CN116011728A - A pipe capacity optimization distribution method, system, medium and equipment - Google Patents

Abstract

The invention discloses a pipe capacity optimal allocation method, a system, a medium and equipment, and relates to the field of natural gas pipeline transportation economy analysis. The method comprises the following steps: obtaining a plurality of constraint conditions of a pipeline to be distributed, and constructing a pipe capacity distribution model according to the constructed objective function, the constraint conditions and the decision variables; and carrying out linearization treatment on the pipe capacity distribution model, combining basic parameters of the pipe to be distributed according to the treated pipe capacity distribution model to obtain a pipe capacity optimizing distribution result of the pipe to be distributed, maximizing pipe output income of a pipeline operation company, improving the utilization rate of each pipe as much as possible, improving gas satisfaction of users, linearizing nonlinear constraint in the model by adopting a piecewise linearization method, and improving solving efficiency of the model to obtain a global optimal solution.

Description

A pipe capacity optimization allocation method, system, medium and device

Technical Field

The present invention relates to the field of economic analysis of natural gas pipeline transportation, and in particular to a pipeline capacity optimization allocation method, system, medium and equipment.

Background Art

The research on pipeline capacity allocation scheme in my country is still in its infancy. At present, few scholars in China have conducted research on this aspect. Therefore, the current pipeline capacity allocation method in Europe is used as a reference. The current research on pipeline capacity allocation method is mainly focused on the research of pipeline capacity allocation algorithm. For example, PRISMA, the largest pipeline capacity allocation platform in Europe, uses the rising clock auction algorithm for marketing long-term products, while the uniform price auction algorithm is used for marketing short-term products. Some scholars have designed auction algorithms for pipeline transportation capacity allocation, and designed auction algorithms based on net present value allocation, proportional allocation, and optimal allocation.

The above research has some shortcomings: First, because the allocation algorithm of pipeline capacity in Europe is the focus of research, and the actual pipeline operation status is not taken into account, the allocation obtained by this allocation algorithm only exists in theory. In actual implementation, there may be a situation where natural gas cannot be transported to the shipper through the pipeline. Second, because the allocation priorities of different types of users are different, and because the demand for pipeline services of users in a year will fluctuate due to various factors, the current allocation algorithm in Europe does not take into account the allocation priority factors and the fluctuation factors of pipeline demand in different periods.

Summary of the invention

The technical problem to be solved by the present invention is to provide a pipe capacity optimization allocation method, system, medium and equipment in view of the deficiencies in the prior art.

The technical solution of the present invention to solve the above technical problems is as follows:

A pipe capacity optimization allocation method, comprising:

Get multiple constraints of the pipeline to be allocated;

Construct a pipe capacity allocation model based on the constructed objective function, multiple constraints and decision variables;

The pipe capacity allocation model is linearized, and according to the processed pipe capacity allocation model and in combination with basic parameters of the pipe to be allocated, an optimized pipe capacity allocation result of the pipe to be allocated is obtained.

The beneficial effects of the present invention are as follows: the present invention constructs a pipe capacity allocation model according to a constructed objective function, multiple constraints and decision variables, linearizes the pipe capacity allocation model, obtains the pipe capacity optimization allocation result of the pipeline to be allocated according to the processed pipe capacity allocation model and the basic parameters of the pipeline to be allocated, thereby maximizing the pipeline transportation revenue of the pipeline operating company, maximizing the utilization rate of each pipeline, and improving the gas satisfaction of users. The nonlinear constraints in the model are linearized by a piecewise linearization method, and the model solving efficiency is improved to obtain the global optimal solution.

Furthermore, it also includes: constructing the objective function according to the pipeline net present value.

Furthermore, the plurality of constraints include:

Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints.

Furthermore, the basic parameters include: shipper allocation demand information, pipe network information and hydraulic parameters.

Furthermore, the shipper allocation demand information includes: gas supplier information, upload site and upload pipeline information, download site and download pipeline information, user pressure demand information, user specified demand quantity information, application service time information, acceptance path adjustment information and gas supply interruption information.

The pipeline network information includes: pipeline topology of the natural gas pipeline network system, unit pipeline transportation cost, pipeline design transmission capacity, pipeline length, pipeline inner diameter, pipeline pressure bearing capacity, pipeline minimum gas transmission pressure and types of each site.

