，j，m is the Reynolds number of the pipe m between the carbon source node i and the carbon sink node j; μ CO2 is the viscosity of supercritical carbon dioxide; P r j Required delivery pressure for carbon sink node j; The pipeline thermal constraint expression is: Where, T i is the temperature of node i; T e is the ambient temperature; H is the temperature drop coefficient; K is the total heat transfer coefficient; C is the specific heat capacity of supercritical carbon dioxide; T min is the critical temperature of carbon dioxide; T max Design temperature for the pipe.

[0010] Preferably, the specific steps of the wild oat algorithm described in S4 include: S401: Set the population size N and the maximum number of iterations Timax, and randomly initialize the decision variables of the optimization model to form the initial solution of the optimization problem as the initial population. The random initialization formula is as follows: Where s m,n is an individual of the wild oat population within the population; rand is a random number in the interval [0,1]; ub is the upper bound of the solution space; lb is the lower bound of the solution space; S402: Calculating algorithm characteristic parameters, wherein the algorithm characteristic parameters include wild oat mass coefficient, wild oat main awn length coefficient, wild oat propagation coefficient, and dynamic adjustment factor; The calculation formula of the wild oat mass coefficient is as follows: Where M is the quality coefficient of wild oats; dim is the target problem dimension; The calculation formula of the main awn length coefficient of wild oats is as follows: Where, O is the main awn length coefficient of wild oats; The wild oat propagation coefficient calculation formula is as follows: Where R is the main awn length coefficient of wild oats; The dynamic adjustment factor calculation formula is as follows: Where U is the main awn length coefficient of wild oats; Ti is the current iteration number; S403: Generate a random number w1 in the range [0,1]. If w1 is greater than 0.5, the exploration phase begins. The wild oat population individual update formula is as follows: Where W1 is the random exploration factor; S t+1 (n) is the position of the wild oat population at the t+1th iteration; S t (n) is the position of the wild oat population at the tth iteration; S best is the optimal position of wild oat population; mod is the modulo operation; S404: If w1 is less than 0.5, the process enters the development phase and generates a random number w2 in the range [0,1]. If w2 is greater than 0.5, the process enters the barrier development phase. The wild oat population individual update formula is as follows: Where W2 is the first random development factor; W3 is the random barrier factor; rdim(-W2,W2) is a matrix with the same dimension as the target problem, and its value range is between -W2 and W2; S405: If w2 is greater than 0.5, the barrier-free development phase begins. The wild oat population individual update formula is as follows: Where W4 is the elastic coefficient of the main awn of wild oats; W5 is the coefficient of variation of the main awn length of wild oats; W6 is the angle between the main awn ejection angle of wild oats and the ground; W7 is the air resistance coefficient; W8 is the barrier-free factor; rdim(-W4,W4) is a matrix with the same dimension as the target problem, and its value range is between -W4 and W4. S406: After the individuals in the complete population are updated, the fitness of the individuals in the population is calculated using the optimization model objective function as the fitness function. The individual with the highest fitness is defined as the optimal individual, and the current number of iterations Ti is checked to see if it satisfies Ti≥Ti. max If it is satisfied, the feasible solution represented by the optimal individual is output as the optimal solution. If it is not satisfied, return to S403. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the attached figure: Figure 1 Flowchart of the hierarchical planning method for CCUS supercritical carbon dioxide pipeline network Figure 2 Flowchart for solving the Wild Oats algorithm Figure 3 Layout of carbon source nodes and carbon sink nodes for CCUS system Figure 4 Provide a matching solution for carbon source nodes and carbon sink nodes in CCUS system; DETAILED DESCRIPTION

