A method for verifying the order of pipeline capacity of a natural gas pipeline network

Abstract

The present invention provides a method for verifying the pipe capacity order of a natural gas pipeline network. The method includes: constructing the constraint conditions of the pipe capacity order verification model of the natural gas pipeline network according to the structural parameters, technical parameters, and operating parameters of the natural gas pipeline network; constructing the pipe capacity order verification model of the natural gas pipeline network according to the constraint conditions and the zero-value objective function; and verifying the feasibility of the operation plan of the natural gas pipeline network according to the pipe capacity order verification model of the natural gas pipeline network. Based on the perspective of verifying the feasibility of the pipeline transportation plan, the present invention proposes a method for verifying the pipe capacity order of a natural gas pipeline network. This method provides a theoretical basis for the calculation problem of multi-user pipeline transportation plans and has certain practical significance for the national pipeline network to achieve scientific decision-making and efficient operation, etc.

Description

Technical Field

[0001] The present invention relates to the field of natural gas pipeline network operation optimization, and particularly to a method for verifying the order of pipeline capacity of a natural gas pipeline network. Background Art

[0002] With the increasing scale of pipeline network interconnection and interoperability, the long-distance natural gas transmission pipeline has evolved from a single-source, single-pipe non-pressurized transmission mode to a multi-source, multi-pipe, and multiple compressor station transmission mode. The gas volume distribution of multiple sources and the selection of compressor startup schemes have made the pipeline startup scheme tend to be complex. When the national pipeline network faces multiple pipeline transportation service applications, it is necessary to adhere to the principle of fair distribution, clarify the pipeline transportation capacity of the pipeline network, and make corresponding pipeline transportation schemes for different demands for shippers to choose. However, the pipeline transportation capacity of the pipeline network is not a fixed value, and its value is affected by many factors, and the mutual influence between different pipeline transportation distribution schemes needs to be considered. This puts forward higher requirements for the capacity allocation and scheme formulation of the national pipeline network. In the actual operation of the pipeline, most operation schemes are qualitatively analyzed and formulated by technicians based on experience, and then the feasibility of the scheme is determined according to the simulation software. However, in the case of increasingly complex interconnected pipeline networks and multiple gas sources and multiple compressor stations, it is difficult for manual methods to make operation schemes suitable for each pipeline transportation service, nor can the optimality of the pipeline transportation scheme be determined, and the efficiency is low, making it difficult to achieve fair and efficient configuration services. Therefore, there is an urgent need for efficient optimization technology to assist in formulating operation schemes and improve the utilization rate of pipeline transportation capacity and the service efficiency of the national pipeline network. Summary of the Invention

[0003] Aiming at the problems of low efficiency in formulating the pipeline transportation scheme of the natural gas pipeline network and difficulty in achieving fair and efficient configuration services, the present invention proposes a method for verifying the order of pipeline capacity of the natural gas pipeline network from the perspective of verifying the feasibility of the pipeline transportation scheme.

[0004] The present invention provides the following technical solutions:

[0005] A method for verifying the order of pipeline capacity of a natural gas pipeline network, comprising:

[0006] S1: Construct the constraint conditions of the verification model for the order of pipeline capacity of the natural gas pipeline network according to the structural parameters, technical parameters, and operation parameters of the natural gas pipeline network;

[0007] S2: Construct a verification model for the order of pipeline capacity of the natural gas pipeline network according to the constraint conditions and the zero-value objective function;

[0008] S3: Verify the feasibility of the operation scheme of the natural gas pipeline network according to the verification model for the order of pipeline capacity of the natural gas pipeline network.

[0009] In one embodiment, the pipe capacity order verification problem refers to verifying whether the pipe network has sufficient transmission capacity to complete the transmission when the pipe network structure, the gas volume from the gas source, the minimum pressure of the gas source distribution station, the path or the path is unknown, and a set of required gas sources / distribution station gas volumes specified by the shipper is input, and the objective function is 0. If the verification is feasible, the model has a solution, and a set of feasible solutions is obtained, including the operation mode of the compressor station put into operation, the compressor startup plan, the flow configuration plan, and the path. If the verification is infeasible, the model has no solution, and the pipe network does not have sufficient transmission capacity to complete this transmission.

[0010] In one embodiment, the structural parameters of the natural gas pipe network include the number of gas source nodes, the number of distribution nodes, the number of user nodes, the connection relationship between the pipeline and each node, and the number of compressors inside the compressor station;

[0011] The technical parameters of the natural gas pipe network include the pipeline design pressure, the pipeline transmission capacity, and the compressor characteristic curve;

[0012] The operating parameters of the natural gas pipe network include the gas supply flow rate of the origin node, the natural gas flow rate of the demand node, and the pressure requirement of the demand node.

[0013] In one embodiment, the switches of two-way pipelines, the switches of compressor station modes, and the switches of components such as compressor stations in the refined pipe network physical model are refined. The pipe capacity order verification model contains a large number of discrete variables. According to the judgment condition 2 of the convex optimization problem, it can be known that the model containing integer variables is a non-convex programming problem. In addition, the pipeline pressure drop equation involves quadratic terms, and the feasible operating range of the compressor belongs to non-linear constraints, and the model belongs to a mixed integer non-linear programming problem.

[0014] The constraint conditions of the natural gas pipe network capacity order verification model include pipe network flow constraints, pipe network pressure constraints, compressor station and valve switch constraints, and compressor constraints;

[0015] The pipe network flow constraints include: node flow balance constraints, gas volume supply and demand constraints, and pipeline flow range constraints;

[0016] The natural gas pipe network node flow balance constraint stipulates that at any connected node, the flow rate flowing into the node should be equal to the flow rate flowing out of the node. The constraint relation formula is:

[0017]

[0018] In the formula, is the inflow rate of node i; is the outflow rate of node i; Q i is the gas transmission volume of node i;

[0019] In the service application provided by the shipper, the gas supply volume of each gas source should be clearly defined, and the sum of all gas source volumes is conserved with all demand volumes. The gas volume supply-demand constraint relation is as follows:

[0020]

[0021] In the formula, is the gas supply volume of gas source node s; is the ordered volume of gas source node s; is the ordered volume of demand node i;

[0022] The remaining capacities of each pipeline are different, and the acceptable flow ranges are also different. Therefore, upper and lower limits are imposed on the flow of each pipeline. In addition, for two-way pipelines, reverse flow is allowed. The pipeline flow range constraint relation is as follows:

[0023]

[0024] In the formula, Q a is the gas transmission volume of one-way pipeline a; is the minimum gas transmission volume of one-way pipeline a; is the maximum gas transmission volume of one-way pipeline a; is the maximum gas transmission volume of two-way pipeline ab.

