A method for evaluating the maximum and remaining transmission capacity of a natural gas pipeline network

Abstract

The present invention discloses a method for evaluating the maximum and remaining transmission capacity of a natural gas pipeline network, comprising the following steps: constructing a global pipeline network topology and performing pipeline network splitting processing; constructing a nonlinear optimization model for maximizing local pipeline transmission volume and solving the local pipeline maximum transmission capacity in parallel, taking into account the influence of station and pipeline constraints; constructing a global pipeline network maximum streamline optimization model, using the local pipeline maximum transmission capacity as an edge flow capacity constraint, and solving the pipeline network maximum transmission capacity taking into account the influence of the pipeline network topology; collecting the content of signed pipeline transmission orders, updating the global pipeline network maximum streamline optimization model, and solving the pipeline network remaining transmission capacity; supplementing the content of newly added pipeline transmission orders, updating the global pipeline network maximum streamline optimization model, and re-solving the latest pipeline network remaining transmission capacity. The present invention comprehensively considers station and pipeline constraints and complex topology, effectively reduces computational complexity through layering and splitting, and can quickly and accurately evaluate the actual transmission capacity of the pipeline network.

Description

Technical Field

[0001] The present invention relates to the field of natural gas pipeline network transmission capacity assessment, and in particular to a method for assessing the maximum transmission capacity and the remaining transmission capacity of a natural gas pipeline network. Background Art

[0002] As a critical infrastructure for energy transportation, the stable and efficient operation of natural gas pipeline networks is of great significance to energy supply security, economy, and low-carbon environmental protection. To optimize pipeline network operations, rationally allocate transmission capacity, meet growing transportation demand, and achieve flexible scheduling and management, it is crucial to accurately assess the actual transmission capacity of the pipeline network. Specifically, this involves fully evaluating the maximum transmission capacity and remaining transmission capacity of the pipeline network from multiple perspectives, providing important evaluation indicators for the feasibility evaluation of commercial pipeline transmission orders and the formulation of scheduling and operation plans. However, due to the increasing scale and complexity of the natural gas pipeline network, the transmission capacity of the pipeline network is subject to a combination of constraints imposed by upstream stations, distribution stations, and the operational constraints of the pipelines themselves. Furthermore, multiple pipelines interact through the complex topology of the pipeline network, making it difficult to efficiently and accurately assess the maximum transmission capacity and remaining transmission capacity.

[0003] Currently, common methods for assessing pipeline network capacity include manual judgment and numerical simulation. However, these methods have limitations in terms of accuracy and timeliness. The manual judgment method involves dispatching operators directly using the pipeline's designed capacity as the network's maximum capacity. However, the designed capacity only considers the operational constraints of a single pipeline under ideal conditions, making it difficult to accurately reflect the network's actual capacity and ineffectively capturing the network's remaining capacity. Numerical simulation can effectively assess both the maximum and remaining capacity of a pipeline network, but this method places high demands on the operator's skills and generally requires extensive simulation trials and a long time to obtain valid assessment results. This is particularly true for large-scale, complex pipeline networks containing numerous components, where the system suffers from high operational complexity, low assessment efficiency, and a high computational workload.

[0004] Currently, there is no efficient and accurate method for assessing the maximum and remaining capacity of natural gas pipeline networks, making it difficult to meet the timeliness and accuracy requirements of applications. To address this, a method for assessing the maximum and remaining capacity of natural gas pipeline networks has been developed. Based on a network splitting approach, combined with a local-first-global hierarchical calculation method, this method gradually considers the impact of pipelines and station components along the network, as well as the complex topology of the network on the capacity. This method can quickly and accurately assess the maximum and remaining capacity of natural gas pipelines and supports real-time, rapid, and accurate calculation of the current remaining capacity of the network for newly added pipeline orders. Summary of the Invention

[0005] In view of the above problems, the purpose of the present invention is to provide a method for evaluating the maximum transmission capacity and remaining transmission capacity of a natural gas pipeline network, which can quickly and accurately evaluate the actual maximum transmission capacity and remaining transmission capacity of each pipeline in the pipeline network.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the maximum transmission capacity and remaining transmission capacity of a natural gas pipeline network, comprising the following steps:

[0007] Step 1: Collect basic data of the pipe network structure, construct a global pipe network topology, and use the pipe network splitting method to split the global pipe network topology to obtain multiple local pipe topologies;

[0008] Step 2: For each local pipeline topology, basic pipeline operation data is collected to form multiple local pipeline capacity maximization optimization problems. Multiple local pipeline capacity maximization nonlinear optimization models are further established and solved in parallel to obtain the maximum transmission capacity of the local pipeline, taking into account the influence of stations along the line and pipeline operation constraints.

