A Route Optimization Method for Natural Gas Shippers Based on Operations Research Optimization Technology

Abstract

The present invention relates to a method for optimizing the path of a natural gas shipper based on operations research optimization technology, including: initializing node information and customer information, and configuring the natural gas supply volume and natural gas demand volume for the current month; configuring various constraint parameters and performing rationality verification; selecting constraint conditions, decision variables, and objective functions to construct a mixed integer programming model; solving the mixed integer programming model, if there is a solution, obtaining the output result, if there is no solution, reconfiguring the constraint parameters. The beneficial effects of the present invention are: on the premise of meeting customer needs, the present invention reasonably allocates and dispenses gas source supply, realizes the automatic matching of the monthly transportation path of the natural gas pipeline network and the intelligent calculation of the monthly gas transmission volume of each path, improves the work efficiency of business personnel, and reduces the enterprise operation cost.

Description

Technical Field

[0001] The present invention relates to the field of natural gas transportation, and more specifically, it relates to a method for optimizing the path of natural gas shippers based on operations research optimization technology. Background Art

[0002] At present, the pipeline transportation path selection and transportation plan of natural gas shippers are basically manually formulated by business personnel based on actual experience. There are the following difficulties in manually matching the pipeline transportation path: First, it is difficult for manual analysis to consider the mutual influence between pipelines and difficult to conduct global analysis and matching. When balancing the resource market and allocating the throughput, it is necessary to overall consider the newly planned pipelines and the existing pipeline network, and achieve the overall network balance through systematic analysis methods. The current pipeline network planning work mainly focuses on the pipeline topological layout, lacking the analysis of the influence of the pipeline layout on the system flow direction. Second, the pipeline network is huge and the workload of global analysis is large. At present, the total mileage of natural gas pipelines in the country has reached about 110,000 kilometers, and the scale of the pipeline network continues to increase. The workload of traditional manual statistics is getting larger and larger, making it difficult to meet the requirements of global analysis. Third, there are numerous uploading points and downloading points, and the pipeline network topological structure is complex. Due to the complex topological structure of the national natural gas pipeline network with multiple inlets, multiple outlets, and multiple loops, it is necessary to overall consider the influence of various facilities such as pipelines, LNG receiving stations, and gas storage facilities. However, the manual analysis method cannot consider too many influencing factors and limiting conditions, making it difficult to analyze the complex pipeline network in detail. Fourth, the traditional manual formulation mode relies on paper documents and manual operations, with low efficiency and prone to errors. Summary of the Invention

[0003] The purpose of the present invention is to propose a method for optimizing the path of natural gas shippers based on operations research optimization technology in view of the deficiencies of the prior art.

[0004] In a first aspect, a method for optimizing the path of natural gas shippers based on operations research optimization technology is provided, including:

[0005] Step 1, initialize node information and customer information, where the nodes include supply points and demand points; and represent the network topological relationship from the supply point to the demand point in the form of a distance matrix; the supply point is a binary group composed of a gas source and an uploading point, and the demand point is a binary group composed of a downloading point and a customer;

[0006] Step 2, configure the monthly natural gas supply volume according to the natural gas supply plan reported by each gas source, and configure the monthly natural gas demand volume according to the natural gas demand reported by each customer;

[0007] Step 3, configure various limiting parameters according to the actual situation of the current month;

[0008] Step 4, conduct a rationality test on the limiting parameters;

[0009] Step 5: When the inspection passes, select constraint conditions, decision variables, and objective functions to construct a mixed-integer programming model;

[0010] Step 6: Solve the mixed-integer programming model. If there is a solution, obtain the output result; if there is no solution, reconfigure the limit parameters. The output result is the optimal path matching relationship and the optimal gas transmission volume between the supply point and the demand point.

[0011] Preferably, it further includes:

[0012] Step 7: Determine whether the output result meets the business requirements. If it meets, use the output result as the optimal path matching and gas transmission plan for reference by business personnel; otherwise, reconfigure the limit parameters.

[0013] Preferably, in Step 3, configure pipeline gas transmission volume limits, daily average target flow rates, optimal pipeline gas transmission volumes, the number of configurable paths at demand points limit parameters, and set fixed path matching relationships and fixed path non-matching relationships.

[0014] Preferably, in Step 4, the rationality inspection includes: the total gas supply in the current month should be greater than or equal to the total customer demand; the upper limit of the path gas transmission volume must be greater than the lower limit; the manually set flow rate shall not exceed the path gas transmission volume limit and shall not be greater than the supply volume of the corresponding supply point for that path; the paths in the fixed matching relationship and the fixed non-matching relationship cannot overlap.