The hydraulic parameters include: physical parameters of natural gas, compression capacity of the compressor station, hydraulic friction coefficient and pipeline wall roughness.

Another technical solution of the present invention to solve the above technical problems is as follows:

A pipe capacity optimization allocation system comprises: a constraint acquisition module, a pipe capacity allocation construction module and a pipe capacity allocation module;

The constraint acquisition module is used to acquire multiple constraint conditions of the pipeline to be allocated;

The pipe capacity allocation construction module is used to construct a pipe capacity allocation model according to the constructed objective function, multiple constraints and decision variables;

The pipe capacity allocation module is used to perform linearization processing on the pipe capacity allocation model, and obtain the pipe capacity optimization allocation result of the pipe to be allocated according to the processed pipe capacity allocation model and the basic parameters of the pipe to be allocated.

The beneficial effects of the present invention are as follows: the present invention constructs a pipe capacity allocation model according to a constructed objective function, multiple constraints and decision variables, linearizes the pipe capacity allocation model, obtains the pipe capacity optimization allocation result of the pipeline to be allocated according to the processed pipe capacity allocation model and the basic parameters of the pipeline to be allocated, thereby maximizing the pipeline transportation revenue of the pipeline operating company, maximizing the utilization rate of each pipeline, and improving the gas satisfaction of users. The nonlinear constraints in the model are linearized by a piecewise linearization method, thereby improving the solution efficiency of the model and obtaining the global optimal solution.

Furthermore, it also includes: an objective function construction module, which constructs the objective function according to the pipeline net present value.

Furthermore, the plurality of constraints include:

Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints.

Another technical solution of the present invention to solve the above technical problems is as follows:

A storage medium stores instructions, and when a computer reads the instructions, the computer executes a pipe capacity optimization allocation method as described in any of the above schemes.

Another technical solution of the present invention to solve the above technical problems is as follows:

An electronic device comprises a processor and the storage medium described in the above scheme, wherein the processor executes instructions in the storage medium.

Advantages of additional aspects of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a method for optimizing pipe capacity allocation provided by an embodiment of the present invention;

FIG2 is a structural block diagram of a pipe capacity optimization allocation system provided by an embodiment of the present invention;

FIG3 is a schematic diagram of an optimization model calculation example provided by another embodiment of the present invention;

FIG. 4 is a schematic diagram of allocating pipeline transport volume to each user according to another embodiment of the present invention.

DETAILED DESCRIPTION

The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

Since the contract signed between the pipeline company and the shipper is multi-period, and the pipeline transportation capacity required by the shipper in different periods may be different, it is necessary to consider the nature of the shipper in different periods and the required pipeline transportation volume of the shipper to obtain the pipeline capacity allocation plan for different periods, so as to meet the gas demand of the shipper in different periods. The focus of this study is to design the pipeline capacity allocation method considering the characteristics of multiple periods and multiple users. The purpose of designing this method is to obtain the "optimal and feasible" pipeline capacity allocation plan under the condition of meeting the needs of different types of users in multiple periods. Among them, "optimal" means that the pipeline company's pipeline transportation income net present value is maximized, user satisfaction is the highest, and the utilization rate of each section of the pipeline is the highest. "Feasible" means that the obtained pipeline capacity allocation plan needs to meet the operation-related constraints of the pipeline operation, such as pipeline network hydraulic characteristics constraints, pipeline transportation capacity constraints, pipeline compressor station constraints, pipeline pressure bearing capacity constraints, etc.

In the process of pipeline capacity allocation, since pipeline capacity allocation is carried out after the pipeline operator and the shipper sign a gas transmission contract, when designing the allocation method, the boundary condition that must be met is the pipeline transmission volume signed by the user. When considering this boundary condition, since the importance of different types of users is different, the amount of transmission volume that meets their contract can be appropriately adjusted. Under the boundary condition, the maximum net present value of pipeline transmission income, pipeline transmission volume and pipeline transmission path are solved.