[0009] Step 3: Based on the global pipe network topology, a global pipe network maximum flow optimization problem is established. The local pipe maximum transport capacity obtained in step 2 is used as the edge flow capacity constraint condition for the corresponding pipe edge. A global pipe network maximum flow linear optimization model is constructed to solve the pipe network maximum transport capacity that takes into account the influence of the pipe network topology.

[0010] Step 4: Collect the contents of the signed pipeline transportation orders and use the edge flow capacity update adjustment method to update the global pipeline network maximum flow linear optimization model established in step 3 to obtain the remaining transportation capacity of the pipeline network;

[0011] Step 5: By updating and supplementing the content of the newly signed pipeline transportation order, the global pipeline network maximum flow linear optimization model established in step 3 is updated using the edge flow capacity update adjustment method, and the latest pipeline network remaining transportation capacity is obtained by re-solving.

[0012] Furthermore, in step 1, the basic data of the pipe network structure include: pipe number, pipe flow direction, pipe design transport capacity, pipe start node and end node, node number, and node type.

[0013] Furthermore, in step 1, the pipe network splitting method includes the following steps:

[0014] S11: identifying intersection nodes in the pipe network according to the global pipe network topology and the pipe flow direction, constructing a split node set, and breaking the global pipe network topology at the split nodes to obtain a preliminary split result of the pipe network structure;

[0015] S12: Based on the designed transport capacity of the pipeline, identify nodes where the designed transport capacity of the pipeline changes. At these nodes, analyze the preliminary splitting results of the pipeline network structure obtained in S11. If there are sub-transmission stations along the upstream and downstream pipelines of the node where the designed transport capacity of the pipeline changes, perform further splitting at this node to obtain the in-depth splitting results of the pipeline network structure.

[0016] S13: Perform node completion processing on the deep splitting result of the pipeline network structure obtained in S12, add a distribution station virtual node at the upstream pipeline splitting node of each splitting node, and add an upload station virtual node at the downstream pipeline splitting node of each splitting point to ensure the integrity of the pipeline structure and obtain the multiple local pipeline topological structures.

[0017] Furthermore, in step 2, the basic pipeline operation data includes: pipeline length, pipeline diameter, pipeline roughness, pipeline design maximum operating pressure, pipeline maximum allowable flow rate, allowable upload pressure range of the upload station, allowable upload volume range of the upload station, allowable download pressure range of the distribution station, allowable download volume range of the distribution station, allowable processing capacity range of the compressor station configuration equipment, allowable inlet and outlet pressure ranges of the compressor station, allowable upload and download pressure ranges of the gas storage, allowable upload and download volume ranges of the gas storage, injection and production operation status of the gas storage, and historical operating data of pressure and flow of each pipeline and node in the pipeline network.

[0018] Furthermore, in step 2, the local pipeline throughput maximization nonlinear optimization model includes a pipeline throughput maximization objective function, constraints, and decision variables:

[0019] S21: The objective function for maximizing pipeline throughput is to maximize the sum of the download volumes from the distribution stations along the pipeline and the gas storage during the injection period, as shown in formula (1):

[0020]

[0021] Where: F is the total pipeline capacity, i is the i-th gas storage facility along the pipeline, N s is the number of gas storage facilities along the pipeline, β i The injection and production operation status of the gas storage reservoir, the injection period is 1, the production period is 0, is the flow rate passing through the i-th gas storage, j is the j-th distribution station along the pipeline, N d is the number of distribution stations along the pipeline, is the download traffic of the j-th distribution station.

[0022] S22: The constraints include: pipeline pressure constraint, pipeline gas flow rate constraint, pipeline hydraulic pressure drop constraint, upload station upload pressure constraint, upload station upload flow constraint, sub-transmission station download pressure constraint, sub-transmission station download flow constraint, compressor station flow constraint, compressor station inlet and outlet pressure constraints, gas storage reservoir gas production and injection pressure constraint, gas storage reservoir gas production and injection flow constraint, and node flow balance constraint.

[0023] S23: The decision variables include: the flow rate of the i-th gas storage facility along the pipeline The flow rate of the jth distribution station along the pipeline The outlet pressure of the kth compressor station along the pipeline The loading volume of the first loading station along the pipeline As shown in formula (2):

[0024]

[0025] Furthermore, in step 3, the global pipeline network maximum flow optimization problem is specifically as follows: according to the global pipeline network topology structure described in step 1, based on graph theory, it is converted into a graph G(V,E) containing a node set V and an edge set E, and a virtual source node s and a virtual sink node t are established, and the pipeline network multi-source and multi-sink optimization problem is converted into a typical single-source and single-sink maximum flow optimization problem.

[0026] The node set V in the figure includes the distribution station nodes, upload station nodes, compressor station nodes, intermediate distribution station nodes, pressure regulating station nodes and gas storage nodes in the pipeline network, as well as the virtual source node s and the virtual sink node t.