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0013] like Figure 1 As shown, the present invention discloses a hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network, including the following specific steps: S1: Establishing a hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network, the hierarchical planning model includes a CCUS system source-sink matching planning layer and a supercritical carbon dioxide pipeline network design optimization layer; S2: Obtain basic data of the CCUS system, establish an upper-layer optimization model based on the CCUS system source-sink matching planning layer, and determine the decision variables of the optimization model; The objective function of the upper optimization model is to minimize the distance between the matching carbon source nodes and carbon sink nodes in the CCUS system. The objective function expression is as follows: Where i is the carbon source node; I is the set of carbon source nodes; j is the carbon sink node; J is the set of carbon sink nodes; l is the distance between the matching carbon source node i and carbon sink node j in the CCUS system; x i，j is the matching variable between carbon source node i and carbon sink node j. If carbon source node i matches carbon sink node j, then x i，j is 1, otherwise it is 0; a i is the horizontal coordinate of carbon source node i; a j is the horizontal coordinate of carbon sink node j; b i is the vertical coordinate of carbon source node i; b j is the ordinate of carbon sink node j; The upper optimization model constraints include node self-allocation prohibition constraints, carbon source node uniqueness constraints, carbon source node capture constraints, carbon sink node injection constraints, node flow balance constraints, non-negativity constraints and carbon sink node demand constraints; The decision variable of the upper optimization model is the matching variable x between the carbon source node i and the carbon sink node j. i，j , the amount of carbon dioxide q transported from the matching carbon source node i to the carbon sink node j i，j ; S3: Obtaining basic data of the supercritical carbon dioxide pipeline network, establishing a lower-layer optimization model based on the supercritical carbon dioxide pipeline network design optimization layer, and determining decision variables of the optimization model; The lower optimization model is to minimize the total investment cost of the supercritical carbon dioxide pipeline network. The total investment cost of the supercritical carbon dioxide pipeline network includes the pipe purchase cost, pipeline construction cost, pipeline maintenance cost and pipeline pressurization cost. The objective function expression is: Where, f pu The purchase cost of pipes; co is the pipeline construction cost; f ma is the pipeline maintenance cost; f pr the cost of pressurizing the pipeline; The lower-level optimization model constraint nodes - pipeline flow balance constraint, pipeline flow velocity constraint, pipeline wall thickness constraint, pipeline outer diameter constraint, pipeline length constraint, pipeline uniqueness constraint, pipeline hydraulic constraint and pipeline thermal constraint; The decision variable of the lower optimization model is the construction variable X between the carbon source node i and the carbon sink node j.i，j，m ; The length l of the pipe m between the carbon source node i and the carbon sink node j i，j，m ; The outer diameter of the pipe m between the carbon source node i and the carbon sink node j D i , j，m ; The wall thickness δ of the pipe m between the carbon source node i and the carbon sink node j i，j，m ; S4: using the Wild Oats algorithm to solve the hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network; The solution of the upper optimization model includes the matching scheme of carbon source node i and carbon sink node j in the CCUS system, the connection distance between the matched carbon source node i and carbon sink node j, and the amount of carbon dioxide transported from the matched carbon source node i to the carbon sink node j; The solution of the upper optimization model is input into the lower optimization model as a known parameter; The solution results of the lower-level optimization model include the pipeline construction plan between the carbon source node i and the carbon sink node j, the pipeline length between the carbon source node i and the carbon sink node j, the outer diameter of the pipeline between the carbon source node i and the carbon sink node j, the wall thickness of the pipeline between the carbon source node i and the carbon sink node j, and the total investment cost of the supercritical carbon dioxide pipeline network.

[0014] In one embodiment, the CCUS system basic data described in S2 includes carbon source node coordinates, carbon sink node coordinates, carbon source node economic capture capacity, carbon source node maximum capture capacity, carbon sink node economic injection capacity, carbon sink node maximum injection capacity and carbon sink node total demand.

[0015] In one embodiment, the node self-configuration prohibition constraint expression described in S2 is: Where x i，i is the matching variable of carbon source node i itself; x j，j is the matching variable of carbon sink node j itself; qi,i is the amount of carbon dioxide captured by carbon source node i itself; q j，j is the amount of carbon dioxide transported to the carbon sink node j; The unique constraint expression of the carbon source node is: The carbon source node capture constraint expression is: Where S i min is the economic capture capacity of carbon source node i; S i max is the maximum capture capacity of carbon source node i; The carbon sink node injection constraint expression is: Where, Cmin j is the economic capture capacity of carbon sink node j; Cmax j is the maximum capture capacity of carbon sink node j; The node traffic balance constraint expression is: Where q in no Inject traffic into the node;q out no The outflow traffic of the node; no is the CCUS system node; Node is the collection of CCUS system nodes; The non-negativity constraint expression is: The carbon sink node demand constraint expression is: Where Z j is the carbon dioxide demand of carbon sink node j.