As shown in FIG1 , a pipe capacity optimization allocation method provided by an embodiment of the present invention includes:

Obtain multiple constraints for the pipeline to be allocated; it should be noted that the multiple constraints may include:

Construct a pipe capacity allocation model based on the constructed objective function, multiple constraints and decision variables;

The pipe capacity allocation model is linearized, and the pipe capacity optimization allocation result of the pipe to be allocated is obtained according to the processed pipe capacity allocation model and the basic parameters of the pipe to be allocated. It should be noted that the basic parameters may include: the allocation demand information of the shipper, the pipe network information and the hydraulic parameters;

Shipper allocation demand information: designated gas supplier, designated upload site and upload pipeline, designated download site and download pipeline, user pressure demand, user designated demand, application service time, acceptance route adjustment and gas supply interruption situation;

Pipeline network information: including the pipeline topology of the natural gas pipeline network system, unit pipeline transportation cost, pipeline design transmission capacity, pipeline length, pipeline inner diameter, pipeline pressure bearing capacity, pipeline minimum gas transmission pressure and each station type;

Hydraulic parameters: including physical parameters of natural gas, compression capacity of compressor station, hydraulic friction coefficient and pipeline wall roughness.

It should be noted that the pipe capacity optimization allocation result includes: the pipe capacity allocation result and the profit result corresponding to the allocation result.

In a certain embodiment, the pipe capacity allocation model may be a MINLP model (mixed integer nonlinear programming model).

The proposed model contains many constraints, including nodes and pipelines, pipeline flow, pipeline pressure, pipeline flow direction, and pipeline capacity. Combined with specific actual data and nonlinear equation linearization methods, the model can be transformed into a MILP mathematical model, which consists of an objective function, constraints, and decision variables, where all constraints and objective functions are linear. Therefore, the model can be solved by GUROBI, a commercial MILP model solver based on the branch and bound algorithm, which is a search algorithm for the global optimal solution. Therefore, the optimal solution for pipe capacity allocation can be obtained. The specific MINLP mathematical model is as follows, and the symbols are explained as follows: the set index is shown in Table 1, the parameter index is shown in Figure 2, the variable index is shown in Table 3, and the integer variable is shown in Figure 4.

Table 1

Table 2

Table 3

Table 4

In one embodiment, the objective function may include:

(1) Net present value is an important technical and economic indicator for evaluating project profitability. Therefore, the objective function is to minimize the net present value of total costs within the decision-making cycle.

NPV＝∑ _t∈T C _t ·(1+r) ^-t ,

Among them, _Ct represents the total operating cost of natural gas in period t, r represents the discount rate, and T represents the set of time periods; when the objective function value is the maximum, the target result is output. Among them, the meaning of the decision cycle is the period of the contract signed between the user and the pipeline operator. In this model, the contract signed between the user and the pipeline operator is set to a period of one month, that is, the decision cycle of the model is one month.

The total cost of the t-cycle (C _t ) includes the pipeline transportation cost (C _t ^tran ) required to transport the natural gas:

C _t ^tran represents the total natural gas pipeline transmission cost in period t;

The total pipeline transportation cost of the natural gas pipeline network in period t is the sum of the transportation costs of all pipelines in the pipeline network system. The transportation cost of a single pipeline can be expressed as the product of the pipeline transportation cost per unit flow, the pipeline natural gas transportation volume, and the pipeline length:

Qi _,j,,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k in period t under the specified gas inlet and outlet point combination n;

ctm _t represents the pipeline transportation cost per unit flow in the pipeline in period t; l _i,j represents the length of the pipeline, and T represents the set of time periods.

(2) In order to improve the utilization rate of low-utilization pipelines in the process of pipeline capacity allocation, the pipeline utilization rate is added as the objective function in the optimization model. The pipeline utilization rate is defined as follows:

(3) In order to improve the user satisfaction with the execution of the pipeline transmission contract during the pipeline capacity allocation process, an objective function representing user satisfaction is added to the optimization model. User satisfaction is defined as follows:

In another embodiment, the constraints may include:

Taking into account the operating characteristics of the natural gas pipeline network system, the following constraints are considered in the model to constrain the scope of the solution.

1) Pipeline network flow constraints

The following four flows are defined for each node to characterize the flow relationship of the pipeline network, including: node upload flow (Qi _,tup ⁾ , node download flow (Qi _,tdo ⁾ , pipeline inflow flow (Qj _,i,t,n,k ) and pipeline outflow flow (Qi _,j,t,n,k ).