[0027] The edge set E in the figure includes all pipelines in the pipeline network, the connection edges between the virtual source node and the upload station node, and the connection edges between the distribution station node and the virtual sink node.

[0028] Furthermore, in step 3, the global pipe network maximum flow linear optimization model includes a maximum flow optimization objective function, constraints, and decision variables:

[0029] S31: The maximum flow optimization objective function is to maximize the total inflow of the virtual sink node t, as shown in formula (3):

[0030]

[0031] Where: Z is the total flow to the virtual sink node t, f(u,t) is the flow from node u to the virtual sink node t through the edge (u,t);

[0032] S32: The constraints include node flow conservation constraints and edge flow capacity constraints.

[0033] S33: The decision variable is the flow f(u,v) of each edge (u,v)∈E in the graph G(V,E).

[0034] Furthermore, in step 4, the content of the signed pipeline transportation order includes the agreed upload node, download node, transportation route and transportation volume.

[0035] Furthermore, in step 4, the edge flow capacity update and adjustment method includes:

[0036] S41: Match the corresponding local pipelines in the pipeline network according to the collected pipeline transportation order content, and summarize and obtain the contracted transportation capacity of each local pipeline;

[0037] S42: Traverse each local pipeline, subtract the contracted transmission capacity of the local pipeline from the maximum transmission capacity of the local pipeline obtained in step 2, and obtain the remaining transmission capacity of the local pipeline;

[0038] S43: Based on the global pipe network maximum flow linear optimization model established in step 3, the flow capacity constraint of the corresponding edge of the local pipe is updated to the remaining transport capacity of the local pipe, and an updated global pipe network maximum flow linear optimization model is obtained.

[0039] Due to the adoption of the above technical solutions, the present invention has the following advantages: 1. The present invention fully and comprehensively considers the operation constraints of stations and pipelines along the pipeline network, and further integrates the influence of the complex topological structure of the pipeline network. Through a hierarchical approach of first local and then global, the present invention gradually considers the influence of pipelines and station components along the pipeline network, as well as the complex topological structure of the pipeline network on the transmission capacity, and can quickly and accurately evaluate the maximum transmission capacity and the remaining transmission capacity; 2. The present invention establishes a pipeline network splitting method to reasonably decompose the pipeline transmission capacity maximization nonlinear optimization problem into multiple local pipeline sub-problems, effectively reducing the complexity of solving large-scale nonlinear optimization problems. , further combined with parallel computing technology, to achieve a significant improvement in solution calculation efficiency; 3. Based on the local pipeline maximum transmission capacity assessment results, the present invention establishes and solves the global pipeline network maximum streamline linear optimization model, thereby realizing the supplementary consideration of the impact of the complex topological structure of the pipeline network on the transmission capacity that has not been fully considered due to the pipeline network splitting, and ensuring the accuracy of the pipeline network maximum transmission capacity assessment results; 4. The present invention establishes a side flow capacity update and adjustment method, which effectively takes into account the pipeline network operator’s signed pipeline transmission orders, and can further evaluate the remaining transmission capacity of the pipeline network, and for real-time new pipeline transmission orders, establishes the latest pipeline network remaining transmission capacity update acquisition method. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 A flow chart of a method for evaluating the maximum transmission capacity and remaining transmission capacity of a natural gas pipeline network provided by an embodiment of the present invention;

[0042] Figure 2 Schematic diagram of the global pipe network topology structure of a pipe network provided by an embodiment of the present invention

[0043] Figure 3 Schematic diagram of the local pipeline topology structure after the splitting of a pipeline network provided by an embodiment of the present invention DETAILED DESCRIPTION

[0044] Natural gas pipeline networks consist of numerous components, including loading stations, distribution stations, compressor stations, and gas storage facilities, and have complex topological connections. This makes the actual transport capacity of each pipeline in the network subject to the combined influence of multiple factors, making it difficult to quickly and accurately assess. The present invention provides a method for assessing the maximum and remaining transport capacity of a natural gas pipeline network. This method, through a hierarchical approach, first locally and then globally, gradually considers the impact of pipelines and station components along the network, as well as the network's complex topological structure, on transport capacity. This method comprehensively considers the multiple impacts on pipeline transport capacity while effectively reducing computational complexity. This method provides a reliable basis for feasibility evaluation of commercial pipeline transportation contracts and the formulation of scheduling and operation plans.

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 are within the scope of protection of the present invention.

[0046] The present invention provides a method for evaluating the maximum transmission capacity and the remaining transmission capacity of a natural gas pipeline network. Figure 1 As shown, the following steps are included:

[0047] Step 1: Collect basic data of the pipe network structure, construct a global pipe network topology, and use the pipe network splitting method to split the global pipe network topology to obtain multiple local pipe topologies;

[0048] Specifically, the basic data of the pipeline network structure include: pipeline number, pipeline flow direction, pipeline design transmission capacity, pipeline starting node and ending node, node number, and node type.