[0016] In one embodiment, the basic data of the supercritical carbon dioxide pipeline network described in S3 include: pipeline economic parameters, pipeline operating condition boundary data, pipeline design parameters and ambient temperature; The pipeline economic parameters include the purchase cost of pipe per unit length, the construction cost per unit length of pipeline, the maintenance cost per unit length of pipeline, the maintenance cost of compressor and the pipeline pressurization cost; The pipeline operating condition boundary data includes pipeline economic flow rate, pipeline safety flow rate, carbon source node pressure, and carbon sink node delivery pressure; The pipeline design parameters include pipeline steel density, pipeline design pressure, pipeline pipe minimum yield strength, pipeline weld coefficient and pipeline strength design coefficient.

[0017] In one embodiment, the pipe purchase cost expression in S3 is: Where, X i，j，m is the construction variable of pipeline m between carbon source node i and carbon sink node j. If pipeline m needs to be built between carbon source node i and carbon sink node j, the construction variable value is 1, otherwise it is 0; k is the purchase cost of pipe per unit length; ρm is the density of pipeline steel; π is the circumference of pi; D i，j，m is the outer diameter of the pipe m between the carbon source node i and the carbon sink node j; δ i，j，m is the outer diameter of the pipe m between the carbon source node i and the carbon sink node j; Li，j，m is the length of the pipe m between the carbon source node i and the carbon sink node j; The pipe construction cost expression is: Where γ is the construction cost per unit length of pipeline; The pipeline maintenance cost expression is: Where θ is the maintenance cost per unit length of pipeline; λ is the maintenance cost of compressor; the boost variable Y of pipeline m between carbon source node i and carbon sink node j is i，j，m , if the pipeline m between the carbon source node i and the carbon sink node j needs to be pressurized, the pressurization variable value is 1, otherwise it is 0; The pipeline pressurization cost expression is: Where, τ is the pipeline pressurization cost; In one embodiment, the node-pipeline flow balance constraint expression described in S3 is: Where Q i，j，m is the carbon dioxide flow rate of pipeline m between carbon source node i and carbon sink node j; The pipeline flow rate constraint expression is: Where V i，j，m is the flow rate of the pipe m between the carbon source node i and the carbon sink node j; V min i,j,m is the economic flow rate of pipeline m between carbon source node i and carbon sink node j; V max i,j,m is the safe flow rate of pipeline m between carbon source node i and carbon sink node j; The pipeline wall thickness constraint expression is: Where, P m is the design pressure of pipeline m; σ m is the minimum yield strength of the pipe material of pipe m; ω m is the weld coefficient of pipe m; E m is the strength design factor of the pipeline; The pipeline inner diameter constraint expression is: Where, d i，j，m is the inner diameter of the pipe m between the carbon source node i and the carbon sink node j; The pipeline length constraint expression is: The pipeline uniqueness constraint expression is: The pipeline hydraulic constraint expression is: Where, ρ CO2 is the density of supercritical carbon dioxide; g is the acceleration of gravity; R is the gas constant; M air is the relative molecular mass of air; Z0 is the compression factor of carbon dioxide under standard working conditions; T0 is the standard working temperature; P0 is the standard working pressure; P i is the carbon source node pressure; P Z i,j,m is the terminal pressure of the pipeline m between the carbon source node i and the carbon sink node j; Z i，j，m is the compression factor of carbon dioxide in the pipeline m between the carbon source node i and the carbon sink node j under the operating conditions; T i，j，m is the operating temperature of the pipeline m between the carbon source node i and the carbon sink node j; ρ r is the relative density of carbon dioxide; f i，j，m is the hydraulic friction factor of the pipe m between the carbon source node i and the carbon sink node j; Re i，j，m is the Reynolds number of the pipe m between the carbon source node i and the carbon sink node j; μ CO2 is the viscosity of supercritical carbon dioxide; P r j Required delivery pressure for carbon sink node j; The pipeline thermal constraint expression is: Where, T i is the temperature of node i; T e is the ambient temperature; H is the temperature drop coefficient; K is the total heat transfer coefficient; C is the specific heat capacity of supercritical carbon dioxide; T min is the critical temperature of carbon dioxide; T max Design temperature for the pipe.