[0025] The pipeline network pressure constraints include: pipeline maximum pressure constraint, demand node pressure constraint, resistance element pressure constraint, gas source node pressure constraint, pipeline hydraulic pressure drop constraint;

[0026] Natural gas flows in the gas transmission pipeline, forming an internal pipeline pressure. The internal pressure generates stress on the pipe wall. When the stress reaches the yield strength of the pipe material, it will cause permanent deformation of the pipe material and ultimately lead to the failure of the pipe material. Therefore, the maximum pressure in the pipeline needs to be less than the design pressure. The pipeline maximum pressure constraint relation is as follows:

[0027] P a ≤MAOP

[0028] In the formula, P a is the pressure of pipe section a; MAOP is the maximum allowable operating pressure of the pipe section;

[0029] At the demand node, the gas pressure should be greater than the minimum delivery pressure. The demand node pressure constraint relation is as follows:

[0030] P i ≥P i min =P i d

[0031] In the formula, P i is the pressure of demand node i; P imin is the minimum delivery pressure of demand node i; P i d is the minimum delivery pressure of demand node i specified by the shipper;

[0032] In physical modeling, a pressure loss resistance element caused by in-station pipelines, measuring devices, filters or partially closed valves is added. The pressure constraint relational expression of the resistance element is:

[0033]

[0034] In the formula, is the inlet pressure of the resistance element re; is the outlet pressure of the resistance element re; Q re is the gas transmission volume of the resistance element re; ζ is the pressure loss of the resistance element re;

[0035] In the gas source node, the gas pressure should be greater than the minimum gas source pressure and not exceed the maximum gas source pressure. The pressure constraint relational expression of the gas source node is:

[0036]

[0037] In the formula, is the maximum pressure of the gas source node s; P s is the pressure of the gas source node s; is the minimum pressure of the gas source node s;

[0038] The pipeline hydraulic pressure drop constraint calculates the pressure drop according to the basic formula of the pipe network expressed by volume flow. The constraint relational expression is:

[0039]

[0040] In the formula, Q is the volume flow of natural gas under standard conditions; C is the constant coefficient of pipeline hydraulic pressure drop; P in is the starting pressure of the pipeline; P out is the ending pressure of the pipeline; D is the pipeline diameter; λ is the pipeline hydraulic friction coefficient; Z is the natural gas compression factor; Δ is the relative density of natural gas; T m is the average temperature of natural gas; L is the pipeline length;

[0041] The described switch constraints include: compressor station switch constraints, compressor station configuration switch constraints and valve switch constraints;

[0042] When the bypass valve is open, the compressor is closed and the compressor station does not increase pressure; when the compressor is closed and the bypass valve is closed, the compressor station is closed, and the inlet and outlet are not in circulation and cannot pass gas, and the flow rate is 0. When the compressor is on and the compressor station is on and the bypass valve is closed, the compressor station increases the pressure of the flowing gas. The internal switch constraint relationship of the compressor station:

[0043]

[0044] Wherein, S cs is the switch variable of the compressor station cs, 0 means closed, and 1 means open; is the bypass valve switch variable of the compressor station cs, 0 means closed, and 1 means open;

[0045] Each startup situation of the compressors in the station is restricted by the inlet valve and the outlet valve. When a certain configuration of the compressor is turned on, the inlet and outlet valves are opened; when a certain configuration of the compressor is turned off, the inlet and outlet valves are closed. The configuration of the compressor station is related to the type, quantity, and series or parallel connection method of the compressors in the station. Therefore, the configuration switch of the compressor station is coupled with the corresponding compressor switch. The constraint relationship of the compressor station configuration switch is:

[0046]

[0047] Wherein, is the switch variable of the compressor station cs, 0 means closed, and 1 means open; is the switch variable of the compressor station cs, 0 means closed, and 1 means open; is the configuration switch variable of the compressor station cs, 0 means closed, and 1 means open. is the switch variable of the compressor c in the compressor station cs, 0 means closed, and 1 means open;

[0048] The valve plays a role of separating or connecting two nodes in the natural gas pipeline network. When the valve is open, it is default that the pressures at both ends of the valve are equal, and the flow rate of the valve does not exceed the maximum valve flow rate range; when the valve is closed, the two ends are not connected and the flow rate is 0. The valve constraint relational expression is:

[0049]

[0050] Wherein, S va is the switch variable of the valve va, 0 means closed, and 1 means open; is the inlet pressure of the valve va; is the outlet pressure of the valve va; is the minimum volume flow rate of the valve va; is the maximum volume flow rate of the valve va; Q va is the volume flow rate of the valve va;

[0051] The constraints of the compressor mentioned above include: compressor flow rate constraint, compressor speed constraint, compressor pressure ratio constraint, compressor maximum outlet pressure constraint, compressor power constraint, and compressor efficiency constraint.

[0052] During the operation of the compressor, when the intake flow rate is too low, a surging phenomenon will occur; while when the intake flow rate is too high, a choking phenomenon will occur. The occurrence of these two operating conditions will affect the normal operation of the compressor and may even cause equipment damage. Therefore, it is necessary to limit the flow rate of the compressor. The compressor flow rate constraint relationship is as follows:

[0053]

[0054] In the formula, Q cs,c is the flow rate of compressor c in compressor station cs; is the surging flow rate of compressor c in compressor station cs; r cs,c is the rotational speed of compressor c in compressor station cs; is the choking flow rate of compressor c in compressor station cs; A su 、B su 、C su 、D su 、A st 、B st 、C st 、D st are equation coefficients obtained by fitting the compressor operation data;

[0055] In the actual operation of the natural gas pipeline, the pipeline operation state is adjusted by controlling the rotational speed. The rotational speed of the compressor is related to the equipment performance and has a certain range limit. The compressor rotational speed constraint relationship is as follows:

[0056]

[0057] In the formula, is the minimum allowable rotational speed of compressor c in compressor station cs; is the maximum allowable rotational speed of compressor c in compressor station cs;

[0058] The pressure ratio refers to the ratio between the discharge pressure and the intake pressure of the compressor. In order to effectively control the pressurization degree of the compressor and ensure the safe and stable operation of the equipment, the compressor pressure ratio constraint relationship is as follows:

[0059]

[0060] In the formula, ε cs,c is the pressure ratio of compressor c in compressor station cs; is the minimum allowable pressure ratio of compressor c in compressor station cs; is the maximum allowable pressure ratio of compressor c in compressor station cs;

[0061] The outlet pressure after the compressor pressurizes shall not be higher than its maximum allowable outlet pressure. The compressor maximum outlet pressure constraint relationship is as follows:

[0062]

[0063] In the formula, is the outlet pressure of the compressor c in the gas compression station cs, Pa; is the maximum allowable outlet pressure of the compressor c in the gas compression station cs, Pa;

[0064] The power constraint relation of the compressor:

[0065]

[0066] In the formula, H cs,c is the adiabatic head of the compressor c in the gas compression station cs; η cs,c is the adiabatic efficiency of the compressor c in the gas compression station cs; χ is the adiabatic index of the compressor; W cs,c is the power of the compressor c in the gas compression station cs; is the minimum allowable power of the compressor c in the gas compression station cs; is the maximum allowable power of the compressor c in the gas compression station cs;

[0067] The efficiency constraint relation of the compressor:

[0068]

[0069] In the formula, a cs,c , b cs,c , c cs,c , d cs,c are the coefficients of the efficiency-flow-speed curve equation of the compressor c in the gas compression station cs, obtained by fitting.