[0049] In practice, the network splitting method reduces the scale of the optimization problem, allowing the nonlinear hydraulic equations of the pipeline to be incorporated into the optimization model, laying the foundation for the accurate solution of the pipeline's transport capacity. The method includes the following steps:

[0050] S11: identifying intersection nodes in the pipe network according to the global pipe network topology and the pipe flow direction, constructing a split node set, and breaking the global pipe network topology at the split nodes to obtain a preliminary split result of the pipe network structure;

[0051] S12: Based on the designed transport capacity of the pipeline, identify nodes where the designed transport capacity of the pipeline changes. At these nodes, analyze the preliminary splitting results of the pipeline network structure obtained in S11. If there are sub-transmission stations along the upstream and downstream pipelines of the node where the designed transport capacity of the pipeline changes, perform further splitting at this node to obtain the in-depth splitting results of the pipeline network structure.

[0052] S13: Perform node completion processing on the deep splitting result of the pipeline network structure obtained in S12, add a distribution station virtual node at the upstream pipeline splitting node of each splitting node, and add an upload station virtual node at the downstream pipeline splitting node of each splitting point to ensure the integrity of the pipeline structure and obtain the multiple local pipeline topological structures.

[0053] Step 2: For each local pipeline topology, basic pipeline operation data is collected to form multiple local pipeline capacity maximization optimization problems. Multiple local pipeline capacity maximization nonlinear optimization models are further established and solved in parallel to obtain the maximum transmission capacity of the local pipeline, taking into account the influence of stations along the line and pipeline operation constraints.

[0054] Specifically, the basic data of pipeline operation include: pipeline length, pipeline diameter, pipeline roughness, pipeline designed maximum operating pressure, pipeline maximum allowable flow rate, allowable upload pressure range of the upload station, allowable upload volume range of the upload station, allowable download pressure range of the distribution station, allowable download volume range of the distribution station, allowable processing capacity range of the compressor station configuration equipment, allowable inlet and outlet pressure range of the compressor station, allowable upload and download pressure range of the gas storage, allowable upload and download volume range of the gas storage, injection and production operation status of the gas storage, and historical operation data of pressure and flow of each pipeline and node in the pipeline network.

[0055] In specific implementation, the local pipeline capacity maximization nonlinear optimization model takes the maximization of the sum of the download volume of the pipeline distribution stations and gas storage during the injection period as the optimization goal, comprehensively considering the pressure and flow constraints involved in the stations along the pipeline, as well as the operation constraints of the pipeline itself, so as to solve the maximum transmission capacity of the local pipeline under the comprehensive consideration of the influence of the stations along the pipeline and the pipeline operation constraints. The pipeline capacity maximization nonlinear optimization model includes the maximum capacity optimization goal, constraints and decision variables:

[0056] S21: The objective function for maximizing pipeline delivery is to maximize the sum of the download volumes from the distribution stations along the pipeline and the gas storage during the injection period, as shown in formula (1);

[0057] S22: Constraints include: pipeline pressure constraint, pipeline gas flow rate constraint, pipeline hydraulic pressure drop constraint, upload station upload pressure constraint, upload station upload flow constraint, distribution station download pressure constraint, distribution station download flow constraint, compressor station flow constraint, compressor station inlet and outlet pressure constraints, gas storage reservoir gas production and injection pressure constraint, gas storage reservoir gas production and injection flow constraint, and node flow balance constraint.

[0058] Specifically, the pipeline pressure constraint represents the starting point of the pipeline and endpoint pressure Less than the maximum design pressure of the pipeline As shown in formula (4):

[0059]

[0060] Specifically, the pipeline gas flow rate constraint represents the gas flow rate in the pipeline Less than the maximum erosion velocity As shown in formula (5):

[0061]

[0062] Specifically, the pipeline hydraulic pressure drop constraint indicates that the pressure at both ends of the pipeline node should satisfy the hydraulic pressure drop equation, as shown in Equation (6):

[0063]

[0064] Where: is the mass flow rate of pipe (i, j), kg / s, and are the starting and ending pressures of pipeline (i, j), Pa, D (i,j) is the inner diameter of the pipe (i, j), m, λ (i,j) is the hydraulic friction coefficient of pipeline (i, j), Z is the natural gas compression factor; R is the natural gas constant, T (i,j) is the average temperature of natural gas in pipeline (i, j), K, L(i,j) is the length of pipe (i, j), m.

[0065] The present invention uses the Colebrook-White formula to calculate the friction coefficient, which has the advantage of high accuracy, as shown in formula (7):

[0066]

[0067] Where: ε (i,j) is the absolute roughness of the pipe (i, j), m, and Re is the Reynolds number of the fluid in the pipe (i, j).