[0015] Preferably, in Step 5, the constraint conditions include: path gas transmission volume limit constraint, upper and lower limits constraint on the number of paths matching demand points, gas source coverage constraint, supply point supply balance constraint, customer demand satisfaction constraint, fixed path matching relationship constraint, fixed path non-matching relationship constraint, path consistency constraint with the previous month, supply point exclusion relationship constraint, minimum flow rate / ratio constraint, and daily average flow rate target value constraint.

[0016] Preferably, in Step 5, the decision variables include continuous variables and boolean variables;

[0017] The continuous variables include: the gas transmission volume transported from the supply point to the demand point , the value of the part where the daily average flow rate of the supply point and the demand point does not reach the daily flow rate target , and the value of the part where the path flow rate of the supply point and the demand point does not meet the minimum flow rate requirement of the demand point ; the boolean variables include and , For indicating supply points and demand points whether they are connected, For indicating supply points and demand points whether the connection relationship is inconsistent with that of the previous month.

[0018] Preferably, the optimization objectives corresponding to the objective function include: minimizing the weighted gas transmission distance from the gas transmission point to the user, minimizing the value of the part where the path flow after the path is enabled does not reach the daily flow target, minimizing the value of the part where the path flow after the path is enabled does not meet the minimum flow requirement set by the demand point, minimizing the difference degree between the path matching relationship this month and the path matching relationship of the previous month, and minimizing the proportion of the gas volume of the adjustable gas source point in the gas demand of the demand point.

[0019] In a second aspect, a natural gas shipper path optimization system based on operations research optimization technology is provided for implementing any of the methods in the first aspect, including:

[0020] An initialization module for initializing node information and customer information, where the nodes include supply points and demand points; and representing the network topology relationship from the supply point to the demand point in the form of a distance matrix; the supply point is a binary group composed of a gas source and an upload point, and the demand point is a binary group composed of a download point and a customer;

[0021] A first configuration module for configuring the monthly natural gas supply volume according to the natural gas supply plan reported by each gas source, and configuring the monthly natural gas demand volume according to the natural gas demand reported by each customer;

[0022] A first configuration module for configuring various constraint parameters according to the actual situation of the current month;

[0023] An inspection module for performing a rationality inspection on the constraint parameters;

[0024] A construction module for selecting constraint conditions, decision variables and an objective function to construct a mixed integer programming model when the inspection passes;

[0025] A solution module for solving the mixed integer programming model, obtaining the output result if there is a solution, and reconfiguring the constraint parameters if there is no solution; the output result is the optimal path matching relationship and the optimal gas transmission volume between the supply point and the demand point.

[0026] In a third aspect, a computer storage medium is provided, in which a computer program is stored; when the computer program runs on a computer, the computer is enabled to execute any of the methods in the first aspect.

[0027] In a fourth aspect, an electronic device is provided, including:

[0028] A memory for storing a computer program;

[0029] A processor for executing the computer program to implement the method according to any one of the first aspects.

[0030] The beneficial effects of the present invention are:

[0031] 1. The present invention uses the mixed integer programming modeling technology to establish an operational optimization model and uses a mathematical solver for solution. It can comprehensively and fully consider the specific situations of each path, gas source, uploading point, downloading point, and customers. On the premise of meeting customer needs, it can reasonably allocate and deploy gas source supply, realize the automatic matching of monthly transportation paths of the natural gas pipeline network and the intelligent calculation of monthly gas transmission volumes of each path, improve the work efficiency of business personnel, and reduce the enterprise operation cost.

[0032] 2. The operational optimization model proposed by the present invention can maximize the flexibility and stability of the system by reasonably configuring adjustable gas sources on the premise of ensuring compliance with constraints such as upper and lower limits of path transmission volume, full satisfaction of customer demand, and full consumption of gas source supply. It can reduce the actual transportation cost by minimizing the weighted gas transmission distance of the transportation path, and reduce the actual scheduling operation cost by making the path matching relationship as consistent as possible with the previous month. Finally, it formulates the optimal path matching plan and automatically calculates the gas transmission volume of each path. Description of the Drawings

[0033] Figure 1 It is a flowchart of a method for optimizing the path of a natural gas shipper based on operational research optimization technology provided by an embodiment of the present invention;