[0018] In one embodiment, the specific steps of the wild oat algorithm described in S4 include: S401: Set the population size N and the maximum number of iterations Timax, and randomly initialize the decision variables of the optimization model to form the initial solution of the optimization problem as the initial population. The random initialization formula is as follows: Where s m,n is an individual of the wild oat population within the population; rand is a random number in the interval [0,1]; ub is the upper bound of the solution space; lb is the lower bound of the solution space; S402: Calculating algorithm characteristic parameters, wherein the algorithm characteristic parameters include wild oat mass coefficient, wild oat main awn length coefficient, wild oat propagation coefficient, and dynamic adjustment factor; The calculation formula of the wild oat mass coefficient is as follows: Where M is the quality coefficient of wild oats; dim is the target problem dimension; The calculation formula of the main awn length coefficient of wild oats is as follows: Where, O is the main awn length coefficient of wild oats; The wild oat propagation coefficient calculation formula is as follows: Where R is the main awn length coefficient of wild oats; The dynamic adjustment factor calculation formula is as follows: Where U is the main awn length coefficient of wild oats; Ti is the current iteration number; S403: Generate a random number w1 in the range [0,1]. If w1 is greater than 0.5, the exploration phase begins. The wild oat population individual update formula is as follows: Where W1 is the random exploration factor; S t+1 (n) is the position of the wild oat population at the t+1th iteration; S t (n) is the position of the wild oat population at the tth iteration; S best is the optimal position of wild oat population; mod is the modulo operation; S404: If w1 is less than 0.5, the process enters the development phase and generates a random number w2 in the range [0,1]. If w2 is greater than 0.5, the process enters the barrier development phase. The wild oat population individual update formula is as follows: Where W2 is the first random development factor; W3 is the random barrier factor; rdim(-W2,W2) is a matrix with the same dimension as the target problem, and its value range is between -W2 and W2; S405: If w2 is greater than 0.5, the barrier-free development phase begins. The wild oat population individual update formula is as follows: Where W4 is the elastic coefficient of the main awn of wild oats; W5 is the coefficient of variation of the main awn length of wild oats; W6 is the angle between the main awn ejection angle of wild oats and the ground; W7 is the air resistance coefficient; W8 is the barrier-free factor; rdim(-W4,W4) is a matrix with the same dimension as the target problem, and its value range is between -W4 and W4. S406: After the individuals in the complete population are updated, the fitness of the individuals in the population is calculated using the optimization model objective function as the fitness function. The individual with the highest fitness is defined as the optimal individual, and the current number of iterations Ti is checked to see if it satisfies Ti≥Ti. max If it is satisfied, the feasible solution represented by the optimal individual is output as the optimal solution. If it is not satisfied, return to S403.

[0019] In a specific embodiment, a small domestic CCUS system is used as an example to optimize the CCUS system source-sink matching and supercritical pipeline design using a hierarchical planning method based on a CCUS supercritical carbon dioxide pipeline network. The CCUS system consists of 12 carbon source nodes and 3 carbon sink nodes. The total demand for carbon sink nodes is 5.38 tons. The layout of the CCUS system carbon source nodes and carbon sink nodes is as follows: Figure 3 As shown in Table 1, the known parameters of carbon source nodes and carbon sink nodes are shown in Table 1.

[0020] Table 1 Known parameters of carbon source nodes and carbon sink nodes The known parameters are imported into the upper optimization model, and the optimal source-sink matching scheme of the CCUS system is obtained by solving the wild oat algorithm. The matching scheme of the carbon source nodes and carbon sink nodes of the system is as follows: Figure 4As shown in Table 2, the minimum total distance between the matching carbon source nodes and carbon sink nodes is 595 km. Carbon sink node 1 injects 2.20 tons of carbon dioxide, carbon sink node 2 injects 1.70 tons of carbon dioxide, and carbon sink node 3 injects 1.48 tons of carbon dioxide. The amount of carbon dioxide transported from each carbon source node to the carbon sink node is shown in Table 2.

[0021] Table 2 Amount of carbon dioxide transported from each carbon source node to the carbon sink node The nominal diameters of the supercritical CO2 pipelines in this CCUS system include 114 mm, 168 mm, and 219 mm. The pipes are made of 316L steel with a density of 7.98 g / cm3 and a yield strength of 117 MPa. The purchase costs per unit length of pipe are 300,000 RMB / km, 450,000 RMB / km, and 580,000 RMB / km, respectively. The construction cost per unit length of pipeline is 550,000 RMB / km, the maintenance cost per unit length of pipeline is 16,500 RMB, the pressurization cost is 5.5 million RMB, and the compressor maintenance cost is 220,000 RMB. The economic flow rate of the supercritical CO2 pipeline is 2.2 m / s, the safe flow rate is 4.0 m / s, the pipeline design pressure is 15.0 MPa, the weld coefficient is 0.9, and the strength design coefficient is 0.72. The known parameters and the optimization results of the upper-level model are imported into the lower-level optimization model. The Wild Oat Algorithm is used to solve the cost distribution and optimal design scheme for the supercritical CO2 pipeline in the CCUS system, as shown in Tables 3 and 4. As can be seen from Table 3, the total cost is 628.9075 million yuan, of which the cost of purchasing pipes is 268.96 million yuan, accounting for 42.77%; the cost of pipeline construction is 327.25 million yuan, accounting for 52.03%.