[0068] Specifically, the upload pressure constraint of the upload station and the upload flow constraint of the upload station represent the upload pressure of the upload station respectively. and upload traffic Satisfy the maximum Minimum Upload pressure and maximum Minimum The upload traffic limit is as shown in formula (8):

[0069]

[0070] Specifically, the transmission station download pressure constraint and the transmission station download flow constraint represent the download pressure of the transmission station. and download traffic Satisfy the maximum Minimum Upload pressure and maximum Minimum The upload traffic limit is as shown in formula (9):

[0071]

[0072] Specifically, the compressor station flow constraint characterizes the flow through the compressor station Satisfy the processing capacity constraints of the equipment within the station, as shown in formula (10):

[0073]

[0074] Specifically, the compressor station inlet and outlet pressure constraints characterize the inlet Exit The pressure meets the operating pressure limit of the compressor, as shown in formula (11):

[0075]

[0076] Specifically, the gas production and injection pressure constraints of the gas storage reservoir represent the gas production pressure during the gas production period. and injection pressure during the injection period Satisfy the feasible range constraints, as shown in formula (12):

[0077]

[0078] Specifically, the gas production and injection flow constraints of the gas storage reservoir represent the gas production flow rate during the gas storage period. and gas injection flow rate during the gas injection period Satisfy the feasible range constraints, as shown in formula (13):

[0079]

[0080] Specifically, the node flow balance constraint represents that according to the law of conservation of mass, the inflow of any node should be equal to the outflow, as shown in Equation (14):

[0081]

[0082] Where: is the absolute volume flow rate between the i-th node and the j-th element, m 3 / s,α (i,j) To represent the flow direction between the i-th node and the j-th element, if the flow is from the j-th element to the i-th node, it is -1, if the flow is from the i-th node to the j-th element, it is 1, U i is the set of components connected to the i-th node.

[0083] S23: Decision variables include: the flow rate of the i-th gas storage facility along the pipeline The flow rate of the jth distribution station along the pipeline The outlet pressure of the kth compressor station along the pipeline The loading volume of the first loading station along the pipeline As shown in formula (2).

[0084] In specific implementation, the above-mentioned nonlinear optimization model for maximizing local pipeline throughput will be adopted, combined with the basic pipeline operation data of multiple local pipelines after splitting, and multiple different nonlinear optimization models for maximizing local pipeline throughput will be established accordingly. The model solution can adopt heuristic optimization algorithms such as genetic algorithm or particle swarm algorithm, which has good application effect.

[0085] In specific implementation, the multiple different local pipeline transmission maximization nonlinear optimization models established are independent of each other when solving. Parallel computing technology can be used to achieve simultaneous parallel solutions to quickly obtain the local pipeline maximum transmission capacity that comprehensively considers the influence of stations along the line and pipeline operation constraints.

[0086] Step 3: Based on the global pipe network topology, a global pipe network maximum flow optimization problem is established. The local pipe maximum transport capacity obtained in step 2 is used as the edge flow capacity constraint condition for the corresponding pipe edge. A global pipe network maximum flow linear optimization model is constructed to solve the pipe network maximum transport capacity that takes into account the influence of the pipe network topology.

[0087] In the specific implementation, the global pipeline network maximum flow optimization problem is specifically as follows: according to the global pipeline network topology structure described in step 1, it is converted into a graph G(V,E) containing a node set V and an edge set E based on graph theory, and a virtual source node s and a virtual sink node t are established to transform the pipeline network multi-source and multi-sink optimization problem into a typical single-source and single-sink maximum flow optimization problem.

[0088] Specifically, the node set V in the figure includes the distribution station nodes, upload station nodes, compressor station nodes, intermediate distribution station nodes, pressure regulating station nodes and gas storage nodes in the pipeline network, as well as the virtual source node s and the virtual sink node t.

[0089] Specifically, the edge set E in the figure includes each pipeline in the pipeline network, the connection edges between the virtual source node and the upload station node, and the connection edges between the distribution station node and the virtual sink node.

[0090] In specific implementation, the virtual source node s will be connected to all actual upload station nodes, and the edge flow capacity of each edge between the virtual source node and the actual upload station node is set to the maximum upload flow of the upload station node, indicating that the flow can flow from the virtual source node to all actual distribution station nodes, realizing the conversion of multiple sources into a single source.

[0091] In specific implementation, all actual substation nodes will be connected to the virtual sink node t, and the edge flow capacity of each edge between the actual substation node and the virtual sink node is set to the maximum download flow of the substation, indicating that the flow can flow from the actual substation node to the virtual sink node, realizing the conversion of multiple sinks into a single sink.