[0070] In one embodiment, the zero-value objective function described in step S2 is to add a meaningless objective variable for the model to be solved normally, and verify whether the pipeline network has sufficient conveying capacity to complete the conveying.

[0071] The zero-value objective function relation is:

[0072] min f = obj var = 0;

[0073] In the formula, f is the objective function; objvar is the objective variable.

[0074] According to the constraint conditions and the zero-value objective function, a verification model for ordering the pipe capacity of the natural gas pipeline network is generated through the MINLP model.

[0075] In one embodiment, the pipeline network structure in the natural gas pipeline network capacity ordering verification model is known, and the parameters in the ordering application form are known. Since some shippers will specify the transportation route while some will not, the route parameters are regarded as known parameters according to the specific situation. The known parameters include the demand at the gas source point, the demand at the download point, the pressure constraint at the download point, the remaining capacity of the pipeline, and the route (known situation).

[0076] In one embodiment, the natural gas pipeline network capacity ordering verification model is used to solve a set of feasible solutions. The decision variables include the flow configuration and the startup scheme along the line. In the physical model, the startup situation of the compressor is separated. Therefore, in addition to the switches of each component, the decision variables also include the startup situation of the compressor. According to the startup situation, it is clear at a glance how many compressors are started in the station. The decision parameters include the flow of each component, the pressure of each component, the switch situation of the two-way pipeline, the startup situation of the compressor, the fuel of the compressor, and the power of the compressor.

[0077] In one embodiment, the model solving method adopts the branch and bound method, the outer approximation algorithm based on the equality relaxation strategy, and the branch and bound method of constrained integer programming.

[0078] The branch and bound method is a method for solving the mixed integer nonlinear programming (MINLP) model. During the branch and bound process, the feasible region of the discrete variable is subdivided again, the boundary of the discrete variable is tightened to a new integer value, and the current non-integer solution is cut off. Each time the boundary is tightened, starting from the optimal solution to the previous relaxed sub-model, a new and more tightened NLP sub-model is solved. In the limited feasible space (assuming the minimum), the objective function value of the NLP sub-model is assumed to be the lower limit of the objective. Even if the NLP algorithm finds a local optimal solution, it may not be the global optimal solution. If the NLP solution returns a locally infeasible state of the sub-model, it is usually assumed that the sub-model has no feasible solution, even if it is only locally infeasible. If the model is convex, these assumptions are satisfied, and the branch and bound method provides the correct boundary. If the model is non-convex, the objective boundary may be incorrect, and better solutions may exist in other un-searched spaces.

[0079] The external approximation algorithm based on the equality relaxation strategy achieves external approximation by linearizing at each iteration and accumulates them to gradually obtain a linear approximation of the improved non-linear convex function, reducing the objective function while expanding the feasible solution region. Equality relaxation is based on the results of non-linear programming, rearranging the equations into equality equations and inequality equations and transforming the problem into a minimization problem. Generally speaking, after making certain assumptions about the convexity of the non-linear function, the equality constraints can be "relaxed" into inequality constraints. This property is used to accumulate the linear approximations in the MIP master problem. The generalized penalty function means that when the convexity assumption does not hold, slack variables (non-negative) are introduced on the right side of the inequality just defined and the modified objective function. The algorithm starts by solving the NLP problem, first relaxing the 0-1 condition of the binary variables during the solution process. If the solution obtained is an integer solution, the search is complete; otherwise, the NLP, called the sub-problem, and the MIP, called the master problem, are solved alternately. The NLP sub-problem is calculated by the fixed 0-1 variables predicted by the MIP master problem at each iteration. If a convex problem is encountered, the master problem gives a lower bound of the objective function, which monotonically increases due to the iterative accumulation of linear approximations. Note: In the case of maximization, this bound is an upper bound. Another stopping criterion that works well in the actual operation of solving non-convex problems is based on heuristics: once the iterative results of the NLP sub-problem start to deteriorate, stop immediately (for example, the difference between the optimal objective function value of the current NLP sub-problem and that of the NLP sub-problem in the previous iteration).

[0080] The branch and bound method for solving constrained integer programming allows control of the solution process. Algorithm 3 combines integer linear programming (ILP) and constraint programming (CP). The theory of integer linear programming is rooted in polyhedron theory, cutting plane theory, and search techniques in the algorithm; constraint programming originated from constraint satisfaction problems. From this perspective, integer linear programming constraints are regarded as a set of conjunctive normal forms, and the domain of decision variables is iteratively reduced using conflict analysis and domain propagation to reduce the search space.

[0081] In summary, the present invention provides a method for verifying the order of pipeline capacity of a natural gas pipeline network. By using the pipeline network structure parameters of the target natural gas pipeline network, a natural gas pipeline network system with a gas source point, a download point, pipelines, compressor stations, and valves is constructed. Combining the structure parameters, technical parameters, and operation parameters of the natural gas pipeline network system, the constraint conditions of the pipeline capacity order verification model of the natural gas pipeline network are constructed. Based on the constraint conditions and the zero-value objective function, a pipeline capacity order verification model of the natural gas pipeline network is constructed, and the model is solved to verify the feasibility of the pipeline transportation plan of the natural gas pipeline network, and a conclusion that the pipeline transportation plan is infeasible or a feasible pipeline transportation plan is given. The present invention mainly solves the problem of verifying the order of pipeline capacity of a natural gas pipeline network. After the establishment of the national pipeline network, due to the increasingly complex pipeline network scale and wide coverage, there are many demands from shippers, the interest relationships among all parties are intricate, the capacity constraints among infrastructure are highly coupled, and the gas transmission capacity of the pipeline network changes with the change of users. Aiming at the problems of low efficiency in formulating the pipeline transportation plan of the natural gas pipeline network and difficulty in achieving fair and efficient configuration services, the optimization method provided by the present invention proposes a method for verifying the order of pipeline capacity of a natural gas pipeline network from the perspective of verifying the feasibility of the pipeline transportation plan. It provides a theoretical basis for the calculation problem of the pipeline transportation plan for multiple users and has certain practical significance for the national pipeline network to achieve scientific decision-making and efficient operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a schematic diagram of the natural gas charging mode;

[0083] Figure 2 It is a flow chart of the present invention;

[0084] Figure 3 It is a structure diagram of a certain natural gas pipeline network system;

[0085] Figure 4 It is a flow chart of the solution of the external approximation method based on the equality relaxation strategy of the present invention;

[0086] Figure 5 It is a comparison of the simulation and optimized station pressures for ordering 1-6 in a certain natural gas pipeline network system;

[0087] Figure 6 It is a comparison of the flow directions of the simulation and optimized feasible solutions for ordering 6 in a certain natural gas pipeline network system; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0089] As shown Figure 2 in the figure, a method for verifying the pipe capacity order of a natural gas pipeline network provided by the present invention includes the following steps:

[0090] S1: According to the structural parameters, technical parameters, and operating parameters of the natural gas pipeline network, construct the constraint conditions of the pipe capacity order verification model of the natural gas pipeline network;

[0091] S2: Construct a pipe capacity order verification model of the natural gas pipeline network according to the constraint conditions and the zero-value objective function;

[0092] S3: Verify the feasibility of the natural gas pipeline network operation plan according to the pipe capacity order verification model of the natural gas pipeline network.