[0022] Table 3. Cost distribution of supercritical carbon dioxide pipelines in CCUS systems Table 4 Amount of carbon dioxide transported from each carbon source node to the carbon sink node The foregoing is merely an example of the embodiments of this specification and is not intended to limit the embodiments of this specification. For those skilled in the art, various modifications and variations of the embodiments of this specification are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of this specification shall be included within the scope of the claims of the embodiments of this specification.

Claims

1. A hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network, characterized in that: The following steps are involved: S1: Establishing a hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network, the hierarchical planning model includes a CCUS system source-sink matching planning layer and a supercritical carbon dioxide pipeline network design optimization layer; S2: Obtain basic data of the CCUS system, establish an upper-layer optimization model based on the CCUS system source-sink matching planning layer, and determine the decision variables of the optimization model; The objective function of the upper optimization model is to minimize the distance between the matching carbon source nodes and carbon sink nodes in the CCUS system. The objective function expression is as follows: ； Where, i is the carbon source node; I It is a collection of carbon source nodes; j It is a carbon sink node; J It is a collection of carbon sink nodes; l A carbon source node that matches the CCUS system i Carbon sink nodes j Connection distance; x i，j Carbon source node i Carbon sink nodes j Matching variables, if the carbon source node i Carbon sink nodes j If it matches, x i，j is 1, otherwise 0; a i Carbon source node i horizontal axis; a j Carbon sink node j horizontal axis; b i Carbon source node i vertical axis; b j Carbon sink node j vertical axis; The upper optimization model constraints include node self-allocation prohibition constraints, carbon source node uniqueness constraints, carbon source node capture constraints, carbon sink node injection constraints, node flow balance constraints, non-negativity constraints and carbon sink node demand constraints; The decision variables of the upper optimization model are carbon source nodes i Carbon sink nodes j Matching variables x i，j , matching carbon source nodes i Transport to carbon sink nodes j The amount of carbon dioxide q i，j ; S3: Obtaining basic data of the supercritical carbon dioxide pipeline network, establishing a lower-layer optimization model based on the supercritical carbon dioxide pipeline network design optimization layer, and determining decision variables of the optimization model; The lower optimization model is to minimize the total investment cost of the supercritical carbon dioxide pipeline network. The total investment cost of the supercritical carbon dioxide pipeline network includes the pipe purchase cost, pipeline construction cost, pipeline maintenance cost and pipeline pressurization cost. The objective function expression is: ； Where, f pu The purchase cost of the pipes; f co to build the pipeline; f ma For pipeline maintenance costs; f pr the cost of pressurizing the pipeline; The lower-level optimization model constraint nodes - pipeline flow balance constraint, pipeline flow velocity constraint, pipeline wall thickness constraint, pipeline outer diameter constraint, pipeline length constraint, pipeline uniqueness constraint, pipeline hydraulic constraint and pipeline thermal constraint; The decision variables of the lower optimization model are carbon source nodes i Carbon sink nodes j Intermediate pipeline m Construction variables X i，j，m ; Carbon source node i Carbon sink nodes j Intermediate pipeline m Boost variable Y i，j，m ; Carbon source node i Carbon sink nodes j Intermediate pipeline m Length of pipe l i，j，m ; Carbon source node i Carbon sink nodes j Intermediate pipeline m Outer diameter D i，j，m ; Carbon source node i Carbon sink nodes j Intermediate pipeline m Wall thickness δ i，j，m ; S4: using the Wild Oats algorithm to solve the hierarchical planning model based on the CCUS supercritical carbon dioxide pipeline network; The solution of the upper optimization model includes the carbon source nodes in the CCUS system i Carbon sink nodes j Matching solutions and matching carbon source nodes i Carbon sink nodes j Connection distance, matching carbon source node i Transport to carbon sink nodes j the amount of carbon dioxide; The solution of the upper optimization model is input into the lower optimization model as a known parameter; The solution of the lower optimization model includes the carbon source node i Carbon sink nodes j Pipeline construction plan, carbon source node i Carbon sink nodes j Intermediate pipeline length, carbon source node i Carbon sink nodes j Pipeline outer diameter, carbon source node i Carbon sink nodes j The wall thickness of the intermediate pipeline and the total investment cost of the supercritical carbon dioxide pipeline network.