[0092] In specific implementation, since the maximum conveying capacity of the local pipeline has been accurately evaluated in step 2, the global pipeline network maximum flow linear optimization model can directly use the local pipeline maximum conveying capacity as the edge flow capacity constraint of the corresponding edge of each pipeline in the pipeline network, so that the model no longer involves complex nonlinear hydraulic constraints. It only needs to consider the edge flow capacity constraint and the overall topological structure constraint of the pipeline network to construct a simple linear optimization model, which greatly reduces the computational complexity and amount of computation for evaluating the maximum conveying capacity of the pipeline network.

[0093] Specifically, the global pipe network maximum flow linear optimization model includes the maximum flow optimization objective function, constraints and decision variables:

[0094] S31: The maximum flow optimization objective function is to maximize the total inflow of the virtual sink node t, as shown in formula (3);

[0095] S32: Constraints include node flow conservation constraints and edge flow capacity constraints;

[0096] Specifically, the node flow conservation constraint states that the incoming flow f(u,i) of each intermediate node i is equal to the outgoing flow f(i,v), as shown in Equation (15):

[0097]

[0098] Specifically, the edge flow capacity constraint indicates that the flow f(u,v) of each edge cannot exceed its capacity c(u,v), as shown in Equation (16):

[0099]

[0100] S33: The decision variable is the flow f(u,v) of each edge (u,v)∈E in the graph G(V,E).

[0101] In specific implementation, the established global pipeline network maximum flow linear optimization model can be solved using the Ford-Fulkerson algorithm or the Edmonds-Karp algorithm based on the augmenting path theory. It can quickly seek the solution to the maximum flow and can handle larger networks. Especially in the case of complex pipeline network topology, it can also ensure high accuracy and computational efficiency, thereby quickly solving the maximum transmission capacity of the pipeline network that takes into account the influence of the pipeline network topology structure.

[0102] In the specific implementation, the local pipeline maximum transmission capacity obtained by comprehensively considering the influence of stations and pipeline operation constraints along the line obtained in step 2 is brought into the global pipeline network maximum flow linear optimization model as the edge flow capacity of the corresponding edge of the pipeline. The influence of the pipeline network topology structure that is not fully considered due to the pipeline network splitting is further supplemented. In this way, the various effects of pipelines and station elements along the line in the pipeline network, as well as the complex topology structure of the pipeline network on the transmission capacity are comprehensively considered, thereby achieving an efficient and accurate evaluation of the maximum transmission capacity of the pipeline network.

[0103] Step 4: Collect the contents of the signed pipeline transportation orders and use the edge flow capacity update adjustment method to update the global pipeline network maximum flow linear optimization model established in step 3 to obtain the remaining transportation capacity of the pipeline network;

[0104] Specifically, the content of the signed pipeline transportation order includes the agreed upload node, download node, transportation route and transportation volume.

[0105] In specific implementation, the edge flow capacity update and adjustment method updates the edge flow capacity constraints in the global pipeline network maximum flow linear optimization model by considering the transportation demand of the contracted pipeline transportation, thereby evaluating the remaining transportation capacity of the pipeline network. Specifically, it includes the following steps:

[0106] S41: Match the corresponding local pipelines in the pipeline network according to the collected pipeline transportation order content, and summarize and obtain the contracted transportation capacity of each local pipeline;

[0107] S42: Traverse each local pipeline, subtract the contracted transmission capacity of the local pipeline from the maximum transmission capacity of the local pipeline obtained in step 2, and obtain the remaining transmission capacity of the local pipeline;

[0108] S43: Based on the global pipe network maximum flow linear optimization model established in step 3, the flow capacity constraint of the corresponding edge of the local pipe is updated to the remaining transport capacity of the local pipe, and an updated global pipe network maximum flow linear optimization model is obtained.

[0109] In specific implementation, in step 4, by summarizing the contracted transmission capacity of local pipelines, the remaining transmission capacity of local pipelines is first obtained from a local perspective, and then the remaining transmission capacity of the pipeline network is obtained by solving the updated global pipeline network maximum streamline linear optimization model. This can effectively combine local and global information and fully consider the impact of factors such as uneven distribution of pipeline network transmission on the overall remaining transmission capacity.

[0110] In specific implementation, the global pipe network maximum flow linear optimization model updated in step 4 can be solved using the same solution algorithm as in step 3.

[0111] Step 5: By updating and supplementing the content of the newly signed pipeline transportation order, the global pipeline network maximum flow linear optimization model established in step 3 is updated using the edge flow capacity update adjustment method, and the latest pipeline network remaining transportation capacity is obtained by re-solving.

[0112] In practice, to accommodate new pipeline transport orders from pipeline network operators, step 5 establishes a real-time update evaluation mechanism based on step 4. By updating and supplementing the newly signed pipeline transport orders, the global pipeline network maximum flow linear optimization model is used to solve and obtain the current and latest remaining transport capacity of the pipeline network.