[0093] In one embodiment, the pipe capacity order verification problem refers to verifying whether the pipeline network has sufficient conveying capacity to complete the conveying when the pipeline network structure, the gas volume from the gas source, the minimum pressure of the gas source distribution station, and the path are known or unknown, and a set of gas volume requirements of the gas source / distribution station specified by the shipper is input, and the objective function is 0. If the verification is feasible, the model has a solution, and a set of feasible solutions is obtained, including the operation mode of the compressor station in operation, the compressor startup plan, the flow configuration plan, and the path. If the verification is infeasible, the model has no solution, and the pipeline network does not have sufficient conveying capacity to complete this conveying.

[0094] In one embodiment, the structural parameters of the natural gas pipeline network include the number of gas source nodes, the number of distribution nodes, the number of user nodes, the connection relationship between the pipeline and each node, and the number of compressors inside the compressor station;

[0095] The technical parameters of the natural gas pipeline network include the pipeline design pressure, the pipeline conveying capacity, and the compressor characteristic curve;

[0096] The operating parameters of the natural gas pipeline network include the gas supply flow rate of the origin node, the natural gas flow rate of the demand node, and the pressure requirement of the demand node.

[0097] In one embodiment, the switches of two-way pipelines, the switches of compressor station modes, and the switches of components such as compressor stations in the refined pipeline network physical model are refined. The pipe capacity order verification model contains a large number of discrete variables. According to the judgment condition 2 of the convex optimization problem, it can be known that the model containing integer variables is a non-convex programming problem. In addition, the pipeline pressure drop equation involves quadratic terms, and the feasible operating range of the compressor belongs to non-linear constraints. The model belongs to a mixed integer non-linear programming problem.

[0098] The constraint conditions of the pipe capacity order verification model of the natural gas pipeline network include pipeline network flow constraints, pipeline network pressure constraints, compressor station and valve switch constraints, and compressor constraints;

[0099] The pipeline network flow constraints include: node flow balance constraints, gas volume supply and demand constraints, and pipeline flow range constraints;

[0100] The flow balance constraint of the natural gas pipeline network nodes stipulates that at any connected node, the flow rate flowing into the node should be equal to the flow rate flowing out of the node. The constraint relation is as follows:

[0101]

[0102] In the formula, is the inflow rate of node i; is the outflow rate of node i; Q i is the gas transmission volume of node i;

[0103] In the service application provided by the shipper, the gas supply volume of each gas source should be specified, and the sum of all gas source volumes is conserved with all demand volumes. The gas volume supply-demand constraint relation is as follows:

[0104]

[0105] In the formula, is the gas supply volume of gas source node s; is the ordered volume of gas source node s; Ordered volume of demand node i;

[0106] The remaining capacities of each pipeline are different, and the acceptable flow rate ranges are also different. Therefore, upper and lower limits are imposed on the flow rate of each pipeline. In addition, for two-way pipelines, it is set that the pipeline can transport gas in both directions. The pipeline flow rate range constraint relation is as follows:

[0107]

[0108] In the formula, Q a is the gas transmission volume of one-way pipeline a; Minimum gas transmission volume of one-way pipeline a; Maximum gas transmission volume of one-way pipeline a; Maximum gas transmission volume of two-way pipeline ab.

[0109] The pipeline network pressure constraints include: pipeline maximum pressure constraint, demand node pressure constraint, resistance element pressure constraint, gas source node pressure constraint, pipeline hydraulic pressure drop constraint;

[0110] Natural gas flows in the gas transmission pipeline, forming an internal pipeline pressure. The internal pressure generates stress on the pipe wall. When the stress reaches the yield strength of the pipe material, it will cause permanent deformation of the pipe material and ultimately lead to the failure of the pipe material. Therefore, the maximum pressure in the pipeline needs to be less than the design pressure. The pipeline maximum pressure constraint relation is as follows:

[0111] P a ≤MAOP (6)

[0112] In the formula, P aP is the pressure of pipe section a; MAOP is the maximum allowable operating pressure of the pipe section;

[0113] At the demand node, the gas pressure should be greater than the minimum delivery pressure. The pressure constraint relation of the demand node is:

[0114] P i ≥P i min =P i d (7)

[0115] In the formula, P i is the pressure of demand node i; P i min is the minimum delivery pressure of demand node i; P i d is the minimum delivery pressure of demand node i specified by the shipper;

[0116] In physical modeling, pressure loss resistance elements caused by in-station pipelines, measuring devices, filters or partially closed valves are added. The pressure constraint relation of the resistance element is:

[0117]

[0118] In the formula, is the inlet pressure of resistance element re; is the outlet pressure of resistance element re; Q re is the gas transmission volume of resistance element re; ζ is the pressure loss of resistance element re;

[0119] At the gas source node, the gas pressure should be greater than the minimum gas source pressure and not exceed the maximum gas source pressure. The pressure constraint relation of the gas source node is:

[0120]

[0121] In the formula, is the maximum pressure of gas source node s; P s is the pressure of gas source node s; is the minimum pressure of gas source node s;

[0122] The pipeline hydraulic pressure drop constraint calculates the pressure drop using the basic formula of the pipe network expressed in volume flow rate. The constraint relation is:

[0123]

[0124] In the formula, Q is the volume flow rate of natural gas under standard conditions; C is the constant coefficient of pipeline hydraulic pressure drop; P in is the starting pressure of the pipeline; P outis the pipeline end pressure; D is the pipeline diameter; λ is the pipeline hydraulic friction coefficient; Z is the natural gas compression factor; Δ is the relative density of natural gas; T m is the average temperature of natural gas; L is the pipeline length;

[0125] The described switch constraints include: compressor station switch constraints, compressor station configuration switch constraints, and valve switch constraints;

[0126] When the bypass valve is open, the compressor is closed and the compressor station does not boost pressure; when the compressor is closed and the bypass valve is closed, the compressor station is closed, and the inlet and outlet are not in circulation and cannot pass gas, and the flow rate is 0. When the compressor is on and the compressor station is on and the bypass valve is closed, the compressor station boosts the pressure of the flowing gas. The internal switch constraint relationship of the compressor station:

[0127]

[0128] In the formula, S cs is the switch variable of compressor station cs, 0 means closed, 1 means open; is the bypass valve switch variable of compressor station cs, 0 means closed, 1 means open;

[0129] Each startup situation of the in-station compressor is restricted by the inlet valve and the outlet valve. When a certain configuration of the compressor is on, the inlet and outlet valves are on; when a certain configuration of the compressor is off, the inlet and outlet valves are off. The configuration of the compressor station is related to the type, quantity, and series or parallel connection method of the in-station compressors. Therefore, the configuration switch of the compressor station is coupled with the corresponding compressor switch. The compressor station configuration switch constraint relationship:

[0130]

[0131] In the formula, is the switch variable of compressor station cs, 0 means closed, 1 means open; is the switch variable of compressor station cs, 0 means closed, 1 means open; is the configuration switch variable of compressor station cs, 0 means closed, 1 means open. is the switch variable of compressor c in compressor station cs, 0 means closed, 1 means open;

[0132] The valve plays a role in separating or connecting two nodes in the natural gas pipeline network. When the valve is open, it is default that the pressures at both ends of the valve are equal, and the valve flow rate does not exceed the maximum valve flow rate range; when the valve is closed, the two ends are not connected and the flow rate is 0. The valve constraint relational formula:

[0133]

[0134] In the formula, S vaThe variable of the valve va switch, 0 indicates closed, and 1 indicates open; is the inlet pressure of the valve va; is the outlet pressure of the valve va; is the minimum volume flow rate of the valve va; is the maximum volume flow rate of the valve va; Q va is the volume flow rate of the valve va;

[0135] The compressor constraints described above include: compressor flow rate constraint, compressor speed constraint, compressor pressure ratio constraint, compressor maximum outlet pressure constraint, compressor power constraint, and compressor efficiency constraint.