2. A hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network according to claim 1, characterized in that: The basic data of the CCUS system described in S2 include the coordinates of the carbon source node, the coordinates of the carbon sink node, the economic capture capacity of the carbon source node, the maximum capture capacity of the carbon source node, the economic injection capacity of the carbon sink node, the maximum injection capacity of the carbon sink node and the total demand of the carbon sink node.

3. A hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network according to claim 1, characterized in that: The node self-configuration prohibition constraint expression described in S2 is: ； ； Where, x i，i Carbon source node i Self-matching variables; x j，j Carbon sink node j Self-matching variables; q i，i Carbon source node i the amount of CO2 it captures itself; q j，j Carbon sink node j the amount of carbon dioxide delivered to itself; The unique constraint expression of the carbon source node is: ； The carbon source node capture constraint expression is: ； Where, S i min Carbon source node i Economic capture volume; S i max Carbon source node i Maximum capture capacity; The carbon sink node injection constraint expression is: ； Where, C j min Carbon sink node j Economic capture volume; C j max Carbon sink node j Maximum capture capacity; The node traffic balance constraint expression is: ； Where, q in no is the node inflow; q out no Outgoing traffic for the node; no It is a CCUS system node; N It is a collection of CCUS system nodes; The non-negativity constraint expression is: ； ； ； The carbon sink node demand constraint expression is: ； Where, Z j Carbon sink node j of carbon dioxide demand.

4. A hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network according to claim 1, characterized in that: The basic data of the supercritical carbon dioxide pipeline network described in S3 include: pipeline economic parameters, pipeline operating condition boundary data, pipeline design parameters and ambient temperature; The pipeline economic parameters include the purchase cost of pipe per unit length, the construction cost per unit length of pipeline, the maintenance cost per unit length of pipeline, the maintenance cost of compressor and the pipeline pressurization cost; The pipeline operating condition boundary data includes pipeline economic flow rate, pipeline safety flow rate, carbon source node pressure, and carbon sink node delivery pressure; The pipeline design parameters include pipeline steel density, pipeline design pressure, pipeline pipe minimum yield strength, pipeline weld coefficient and pipeline strength design coefficient.

5. The hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network according to claim 1, characterized in that: The pipe purchase cost expression described in S3 is: ； Where, X i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m Construction variables, if the carbon source node i Carbon sink nodes j Pipeline construction is required m , then the construction variable value is 1, otherwise it is 0; k is the purchase cost of pipe per unit length; ρ m is the density of pipeline steel; π is pi; D i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m outer diameter; δ i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m outer diameter; L i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m Length of pipe; The pipe construction cost expression is: ； Where, γ is the construction cost per unit length of pipeline; The pipeline maintenance cost expression is: ； Where, θ is the maintenance cost per unit length of pipeline; λ is the compressor maintenance cost; carbon source node i Carbon sink nodes j Intermediate pipeline m Boost variable Y i，j，m , if the carbon source node i Carbon sink nodes j Intermediate pipeline m If boost is needed, the boost variable value is 1, otherwise it is 0; The pipeline pressurization cost expression is: ； Where, τ is the pipeline pressurization cost; The hierarchical planning method based on a CCUS supercritical carbon dioxide pipeline network according to claim 1, wherein the node-pipeline flow balance constraint expression described in S3 is: ； Where, Q i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m The carbon dioxide flow rate; The pipeline flow rate constraint expression is: ； ； Where, V i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m Flow rate; V min i,j,m Carbon source node i Carbon sink nodes j Intermediate pipeline m the economic flow rate; Vmax i,j,m Carbon source node i Carbon sink nodes j Intermediate pipeline m Safe flow rate; The pipeline wall thickness constraint expression is: ； Where, P m For pipelines m Design pressure; σ m For pipelines m Minimum yield strength of the pipe; ω m For pipelines m Weld coefficient; E m is the strength design factor of the pipeline; The pipeline inner diameter constraint expression is: ； Where, d i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m inner diameter; The pipeline length constraint expression is: ； The pipeline uniqueness constraint expression is: ； The pipeline hydraulic constraint expression is: ； ； ； ； Where, ρ CO2 is the density of supercritical carbon dioxide; g is the acceleration due to gravity; R is the gas constant; M air is the relative molecular mass of air; Z 0 is the compression factor of carbon dioxide under standard working conditions; T 0 is the standard operating temperature; P 0 is the standard working pressure; P i is the carbon source node pressure; P Z i,j,m Carbon source node i Carbon sink nodes j Intermediate pipeline m The end pressure; Z i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m The compressibility factor of carbon dioxide under operating conditions; T i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m operating temperature; ρ r is the relative density of carbon dioxide; f i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m The hydraulic friction factor; Re i，j，m Carbon source node i Carbon sink nodes j Intermediate pipeline m Reynolds number; μ CO2 is the supercritical carbon dioxide viscosity; P r Carbon sink node j Demand for delivery pressure; The pipeline thermal constraint expression is: ； ； ； Where, T i For nodes i temperature; T e is the ambient temperature; H is the temperature drop coefficient; K is the total heat transfer coefficient; C is the specific heat capacity of supercritical carbon dioxide; T min is the critical temperature of carbon dioxide; T max Design temperature for the pipe.