[0113] In specific implementation, based on the completion of steps 1-4, when there is a change in the pipeline transportation order, the current latest pipeline network remaining transportation capacity can be updated and evaluated by directly adopting step 5, which can effectively reduce the evaluation calculation amount and meet the real-time update evaluation needs of the pipeline network remaining transportation capacity.

[0114] Specifically, updating and supplementing the content of the newly signed pipeline transportation order is to further add the newly signed transportation demand on the basis of the signed transportation capacity of each local pipeline summarized in step 4, and to summarize and obtain the current latest signed transportation capacity of each local pipeline.

[0115] Example:

[0116] The maximum transmission capacity and remaining transmission capacity evaluation method of a natural gas pipeline network of the present invention is adopted, and the relevant data required by the method are combined to evaluate the maximum transmission capacity and remaining transmission capacity of a certain pipeline network, so as to further illustrate the present invention and verify the reliability and effectiveness of the present invention.

[0117] The basic data of a pipe network structure and the global pipe network topology are as follows: Figure 2 As shown in the figure, the pipeline network is a regional trunk gas supply network including high-pressure pipelines and low-pressure pipelines. The local pipeline topology after the pipeline network is split is as follows: Figure 3 As shown in FIG, there are 9 local pipeline topologies in total, among which upload station 4, upload station 5, sub-transmission station 12, sub-transmission station 13 and sub-transmission station 14 are virtual nodes added to ensure the integrity of the local pipeline structure.

[0118] The basic pipeline operation data corresponding to each local pipeline topology structure are shown in Table 1.

[0119] Table 1 Basic operation data of pipeline

[0120]

[0121]

[0122]

[0123] Based on the local pipeline topology and basic pipeline operation data, multiple local pipeline capacity maximization optimization problems are constructed. Correspondingly, multiple local pipeline capacity maximization nonlinear optimization models are established. The maximum transmission capacity of the local pipeline is obtained by comprehensively considering the influence of stations along the line and pipeline operation constraints, as shown in Table 2.

[0124] Table 2 Evaluation results of the maximum transport capacity of local pipelines

[0125] Pipeline Number <![CDATA[Local maximum conveying capacity of pipeline (m 3 / s)]]> Pipeline Number <![CDATA[Local maximum conveying capacity of pipeline (m 3 / s)]]> Pipeline 1 110.00 Pipeline 11 133.07 Pipeline 2 108.23 Pipeline 12 118.07 Pipeline 3 470.00 Pipeline 13 103.07 Pipeline 4 450.00 Pipeline 14 93.07 Pipeline 5 450.00 Pipeline 15 83.07 Pipeline 6 104.24 Pipeline 16 14.00 Pipeline 7 158.44 Pipeline 17 25.00 Pipeline 8 175.00 Pipeline 18 111.16 Pipeline 9 175.00 Pipeline 19 6.54 Pipeline 10 133.07 Pipeline 20 6.54

[0126] According to the global pipeline network topology and the global pipeline network maximum flow optimization problem, the local pipeline maximum transport capacity is used as the edge flow capacity constraint condition of the corresponding edge. A global pipeline network maximum flow linear optimization model is constructed, and the maximum transport capacity of the pipeline network considering the influence of the pipeline network topology is obtained as shown in Table 3.

[0127] Table 3 Evaluation results of the maximum transport capacity of the pipeline network

[0128]

[0129]

[0130] Based on the results of the pipeline network's maximum transport capacity assessment, the established global pipeline network maximum flow linear optimization model was updated by collecting the details of signed pipeline transport orders and combining them with the edge flow capacity update and adjustment method of the present invention to obtain the remaining transport capacity of the pipeline network. The details of the signed pipeline transport orders for the pipeline network are shown in Table 4.

[0131] Table 4 Contents of pipeline transportation orders signed by the pipeline network

[0132] Shipper Upload Node Download Node Conveyor path Transport capacity Shipper 1 Upload Station 1 Substation 7 Via pipe 10, pipe 11 and pipe 12 700,000 square meters / day Shipper 2 Upload Station 2 Substation 4 Via pipe 3, pipe 4, pipe 5 and pipe 7 5 million cubic meters / day

[0133] According to the content of the pipeline transportation orders signed by the pipeline network, the established global pipeline network maximum flow linear optimization model is updated, and the remaining transportation capacity of the pipeline network is obtained as shown in Table 5.