[0136] During the operation of the compressor, when the intake flow rate is too low, a surge phenomenon will occur; when the intake flow rate is too high, a choking phenomenon will occur. The occurrence of these two operating conditions will affect the normal operation of the compressor and may even cause equipment damage. Therefore, it is necessary to limit the flow rate of the compressor. The compressor flow rate constraint relationship formula:

[0137]

[0138] In the formula, Q cs,c is the flow rate of the compressor c in the gas compression station cs; is the surge flow rate of the compressor c in the gas compression station cs; r cs,c is the speed of the compressor c in the gas compression station cs; is the choking flow rate of the compressor c in the gas compression station cs; A su , B su , C su , D su , A st , B st , C st , D st are equation coefficients obtained by fitting compressor operation data;

[0139] In the actual operation of the natural gas pipeline, the pipeline operation state is adjusted by controlling the speed. The compressor speed is related to the equipment performance and has a certain range limit. The compressor speed constraint relationship formula:

[0140]

[0141] In the formula, is the minimum allowable speed of the compressor c in the gas compression station cs; is the maximum allowable speed of the compressor c in the gas compression station cs;

[0142] The pressure ratio refers to the ratio between the compressor discharge pressure and the intake pressure. In order to effectively control the compressor boost level and ensure the safe and stable operation of the equipment, the compressor pressure ratio constraint relationship formula:

[0143]

[0144] Wherein, ε cs,c is the compression ratio of the compressor c in the gas compression station cs; is the minimum allowable compression ratio of the compressor c in the gas compression station cs; is the maximum allowable compression ratio of the compressor c in the gas compression station cs;

[0145] The outlet pressure of the compressor after boosting shall not be higher than its maximum allowable outlet pressure. The constraint relation formula for the maximum outlet pressure of the compressor is:

[0146]

[0147] Wherein, is the outlet pressure of the compressor c in the gas compression station cs, Pa; is the maximum allowable outlet pressure of the compressor c in the gas compression station cs, Pa;

[0148] The constraint relation formula for the power of the compressor is:

[0149]

[0150] Wherein, H cs,c is the adiabatic head of the compressor c in the gas compression station cs; η cs,c is the adiabatic efficiency of the compressor c in the gas compression station cs; χ is the adiabatic index of the compressor; W cs,c is the power of the compressor c in the gas compression station cs; is the minimum allowable power of the compressor c in the gas compression station cs; is the maximum allowable power of the compressor c in the gas compression station cs;

[0151] The constraint relation formula for the efficiency of the compressor is:

[0152]

[0153] Wherein, a cs,c , b cs,c , c cs,c , d cs,c are the coefficients of the efficiency-flow-speed curve equation of the compressor c in the gas compression station cs, which are obtained by fitting.

[0154] In one embodiment, the zero-value objective function described in step S2 is to add a meaningless objective variable to enable the model to be solved normally and verify whether the pipeline network has sufficient transportation capacity to complete the transportation.

[0155] The zero-value objective function relation formula is:

[0156] min f = objvar0(24)

[0157] In the formula, f is the objective function; objvar is the objective variable.

[0158] According to the constraint conditions and the zero-value objective function, a natural gas pipeline network capacity order verification model is generated through the MINLP model.

[0159] The symbol explanations of formulas (1) to (24) are shown in Tables 1, 2, and 3.

[0160] Table 1 Indexes and Sets of the Natural Gas Pipeline Capacity Order Verification Model

[0161] i ∈ I Set of nodes s ∈ S Set of demand nodes a ∈ A Set of one-way pipelines ab ∈ A' Set of two-way pipelines re ∈ R Set of resistance elements <![CDATA[c∈A c > Set of compressors <![CDATA[cs∈A cs > Set of compressor stations <![CDATA[va∈A va > Set of valves

[0162] Table 2 Known Parameters of the Natural Gas Pipeline Capacity Order Verification Model

[0163]

[0164]

[0165]

[0166] Table 3 Decision Variables of the Natural Gas Pipeline Capacity Order Verification Model

[0167]

[0168]

[0169] In one embodiment, the pipeline network structure in the natural gas pipeline network capacity order verification model is known, and the parameters in the order application form are known. Since some shippers will specify the transportation path and some will not, the path parameters are regarded as known parameters according to the specific situation. The known parameters include the demand at the gas source point, the demand at the download point, the pressure constraint at the download point, the remaining capacity of the pipeline, and the path (known situation).

[0170] In one embodiment, the natural gas pipeline network capacity order verification model is used to solve a set of feasible solutions, and the decision variables include the flow configuration and the compressor startup plan along the line. In the physical model, the compressor startup situation is separated, so in addition to the switches of each component, the decision variables also include the compressor startup situation. According to the startup situation, it is clear at a glance how many compressors are turned on in the station. The decision parameters include the flow of each component, the pressure of each component, the switch situation of the two-way pipeline, the compressor startup situation, the compressor fuel, and the compressor power.

[0171] In one embodiment, the model solving method adopts the branch and bound method, the outer approximation algorithm based on the equality relaxation strategy, and the branch and bound method of constraint integer programming.

[0172] The branch-and-bound method is a method for solving mixed-integer nonlinear programming (MINLP) models. During the branch-and-bound process, the feasible region of discrete variables is subdivided again, the boundaries of the discrete variables are tightened to new integer values, and the current non-integer solution is cut off. Each time the boundary is tightened, starting from the optimal solution to the previous relaxed submodel, a new and more tightened NLP submodel is solved. In a finite feasible space (assuming a minimum), the objective function value of the NLP submodel is assumed to be the lower bound of the objective, and even if the NLP algorithm finds a local optimal solution, it may not be the global optimal solution. If the NLP solution returns a locally infeasible state of the submodel, it is usually assumed that the submodel has no feasible solution, even if it is only locally infeasible. If the model is convex, these assumptions are satisfied, and the branch-and-bound method provides the correct boundaries. If the model is non-convex, the objective boundaries may be incorrect, and better solutions may exist in other unsearched spaces.