2) Capacity constraints

The amount uploaded to the gas inlet point (Q _i,t ⁱⁿ ) does not exceed the local capacity (qp _i,t ). For the gas supply point, the capacity qp _i,t is greater than 0, and the capacity of other nodes is 0. (Specify the gas supplier)

Where _Vs represents the set of all gas inlet points in the pipeline network topology, Qi _, ^tup represents the natural gas upload volume of node i in period t, and _qpi,t represents the production capacity of gas supply point i in period t.

The download volume (Qi,tout) of the gas point can be higher or lower than the user demand. If it is lower than the user demand, the user weight needs to be set according to the score, and the user demand with higher weight is met first (the priority is assigned according to the score).

3) Priority constraints

4) Demand constraints

The allocated capacity under the specified gas inlet and outlet point combination shall not exceed the demand provided by the user.

(1) For non-interruptible users:

Wherein, Qi _,j,,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k in period t under the specified gas inlet and outlet point combination n; qd _i,t represents the demand of demand point i in period t; E represents the set of all pipelines in the pipeline network topology structure in period t; T represents the set of each time period; Nint represents the set of interruptible users.

(2) For interruptible users:

Wherein, Qi _,j,,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k in period t under the specified gas inlet and outlet point combination n; qd _i,t represents the demand of demand point i in period t; E represents the set of all pipelines in the pipeline network topology structure in period t; T represents the set of each time period; Nnint represents the set of uninterruptible users.

5) Upload capacity constraints of intermediate nodes

The intermediate nodes are all the nodes except the gas supply points, including the compressor station nodes and ordinary nodes. The upload amount (Q _i,t ^up ) of the intermediate nodes is 0.

Where, Qi _,tup ^represents the natural gas upload volume of node i in period t; V represents the set of all nodes in the pipeline network topology diagram, including gas inlet points, gas extraction points and common nodes; _Vs represents the set of all gas inlet points in the pipeline network topology; _Vd represents the set of all gas extraction points in the pipeline network topology;

6) Node traffic balance

Node flow balance means that the sum of the node upload volume and the flow into the node at each node in the pipe network system is equal to the node download volume and the flow out of the node. Considering that there are some bidirectional pipes in the pipe network structure, the constraints are expressed as follows:

Wherein, Qi _,t ^up represents the natural gas upload volume of node i in period t; _Qi,j,,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k under the specified gas inlet and outlet point combination n in period t; qc _i,j,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k’s reserved contract capacity under the specified gas inlet and outlet point combination n in period t.

7) Pipeline flow constraints

If there is a pipeline between nodes i and j, the flow in the pipeline has certain upper and lower limits:

Wherein, q ^min represents the minimum gas transmission capacity of the pipeline; qc _i,j,t,n,k represents the natural gas flow in the pipeline (i,j) occupied by the scheduled contract capacity of user k in period t under the specified gas inlet and outlet point combination n; q ^max represents the maximum gas transmission capacity of the pipeline; Qi _,j,,t,n,k represents the natural gas flow in the pipeline (i,j) occupied by user k in period t under the specified gas inlet and outlet point combination n;

8) Node traffic balance

Node flow balance means that the sum of the node upload and the flow into the node at each node in the pipe network system is equal to the node download and the flow out of the node. Considering that there are some bidirectional pipes in the pipe network structure, the constraints are expressed as follows: (specify the upload and download station field)

Where, Qi _,t ^up represents the natural gas upload volume of node i in period t; Qi _,j,,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k in period t under the specified gas inlet and outlet point combination n; qc _i,j,t,n,k represents the natural gas flow in pipeline (i,j) occupied by user k’s scheduled contract capacity in period t under the specified gas inlet and outlet point combination n;

9) Pipeline flow constraints

If there is a pipeline between nodes i and j, the flow in the pipeline has certain upper and lower limits:

10) Pipeline capacity path constraints

The pipe capacity allocation of each pipeline corresponding to the same gas inlet and outlet point combination is equal:

Among them, Qi _',j',t,n,k has the same meaning as Qi _,j,,t,n,k, i.e., the natural gas flow in pipeline (i',j') occupied by user k in period t under the specified gas inlet and outlet point combination n; the purpose of introducing i' and j' is only to indicate different pipeline sections.

11) Pipeline flow constraints

Since some pipelines in the natural gas pipeline network system have forward and reverse transmission, it is necessary to add constraints to limit the flow direction of each pipeline.