[0134] Table 5 Evaluation results of the remaining transport capacity of the pipeline network

[0135] Pipeline Number <![CDATA[Global remaining conveying capacity of pipeline (m 3 / s)]]> Pipeline Number <![CDATA[Global remaining conveying capacity of pipeline (m 3 / s)]]> Pipeline 1 110.00 Pipeline 11 101.11 Pipeline 2 84.60 Pipeline 12 93.46 Pipeline 3 395.57 Pipeline 13 85.32 Pipeline 4 378.65 Pipeline 14 84.75 Pipeline 5 378.65 Pipeline 15 82.09 Pipeline 6 104.24 Pipeline 16 14.00 Pipeline 7 100.57 Pipeline 17 25.00 Pipeline 8 175.00 Pipeline 18 111.16 Pipeline 9 175.00 Pipeline 19 1.16 Pipeline 10 101.11 Pipeline 20 1.16

[0136] In addition, by updating and supplementing the content of newly signed pipeline transportation orders, adopting the edge flow capacity update adjustment method, and continuously updating the global pipeline network maximum flow linear optimization model, the present invention can support real-time, fast, and accurate solution to obtain the current latest pipeline network remaining transportation capacity.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. All other embodiments obtained by ordinary technicians in this field without making any creative work should fall within the scope of protection of the embodiment scheme of this specification.

Claims

1. A method for evaluating the maximum transmission capacity and remaining transmission capacity of a natural gas pipeline network, characterized in that: The following steps are involved: Step 1: Collect basic data of the pipe network structure, construct a global pipe network topology, and use the pipe network splitting method to split the global pipe network topology to obtain multiple local pipe topologies; Step 2: For each local pipeline topology, basic pipeline operation data is collected to form multiple local pipeline capacity maximization optimization problems. Multiple local pipeline capacity maximization nonlinear optimization models are further established and solved in parallel to obtain the maximum transmission capacity of the local pipeline, taking into account the influence of stations along the line and pipeline operation constraints. Step 3: Based on the global pipe network topology, a global pipe network maximum flow optimization problem is established. The local pipe maximum transport capacity obtained in step 2 is used as the edge flow capacity constraint condition for the corresponding pipe edge. A global pipe network maximum flow linear optimization model is constructed to solve the pipe network maximum transport capacity that takes into account the influence of the pipe network topology. Step 4: Collect the contents of the signed pipeline transportation orders and use the edge flow capacity update adjustment method to update the global pipeline network maximum flow linear optimization model established in step 3 to obtain the remaining transportation capacity of the pipeline network; Step 5: By updating the real-time pipeline order content, the global pipeline network maximum flow linear optimization model established in step 3 is updated using the edge flow capacity update adjustment method, and the latest pipeline network remaining transmission capacity is obtained by re-solving; In step 1, the pipe network splitting method includes the following steps: S11: identifying intersection nodes in the pipe network according to the global pipe network topology and the pipe flow direction, constructing a split node set, and breaking the global pipe network topology at the split nodes to obtain a preliminary split result of the pipe network structure; S12: Based on the designed transport capacity of the pipeline, identify nodes where the designed transport capacity of the pipeline changes. At these nodes, analyze the preliminary splitting results of the pipeline network structure obtained in S11. If there are sub-transmission stations along the upstream and downstream pipelines of the node where the designed transport capacity of the pipeline changes, perform further splitting at this node to obtain the in-depth splitting results of the pipeline network structure. S13: performing node completion processing on the deep splitting result of the pipeline network structure obtained in S12, adding a distribution station virtual node to the upstream pipeline splitting node of each splitting node, and adding an upload station virtual node to the downstream pipeline splitting node of each splitting point, to ensure the integrity of the pipeline structure, and obtain the multiple local pipeline topological structures; In step 3, the global pipe network maximum flow optimization problem is specifically: S31: Based on the global pipe network topology structure in step 1, the graph G(V,E) is converted into a graph containing a node set V and an edge set E based on graph theory, and a virtual source node s and a virtual sink node t are established to transform the pipe network multi-source and multi-sink optimization problem into a typical single-source and single-sink maximum flow optimization problem; S32: The node set V in the figure includes the distribution station nodes, upload station nodes, compressor station nodes, intermediate distribution station nodes, pressure regulating station nodes and gas storage nodes in the pipeline network, as well as the virtual source node s and the virtual sink node t; S33: The edge set E in the figure includes each pipeline in the pipeline network, the connection edges between the virtual source node and the upload station node, and the connection edges between the distribution station node and the virtual sink node; In step 4, the edge flow capacity update and adjustment method includes: S41: Match the corresponding local pipelines in the pipeline network according to the collected pipeline transportation order content, and summarize and obtain the contracted transportation capacity of each local pipeline; S42: Traverse each local pipeline, subtract the contracted transmission capacity of the local pipeline from the maximum transmission capacity of the local pipeline obtained in step 2, and obtain the remaining transmission capacity of the local pipeline; S43: Based on the global pipe network maximum flow linear optimization model established in step 3, the flow capacity constraint of the corresponding edge of the local pipe is updated to the remaining transport capacity of the local pipe, and an updated global pipe network maximum flow linear optimization model is obtained.

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