[0173] The outer approximation algorithm based on the equality relaxation strategy achieves outer approximation by linearizing at each iteration and accumulating them to gradually obtain a linear approximation of the improved non-linear convex function, shrinking the objective function while expanding the feasible solution region. Equality relaxation is based on the results of non-linear programming, rearranging the equations into equality equations and inequality equations, and transforming the problem into a minimization problem. Generally speaking, after making certain assumptions about the convexity of the non-linear function, the equality constraints can be "relaxed" into inequality constraints. This property is used to accumulate the linear approximations in the MIP master problem. The generalized penalty function refers to introducing slack variables (non-negative) on the right side of the just-defined inequality and the modified objective function when the convexity assumption does not hold. The algorithm starts by solving the NLP problem, first relaxing the 0-1 condition of the binary variables. If the solution obtained is an integer solution, the search is complete; otherwise, the NLP, called the subproblem, and the MIP, called the master problem, are solved alternately. The NLP subproblem is calculated by the predicted fixed 0-1 variables of the MIP master problem at each iteration. If a convex problem is encountered, the master problem gives the lower bound of the objective function, which monotonically increases due to the accumulated linear approximations in each iteration. Note: In the case of maximization, this boundary is the upper bound. Another stopping criterion that works well in the actual operation of solving non-convex problems is based on heuristics: once the iteration results of the NLP subproblem start to deteriorate, stop immediately (for example, the difference between the optimal objective function value of the current NLP subproblem and the value of the NLP subproblem in the previous iteration).

[0174] The branch and bound method for the constrained integer programming allows for the control of the solution process. Algorithm 3 combines integer linear programming (ILP) and constraint programming (CP). The theory of integer linear programming is rooted in polyhedron theory and cutting plane theory, as well as the search techniques in the algorithm; constraint programming originated from the constraint satisfaction problem. From this perspective, integer linear programming constraints are regarded as a set of conjunctive normal forms, and the domain of decision variables is iteratively reduced using conflict analysis and domain propagation to reduce the search space.

[0175] In summary, a method for verifying the pipe capacity order of a natural gas pipeline network provided by the present invention constructs a natural gas pipeline network system with gas source points, download points, pipelines, compressor stations, and valves through the pipeline network structure parameters of the target natural gas pipeline network; combines the structure parameters, technical parameters, and operation parameters of the natural gas pipeline network system to construct the constraint conditions of the pipe capacity order verification model for the natural gas pipeline network; constructs the pipe capacity order verification model for the natural gas pipeline network based on the constraint conditions and the zero-value objective function, solves the model, verifies the feasibility of the pipeline transportation plan for the natural gas pipeline network, and obtains a conclusion that the pipeline transportation plan is infeasible or gives a feasible pipeline transportation plan. The present invention mainly solves the problem of verifying the pipe capacity order of the natural gas pipeline network. After the establishment of the national pipeline network, due to the increasingly complex pipeline network scale and wide coverage, there are many demands from shippers, the interest relationships among all parties are intricate, the capacity constraints between infrastructure are highly coupled, and the gas transmission capacity of the pipeline network changes with the users. Aiming at the problems of low efficiency in formulating the pipeline transportation plan for the natural gas pipeline network and difficulty in achieving fair and efficient configuration services, the optimization method provided by the present invention proposes a method for verifying the pipe capacity order of the natural gas pipeline network from the perspective of verifying the feasibility of the pipeline transportation plan. It provides a theoretical basis for the calculation problem of the multi-user pipeline transportation plan and has certain practical significance for the national pipeline network to achieve scientific decision-making and efficient operation.

[0176] To further illustrate the present solution, the present invention takes a certain natural gas pipeline network system as a specific application example to carry out research on the pipe capacity order verification model for the natural gas pipeline network. The total length of this natural gas pipeline network is 495 km, and the historical maximum pressure is 7 MPa. It consists of 2 gas source points, 1 ring-shaped pipeline network, and 3 distribution points, showing a ring-shaped structure, as Figure 3 shown. The whole line includes 2 compressors, the pipeline roughness is 0.1 mm, the maximum flow rate of gas source A is 1800×10 4 m 3 / d, and the maximum flow rate of gas source B is 1200×10 4 m 3 / d. The structural parameters of this example are shown in Table 4. The maximum power of the in-station compressor is 5000 kW, the rated speed is 3500 - 6500 rpm, and the maximum outlet pressure is 7 MPa.

[0177] Table 4 Pipeline network structure parameter table of a certain natural gas pipeline network system

[0178]

[0179]

[0180] Based on historical data, 9 groups of ordering parameters are set as shown in Table 5, and it is required that the pressure at the demand node of the distribution station is not lower than 4 MPa.

[0181] Table 5 Ordering parameter table of a natural gas pipeline network system

[0182]

[0183] The hydrocarbon composition of natural gas in a natural gas pipeline network system is shown in Table 6.

[0184] Table 6 Hydrocarbon composition of natural gas

[0185]

[0186] A pipe capacity ordering verification model for the natural gas pipeline network is established. Based on this model and the external approximation algorithm with the equality relaxation strategy, each ordering parameter in Table 5 is verified. The solution framework of the external approximation algorithm with the equality relaxation strategy is as Figure 4 shown, and the obtained ordering verification results are shown in Table 7.

[0187] Table 7 Optimal solution results of ordering verification for a natural gas pipeline network system

[0188]

[0189] In the solution results of the operation, the solution status of ordering 1 - 5 is "integer solution", and the verified ordering is feasible. Among them, both compressors of ordering 1 - 5 are in bypass state. The solution status of ordering 6 is "partial integer solution". In the feasible solution, C1 is in bypass and C2 is turned on. The compressor startup information is shown in Table 3 - 8; the solution status of ordering 7 - 9 is "partial infeasible solution". Based on the solution principle of Algorithm 2, the verified ordering is infeasible.

[0190] To verify the accuracy of the pipe capacity ordering verification model for this type of natural gas pipeline network, a simulation software is used to conduct a simulation verification on this embodiment. The comparison of the station pressures of ordering 1 - 6 under simulation and optimization is as Figure 4 shown. In the feasible solution obtained by the optimization algorithm, the gas source pressure is greater than that of the simulation solution, and the pressure at the distribution station is smaller than that of the simulation solution. This is mainly because there is a compressor turned on in the feasible simulation solution, which boosts the pressure of the whole line, while the optimization algorithm only transports according to the pressure of the gas itself.

[0191] By analyzing the specific startup information of ordering 1 - 6 under simulation and optimization, it is found that among the 6 groups of feasible solutions, the simulation verification method preferentially turns on the compressor to meet the ordering requirements, while the optimization algorithm preferentially does not turn on the compressor at low pressure to meet the ordering requirements, as shown in Table 8.