For a one-way pipe, the flow direction of the pipe remains unchanged:

For bidirectional pipes, ensure that the flow direction remains unchanged within the same cycle:

There is flow only when there is flow direction:

12) Pipeline network pressure constraints

① Pressure constraints at both ends of the compressor station

The inlet pressure of the compressor station node should not be higher than the outlet pressure, and the outlet pressure should be within a certain upper limit:

Wherein, Pi _,t,n represents the starting pressure of pipeline (i,j) in period t, i.e., the outlet pressure of node i under gas inlet and outlet combination n; _Ein represents the set of arcs entering each node in period t, called the in-arc set; _Eout represents the set of arcs entering each node in period t, called the out-arc set;

The pressure at both ends of a normal node is equal:

Wherein, Pi _,t,n represents the starting pressure of pipeline (i,j) in period t, i.e., the outlet pressure of node i under gas inlet and outlet combination n.

②Hydraulic constraints of pipe network

The flow state of natural gas in the gas pipeline satisfies the hydraulic characteristic equation. For a one-way pipeline, the flow direction of natural gas in the pipeline is fixed, so the hydraulic constraint can be written as:

For a bidirectional pipeline, the natural gas flow direction in the pipeline is uncertain, and the hydraulic constraints are affected by the flow direction decision, so it can be written as:

Wherein, Z represents the compression factor of natural gas, T _pj represents the average temperature of the pipeline, l _i,j represents the length of the pipeline (i, j), c ₀ represents the hydraulic constant, d _i,j represents the inner diameter of the pipeline (i, j), and Δ _* represents the relative density of the gas.

③Constraints on pipeline pressure bearing capacity

During natural gas transportation, the gas pressure in the pipeline should be within the pressure range of the pipeline:

Where _Pi,j,t represents the pressure of the ij pipe segment at time t, E represents the set of all pipes in the pipe network topology structure of period t, and T represents the set of decision cycles.

④Demand point pressure constraints:

The pressure at each demand point in the pipe network system should be within a certain range:

Among them, Pd _i ^min represents the minimum demand pressure of demand point i; Pd _i ^max represents the maximum demand pressure of demand point i.

This solution constructs a pipe capacity allocation model according to the constructed objective function, multiple constraints and decision variables, linearizes the pipe capacity allocation model, and obtains the pipe capacity optimization allocation result of the pipeline to be allocated based on the processed pipe capacity allocation model and the basic parameters of the pipeline to be allocated, so as to maximize the pipeline transportation revenue of the pipeline operating company, improve the utilization rate of each pipeline as much as possible, and improve the gas satisfaction of users. The nonlinear constraints in the model are linearized by the piecewise linearization method to improve the solution efficiency of the model and obtain the global optimal solution.

Optionally, in some embodiments, it also includes: constructing the objective function according to the pipeline net present value.

Optionally, in some embodiments, the plurality of constraints include:

Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints.

Optionally, in some embodiments, the basic parameters include: shipper allocation demand information, pipeline network information and hydraulic parameters.

Optionally, in some embodiments, the shipper allocation demand information includes: gas supplier information, upload site and upload pipeline information, download site and download pipeline information, user pressure demand information, user specified demand quantity information, application service time information, acceptance path adjustment information and gas supply interruption information.

The pipeline network information includes: pipeline topology of the natural gas pipeline network system, unit pipeline transportation cost, pipeline design transmission capacity, pipeline length, pipeline inner diameter, pipeline pressure bearing capacity, pipeline minimum gas transmission pressure and types of each site.

The hydraulic parameters include: physical parameters of natural gas, compression capacity of the compressor station, hydraulic friction coefficient and pipeline wall roughness.

In a certain embodiment, the present invention is applicable to the pipe capacity allocation of a natural gas pipeline operating enterprise of a certain scale. The pipe capacity allocation scheme determined by the multi-period pipe capacity allocation optimization method considering the hydraulic characteristics of the pipeline network, user satisfaction, and the utilization rate of the pipeline network can ensure that the utilization rate of the pipeline capacity is at a reasonable level, further improve user satisfaction, and maximize the net present value of the pipeline operating enterprise.