6. A hierarchical planning method based on CCUS supercritical carbon dioxide pipeline network according to claim 1, characterized in that: The specific steps of the Wild Oats algorithm described in S4 include: S401: Set population size N and the maximum number of iterations Ti max ,The decision variables of the randomly initialized optimization model form the initial solution of the optimization problem as the initial population. The random initialization formula is as follows: ； Where, s m,n It is an individual of the wild oat population within the population; rand is a random number in the interval [0,1]; ub To find the upper bound of the space; lb To solve the lower bound of space; S402: Calculating algorithm characteristic parameters, wherein the algorithm characteristic parameters include wild oat mass coefficient, wild oat main awn length coefficient, wild oat propagation coefficient, and dynamic adjustment factor; The calculation formula of the wild oat mass coefficient is as follows: ； Where, M is the quality coefficient of wild oats; dim is the target problem dimension; The calculation formula of the main awn length coefficient of wild oats is as follows: ； Where, O is the main awn length coefficient of wild oats; The wild oat propagation coefficient calculation formula is as follows: ； Where, R is the main awn length coefficient of wild oats; The dynamic adjustment factor calculation formula is as follows: ； Where, U is the main awn length coefficient of wild oats; Ti is the current iteration number; S403: Generate a random number in the range [0,1] w 1. If w When 1 is greater than 0.5, it enters the exploration phase, and the wild oat population individual update formula is as follows: ； ； Where, W 1 is the random exploration factor; S t+1 ( n ) is the t +1 wild oat population position at iteration; S t ( n ) is the t The wild oat population position at the iteration; S best is the optimal position of wild oat population; mod is the modulo operation; S404: If w When 1 is less than 0.5, it enters the development phase and generates a random number in the range [0,1]. w 2. If w When 2 is greater than 0.5, it enters the barrier development stage, and the wild oat population individual update formula is as follows: ； ； ； Where, W 2 is the first random development factor; W 3 is the random obstacle factor; r dim(- W 2, W 2) is a matrix with the same dimension as the target problem, and its value range is - W 2 to W 2 between; S405: If w When 2 is greater than 0.5, it enters the barrier-free development stage, and the individual update formula of the wild oat population is as follows: ； ； ； ； ； ； Where, W 4 is the elastic coefficient of the main awn of wild oats; W 5 is the coefficient of variation of the main awn length of wild oats; W 6 is the angle between the main awn ejection angle of wild oats and the ground; W 7 is the air resistance coefficient; W 8 is the accessibility factor; r dim(- W 4, W 4) is a matrix with the same dimension as the target problem, and its value range is - W 4 to W 4 between; S406: After the individuals in the complete population are updated, the fitness of the individuals in the population is calculated using the optimization model objective function as the fitness function. The individual with the highest fitness is defined as the optimal individual, and the current number of iterations is checked. Ti Is it satisfied Ti ≥ Ti max If it is satisfied, the feasible solution represented by the optimal individual is output as the optimal solution. If it is not satisfied, return to S403.

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