[0192] Table 8 Comparison of Compressor Startup Schemes between Simulation and Optimization Feasible Solutions for Example 4

[0193] Order Simulation compressor startup plan Optimized compressor startup plan Order 1 C1 + C2 None Order 2 C1 + C2 None Order 3 C1 + C2 None Order 4 C1 + C2 None Order 5 C2 None Order 6 C2 C2

[0194] Table 9 Compressor Startup Information Table for Simulation and Optimization of Example 4 with Order Quantity 6

[0195]

[0196] For the simulation and optimization solutions with order quantity 6, the C2 compressor is started up. However, in the optimized startup scheme, the compressor is pressurized to 7 MPa, while in the simulation scheme, the compressor is only pressurized to 6.71 MPa. As a result, the pressure at the delivery station for order quantity 6 is higher in the optimized case than in the simulation case, as shown in Table 9. Although the compressor startup schemes are the same, the flow rates distributed along the path are different. As can be seen from Figure 5 It can be seen that in both cases, a part of the gas from gas source B is diverted through P3 to delivery station 1. The difference is that in the simulation method, the diverted flow rate is 97.32×10 4 m 3 / d, while in the optimized method, the diverted flow rate is 57.311×10 4 m 3 / d. The direction planning of the two-way pipelines in both paths is the same.

[0197] For a ring-shaped pipeline network with compressors, the feasible solutions obtained by the simulation and optimization methods not only differ in path allocation but also show a situation where the same compressor startup scheme has different pressurization capabilities. The simulation verification method preferentially starts up the compressor to meet the order requirements, while the optimization algorithm preferentially does not start up the compressor to meet the order requirements.

[0198] The above description is only for the embodiments of this specification and does not limit the embodiments of this specification. For those skilled in the art, various changes and modifications can be made to the embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the embodiments of this specification shall be included within the scope of the claims of the embodiments of this specification.

Claims

1. A method for verifying the order of pipeline capacity of a natural gas pipeline network, characterized in that, It includes the following steps: S1: According to the structural parameters, technical parameters, and operating parameters of the natural gas pipeline network, construct the constraint conditions of the pipe capacity ordering verification model for the natural gas pipeline network; The structural parameters of the natural gas pipeline network include the number of gas source nodes, the number of gas transmission nodes, the number of user nodes, the connection relationship between the pipeline and each node, and the number of compressors inside the compressor station; The technical parameters of the natural gas pipeline network include the pipeline design pressure, the pipeline transmission capacity, and the compressor characteristic curve; The operating parameters of the natural gas pipeline network include the gas supply flow rate at the origin node, the natural gas flow rate at the demand node, and the pressure requirement at the demand node; The constraint conditions include pipeline network flow constraints, pipeline network pressure constraints, compressor station and valve switch constraints, and compressor constraints; S2: Construct the pipe capacity ordering verification model for the natural gas pipeline network according to the constraint conditions and the zero-value objective function; The zero-value objective function is used to verify whether the pipeline network has sufficient transmission capacity to complete the natural gas transmission under the known supply and demand conditions of each node; The relational expression of the zero-value objective function is: min f = objvar = 0; In the formula, f is the objective function; objvar is the objective variable; According to the constraint conditions and the zero-value objective function, generate the pipe capacity ordering verification model for the natural gas pipeline network through a mixed-integer nonlinear programming model; S3: Solve the pipe capacity ordering verification model for the natural gas pipeline network to verify the feasibility of the natural gas pipeline network operation plan.

2. The natural gas pipeline network capacity order verification method according to claim 1, wherein, The pipe capacity ordering verification problem refers to the situation where, with the pipeline network structure, gas source incoming gas volume, minimum pressure at the gas source transmission station, path or unknown path known, a set of gas volumes required by the shipper for the gas source / transmission station is input, the objective function is 0, and it is verified whether the pipeline network has sufficient transmission capacity to complete the transmission; If the verification is feasible, the model has a solution, and a pipeline network operation plan is obtained, including a feasible compressor station operation mode, compressor startup plan, flow configuration plan, and path; if the verification is infeasible, the model has no solution, and the pipeline network does not have sufficient transmission capacity to complete this transmission.