First, describe the problem and input parameters to clarify the problem to be solved by the model and the basic parameters required. Then determine the objective function, which takes into account the hydraulic characteristics of the network, user satisfaction, and network utilization. Then determine the constraints, including pipeline flow constraints, pressure constraints, user priority constraints, and other related constraints. Finally, select the MINLP model and perform linearization. The final result can be obtained by solving the model. After linearizing the hydraulic constraints, it becomes a MILP model, which is solved by calling the GUROBI solver. Among them, the MINLP model is established by determining the model objective function, decision variables, and constraints.

In one embodiment, as shown in FIG2 , a pipe capacity optimization allocation system includes: a constraint acquisition module 1101 , a pipe capacity allocation construction module 1102 , and a pipe capacity allocation module 1103 ;

The constraint acquisition module 1101 is used to acquire multiple constraint conditions of the pipeline to be allocated;

The pipe capacity allocation construction module 1102 is used to construct a pipe capacity allocation model according to the constructed objective function, multiple constraints and decision variables;

The pipe capacity allocation module 1103 is used to perform linearization processing on the pipe capacity allocation model, and obtain the pipe capacity optimization allocation result of the pipe to be allocated according to the processed pipe capacity allocation model and the basic parameters of the pipe to be allocated.

This solution constructs a pipe capacity allocation model according to the constructed objective function, multiple constraints and decision variables, linearizes the pipe capacity allocation model, and obtains the pipe capacity optimization allocation result of the pipeline to be allocated based on the processed pipe capacity allocation model and the basic parameters of the pipeline to be allocated, so as to maximize the pipeline transportation revenue of the pipeline operating company, improve the utilization rate of each pipeline as much as possible, and improve the gas satisfaction of users. The nonlinear constraints in the model are linearized by the piecewise linearization method to improve the solution efficiency of the model and obtain the global optimal solution.

Optionally, in some embodiments, it further includes: an objective function construction module, which constructs the objective function according to the pipeline net present value.

Optionally, in some embodiments, the plurality of constraints include:

Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints.

It can be understood that in some embodiments, some or all of the optional implementation modes in the above embodiments may be included.

It should be noted that the above embodiments are product embodiments corresponding to the previous method embodiments. For the description of the optional implementation methods in the product embodiments, reference can be made to the corresponding description in the above method embodiments, which will not be repeated here.

In a certain example, the optimal allocation of pipe capacity for residual capacity calculation may include: the main content of the multi-period pipe capacity allocation optimization method considering the hydraulic characteristics of the pipe network, user satisfaction, and pipe network utilization is to establish a pipe capacity optimization model. By determining the objective function, decision variables, constraints and other related parameters of the model, where Qi _,j,t,n,k, the decision variables also include the gas transmission path and pipe capacity allocation, and solving through examples, the final pipe capacity allocation scheme is obtained, and the effectiveness of the scheme is verified. Since the current pipe capacity allocation does not take into account the user's gas experience, pipeline utilization, pipeline hydraulic characteristics and other related factors, the present invention takes the above factors into account in the pipe capacity allocation optimization method, and at the same time considers the differences in gas consumption of users in different periods, and proposes a multi-period pipe capacity allocation optimization method considering the hydraulic characteristics of the pipe network, user satisfaction, and pipe network utilization, which is used to optimize the allocation of the remaining gas transmission capacity of China's pipe network. The objective function of the pipeline operator's maximum net present value, user satisfaction, and pipeline utilization is considered at the same time, so that the objective function of the optimization model is a multi-objective function. Considering the remaining transmission capacity of the pipeline in multiple periods, the hydraulic characteristics constraints of the pipeline operation, the capacity constraints of the gas source point, etc., the nonlinear hydraulic equations are linearized to make the constructed model a MILP model (mixed integer nonlinear programming model) and solve it. In this way, the pipeline company's pipeline net present value, pipeline transmission volume and pipeline transmission path in each period are obtained.

In one embodiment, as shown in FIG3 , the optimization model calculation example includes:

A virtual example is used to illustrate that the established model has the function of optimizing pipe capacity allocation taking into account user allocation priorities.

Example 1 is shown in Figure 3. The example contains 7 nodes, including 1 gas supply point, represented by a green node (node 5); 3 demand points, represented by red nodes (nodes 1, 3, and 4); and 3 common nodes, represented by blue nodes (nodes 2, 6, and 7). All pipelines are unidirectional pipelines. For the three demand points, user 3 is a fully guaranteed user, and user 1 and user 4 are users that can be reduced.