3. A method for verifying the order of network capacity of a natural gas pipeline network according to claim 1, characterized in that, The pipeline network flow constraints include: node flow balance constraints, gas volume supply and demand constraints, and pipeline flow range constraints; The node flow balance constraint stipulates that at any connected node, the flow rate flowing into the node should be equal to the flow rate flowing out of the node, and the constraint relational expression is: In the formula, is the inflow rate of node i; is the outflow rate of node i; Q i is the gas transmission volume of node i; In the service application provided by the shipper, the gas supply volume of each gas source should be clearly defined, and the sum of all gas source volumes is conserved with all demand volumes. The relational expression of the gas volume supply and demand constraint is: In the formula, is the gas supply volume of the gas source node s; is the order volume of the gas source node s; is the order volume of the demand node i; The remaining capacities of each pipeline are different, and the acceptable flow ranges are also different. Therefore, upper and lower limits are imposed on the flow rate of each pipeline; in addition, for two-way pipelines, it is set that the pipeline can be transported in both forward and reverse directions. The relational expression of the pipeline flow range constraint is: Where, Q a is the gas transmission volume of the unidirectional pipeline a; is the minimum gas transmission volume of the unidirectional pipeline a; is the maximum gas transmission volume of the unidirectional pipeline a; is the maximum gas transmission volume of the bidirectional pipeline ab; The pipeline network pressure constraints include: pipeline maximum pressure constraints, demand node pressure constraints, resistance element pressure constraints, gas source node pressure constraints, and pipeline hydraulic pressure drop constraints; Natural gas flows in the gas transmission pipeline, forming an internal pressure in the pipeline. The internal pressure generates stress on the pipe wall; when the stress reaches the yield strength of the pipe material, it will cause permanent deformation of the pipe material and ultimately lead to the failure of the pipe material; therefore, the maximum pressure in the pipeline needs to be less than the design pressure. The relational expression of the pipeline maximum pressure constraint is: P a ≤MAOP Where P a is the pressure of pipe section a; MAOP is the maximum allowable operating pressure of the pipe section; At the demand node, the gas pressure should be greater than the minimum delivery pressure, and the pressure constraint relational expression for the demand node is: P i ≥ P i min = P i d where P i is the pressure of demand node i; P i min is the minimum delivery pressure of demand node i; P i d is the minimum delivery pressure of demand node i specified by the shipper; In physical modeling, pressure loss resistance elements caused by in-station pipelines, measuring devices, filters or partially closed valves are added, and the pressure constraint relational expression for the resistance elements is: In the formula, is the inlet pressure of the resistance element re; is the outlet pressure of the resistance element re; Q re is the gas throughput of the resistance element re; ζ is the pressure loss of the resistance element re; In the gas source node, the gas pressure should be greater than the minimum gas source pressure and not exceed the maximum gas source pressure; the pressure constraint relational expression for the gas source node is: In the formula, is the maximum pressure of the gas source node s; P s is the pressure of the gas source node s; is the minimum pressure of the gas source node s; The pipeline hydraulic pressure drop constraint calculates the pressure drop using the basic formula of the pipe network expressed in volume flow rate, and the constraint relational expression is: Wherein, Q is the volume flow rate of natural gas under standard conditions; C is the constant coefficient of pipeline hydraulic pressure drop; P in is the pipeline starting point pressure; P out is the pipeline end point pressure; D is the pipeline diameter; λ is the pipeline hydraulic friction coefficient; Z is the natural gas compression factor; Δ is the relative density of natural gas; T m is the average temperature of natural gas; L is the pipeline length; The described switch constraints include: compressor station switch constraint, compressor station configuration switch constraint and valve switch constraint; When the bypass valve is open, the compressor is closed and the compressor station does not increase pressure; when the compressor is closed and the bypass valve is closed, the compressor station is closed, and the inlet and outlet are not in circulation and cannot pass gas, and the flow rate is 0; when the compressor is open and the compressor station is open and the bypass valve is closed, the compressor station increases the pressure of the flowing gas; the internal switch constraint relationship of the compressor station: where S cs is the switch variable of the compressor station cs, 0 means off, 1 means on; is the bypass valve switch variable of the compressor station cs, 0 means off, 1 means on; Each startup situation of the in-station compressor is constrained by the inlet valve and the outlet valve. When a certain configuration of the compressor is turned on, the inlet and outlet valves are opened; when a certain configuration of the compressor is turned off, the inlet and outlet valves are closed; the configuration of the compressor station is related to the type, quantity, series or parallel connection method of the in-station compressors. Therefore, the configuration switch of the compressor station is coupled with the corresponding compressor switch, and the configuration switch constraint relationship of the compressor station: In the formula, is the switch variable of the compressor station cs, where 0 means off and 1 means on; is the switch variable of the compressor station cs, where 0 means off and 1 means on; is the configuration switch variable of the compressor station cs, where 0 means off and 1 means on; is the switch variable of the compressor c in the compressor station cs, where 0 means off and 1 means on; The valve plays a role in separating or connecting two nodes in the natural gas pipeline network; when the valve is open, it is default that the pressures at both ends of the valve are equal, and the valve flow rate does not exceed the maximum valve flow rate range; when the valve is closed, the two ends are not connected and the flow rate is 0, and the valve constraint relational expression: where S va is the switch variable of valve va, 0 indicates closed, and 1 indicates open; is the inlet pressure of valve va; is the outlet pressure of valve va; is the minimum volumetric flow rate of valve va; is the maximum volumetric flow rate of valve va; Q va is the volumetric flow rate of valve va; The described compressor constraints include: compressor flow rate constraint, compressor speed constraint, compressor pressure ratio constraint, compressor maximum outlet pressure constraint, compressor power constraint and compressor efficiency constraint; During the operation of the compressor, when the intake flow rate is too low, a surge phenomenon will occur; when the intake flow rate is too high, a choking phenomenon will occur; the occurrence of these two operating conditions will affect the normal operation of the compressor and may even cause equipment damage; therefore, it is necessary to limit the flow rate of the compressor, and the compressor flow rate constraint relational expression: Where Q cs,c is the flow rate of the compressor c in the compressor station cs; is the surge flow rate of the compressor c in the compressor station cs; r cs,c is the rotational speed of the compressor c in the compressor station cs; is the stagnation flow rate of the compressor c in the compressor station cs; A su , B su , C su , D su , A st , B st , C st , D st are the equation coefficients, obtained by fitting the compressor operation data; In the actual operation of the natural gas pipeline, the pipeline operation state is adjusted by controlling the speed. The compressor speed is related to the equipment performance and has a certain range limit, and the compressor speed constraint relational expression: In the formula, is the minimum allowable speed of the compressor c in the compressor station cs; is the maximum allowable speed of the compressor c in the compressor station cs; The pressure ratio refers to the ratio between the compressor discharge pressure and the intake pressure. In order to effectively control the compressor pressurization degree and ensure the safe and stable operation of the equipment, the compressor pressure ratio constraint relational expression: where ε cs,c is the compression ratio of compressor c in compressor station cs; is the minimum allowable compression ratio of compressor c in compressor station cs; is the maximum allowable compression ratio of compressor c in compressor station cs; The outlet pressure of the compressor after pressurization cannot be higher than its allowed maximum outlet pressure, and the compressor maximum outlet pressure constraint relational expression: In the formula, is the outlet pressure of the compressor c in the compressor station cs, in Pa; is the maximum allowable outlet pressure of the compressor c in the compressor station cs, in Pa; The described compressor power constraint relational expression: Where, H cs,c is the adiabatic head of the compressor c in the compressor station cs; η cs,c is the adiabatic efficiency of the compressor c in the compressor station cs; χ is the adiabatic index of the compressor; W cs,c is the power of the compressor c in the compressor station cs; is the minimum allowable power of the compressor c in the compressor station cs; is the maximum allowable power of the compressor c in the compressor station cs; The described compressor efficiency constraint relational expression: Wherein, a cs,c , b cs,c , c cs,c , d cs,c are the coefficients of the efficiency-flow rate-rotational speed curve equation of the compressor c in the gas compression station cs, and are obtained by fitting.

4. The natural gas pipeline network capacity order verification method according to claim 1, characterized in that The described model solution method adopts the external approximation method based on the equality relaxation strategy, and the specific steps are: S401: Relax the discrete 0-1 constraints of the binary variables into continuous variables, solve the initial non-linear programming sub-problem to obtain the optimal solution of the continuous variables; if the sub-problem directly returns a solution that meets the integer conditions, the algorithm terminates and outputs the result; otherwise, linearize the non-linear function based on the gradient information of the current solution, generate the linear approximation terms required for the outer approximation, and add them to the historical approximation set to gradually approximate the original problem structure; S402: Use the linear approximation terms accumulated in the historical iterations to construct the mixed-integer programming master problem model, integrating the relaxed equality and inequality constraints; solve the master problem to obtain the candidate assignments of the binary variables, and obtain the objective function boundary values: in convex problems, the objective value of the master problem is the global lower bound, and the boundary is updated strictly monotonically with the iteration process; for non-convex problems, this boundary is only for reference, and subsequent verification is required to ensure its effectiveness; S403: The non-linear programming sub-problem fixes the binary variable assignments provided by the master problem and re-solves the non-linear programming sub-problem to optimize the continuous variables; if the sub-problem is feasible, extract the gradient information of its solution to generate new linear approximation terms and update the master problem model to improve the approximation accuracy; if the sub-problem is infeasible, introduce slack variables through the generalized penalty function to correct the constraint conflicts, or directly prune the current invalid branches; if the sub-problem returns an integer feasible solution, terminate the algorithm and output the optimal solution; S404: For convex problems, when the gap between the boundary predicted by the master problem and the actual objective value of the sub-problem is less than the preset threshold, it is determined that the algorithm converges; for non-convex problems, a dynamic heuristic termination rule is adopted: if the objective function value of the sub-problem has not improved for several consecutive iterations, or a significant deviation of the slack variable from zero is detected, the calculation is terminated in advance to avoid wasting invalid resources; S405: Dynamically maintain the set of linear approximation terms to balance the scale of the master problem and the approximation accuracy: remove redundant or outdated approximation terms, and preferentially retain the high-quality linearization results close to the current solution; apply an adaptive penalty function weight to the approximation terms in the non-convex region to suppress their misleading influence on the master problem model; Ensure the robustness and solution efficiency of the algorithm in complex non-convex scenarios by periodically correcting the master problem structure.

Images