Table 1 shows the basic parameters of the pipeline.

Table 1

In this example, the gas supply volume of gas supply point 5 is 1.5 billion cubic meters per month, and the demand volume of each demand point is shown in Table 2:

Table 2 The weights of the users with priority for the three users are shown in Table 3:

Table 3

The distribution of pipeline transmission capacity for three users and the distribution of pipeline transmission capacity for each user are shown in Figure 4:

Due to the limitation of pipeline capacity (1 billion cubic meters/month), it cannot meet the needs of all users. Finally, the pipeline service demand of user 3 is fully met, the pipeline demand of user 1 is partially met, and user 4 is not allocated any pipeline capacity. Although users 1 and 3 have the same weight, user 3 is a fully guaranteed user and user 1 is a reducible user. The priority of fully guaranteed users is higher than that of reducible users. The weight of the same type of reducible user 1 is higher than that of 4, so the remaining pipeline capacity is allocated to user 1.

The reader should understand that in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradiction.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the method embodiments described above are only illustrative, for example, the division of steps is only a logical function division, and there may be other division methods in actual implementation, such as multiple steps can be combined or integrated into another step, or some features can be ignored or not executed.

If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims (10)

1. A method for optimizing pipe capacity allocation, comprising: Get multiple constraints of the pipeline to be allocated; Construct a pipe capacity allocation model based on the constructed objective function, multiple constraints and decision variables; The pipe capacity allocation model is linearized, and according to the processed pipe capacity allocation model and in combination with basic parameters of the pipe to be allocated, an optimized pipe capacity allocation result of the pipe to be allocated is obtained. 2. A pipeline capacity optimization allocation method according to claim 1, characterized in that it also includes: constructing the objective function based on the pipeline net present value. 3. A pipe capacity optimization allocation method according to claim 1, characterized in that the plurality of constraints include: Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints. 4. A method for optimizing pipe capacity allocation according to any one of claims 1 to 3, characterized in that the basic parameters include: shipper allocation demand information, pipe network information and hydraulic parameters. 5. A method for optimizing pipe capacity allocation according to claim 4, characterized in that the consignor allocation demand information includes: gas supplier information, upload site and upload pipeline information, download site and download pipeline information, user pressure demand information, user specified demand information, application service time information, acceptance path adjustment information and gas supply interruption information; The pipeline network information includes: pipeline topology of the natural gas pipeline network system, unit pipeline transportation cost, pipeline design transmission capacity, pipeline length, pipeline inner diameter, pipeline pressure bearing capacity, pipeline minimum gas transmission pressure and each station type; The hydraulic parameters include: physical parameters of natural gas, compression capacity of the compressor station, hydraulic friction coefficient and pipeline wall roughness. 6. A pipe capacity optimization allocation system, characterized by comprising: a constraint acquisition module, a pipe capacity allocation construction module and a pipe capacity allocation module; The constraint acquisition module is used to acquire multiple constraint conditions of the pipeline to be allocated; The pipe capacity allocation construction module is used to construct a pipe capacity allocation model according to the constructed objective function, multiple constraints and decision variables; The pipe capacity allocation module is used to perform linearization processing on the pipe capacity allocation model, and obtain the pipe capacity optimization allocation result of the pipe to be allocated according to the processed pipe capacity allocation model and the basic parameters of the pipe to be allocated. 7. A pipeline capacity optimization allocation system according to claim 6, characterized in that it also includes: an objective function construction module, which constructs the objective function according to the pipeline net present value. 8. A pipe capacity optimization allocation system according to claim 6, characterized in that the plurality of constraints include: Pipeline network flow constraints, capacity constraints, priority constraints, demand constraints, intermediate node upload constraints, node flow balance constraints, pipeline flow constraints, node flow balance constraints, pipeline flow constraints, pipe capacity path constraints, pipeline flow direction constraints and pipeline network pressure constraints. 9. A storage medium, characterized in that instructions are stored in the storage medium, and when a computer reads the instructions, the computer executes a pipe capacity optimization allocation method as described in any one of claims 1 to 5. 10. An electronic device, comprising a processor and the storage medium according to claim 9, wherein the processor executes instructions in the storage medium.

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