[0034] Figure 2 It is a framework diagram of an operational research optimization model provided by an embodiment of the present invention;

[0035] Figure 3 It is a flowchart of another method for optimizing the path of a natural gas shipper based on operational research optimization technology provided by an embodiment of the present invention;

[0036] Figure 4 It is a schematic structural diagram of a system for optimizing the path of a natural gas shipper based on operational research optimization technology provided by an embodiment of the present invention. Detailed Embodiments

[0037] The following further describes the present invention with reference to embodiments. The description of the following embodiments is only for helping to understand the present invention. It should be noted that for those of ordinary skill in the technical field, without departing from the principle of the present invention, several modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0038] Embodiment 1:

[0039] To solve the problems of the prior art, Embodiment 1 of the present application provides a path optimization method for natural gas shippers based on operations research optimization technology. The purpose of the present application is to determine the optimal path matching relationship and monthly planned gas transmission volume between supply points and demand points based on data such as monthly user demand, monthly gas supply from gas sources, pipeline network topology, and distance information, with the optimization goals of meeting supply-demand balance, minimizing weighted gas transmission distance, maximizing system flexibility and stability, etc.

[0040] Specifically, as Figure 1 shown, a path optimization method for natural gas shippers based on operations research optimization technology includes:

[0041] Step 1: Initialize node information and customer information. The nodes include supply points and demand points; and represent the network topology relationship from supply points to demand points in the form of a distance matrix. The supply point is a binary group composed of a gas source and an upload point, and the demand point is a binary group composed of a download point and a customer.

[0042] During pipeline network transportation, at the upload point, the natural gas supplied by the gas source point is transported into the pipeline system. After pipeline network transportation, at the download point, the natural gas is received from the pipeline system and transported to the customer. The binary group (gas source, upload point) composed of the gas source and the upload point is defined as the supply point, and the binary group (customer, download point) composed of the download point and the customer is defined as the demand point. Among them, the relationships between the gas source and the upload point, and the download point and the customer are all many-to-many relationships. Then the path refers to the connection relationship from the supply point to the demand point.

[0043] During initialization, it is necessary to obtain the basic information of the gas source, basic customer information, basic information of the download point, basic information of the upload point, and maintain the distance information between the upload and download points. The basic information includes relevant information such as name, abbreviation, (customer) contact person and contact phone number, and (gas source) whether it is an adjustable gas source. The network topology relationship from the supply point to the demand point is represented in the form of a distance matrix. The columns of the matrix represent the supply points, the rows represent the demand points, and the values of the matrix are the distances from the corresponding supply points to the demand points.

[0044] Step 2: Configure the monthly natural gas supply volume according to the natural gas supply plan reported by each gas source, and configure the monthly natural gas demand volume according to the natural gas demand reported by each customer.

[0045] Step 3: Configure various constraint parameters according to the actual situation of the current month.

[0046] According to the actual situation of the current month, configure parameters such as the pipeline gas transmission volume limit, the daily average target flow, the optimal pipeline gas transmission volume, and the limit on the number of configurable paths for demand points, and set the fixed path matching relationship and the fixed path non-matching relationship. Based on the actual business rules, this embodiment stipulates the demand point categories as follows: when the daily average demand of a demand point is less than or equal to 20, the lower limit of the number of configurable paths is set to 1; when the demand is greater than 20 and less than or equal to 50, the lower limit of the number of configurable paths is set to 2; when the demand is greater than 50, the lower limit of the number of configurable paths is set to 3. The lower limit of the daily average flow of the enabled path is set to 0.01 / day, and the expected daily flow target is set to 1 / day.

[0047] Step 4: Conduct a rationality check on the above-mentioned limit parameters.

[0048] Check whether various parameters are configured reasonably. For example, the total gas supply of the gas source points in the current month should be greater than or equal to the total customer demand, otherwise the supply-demand balance cannot be achieved; the upper limit of the gas transmission volume of the path must be greater than the lower limit; the manually set flow cannot exceed the pipeline gas transmission volume limit and cannot be greater than the supply volume of the corresponding supply point of the path; there should be no overlap in the paths in the fixed matching relationship and the fixed non-matching relationship. If the rationality check of the parameters cannot be satisfied, return to Step 2.

[0049] Step 5: When the check passes, as Figure 2 shown, select the constraint conditions, decision variables, and objective function to construct a mixed integer programming model.

[0050] This invention mainly undertakes structured data from different data sources for optimal path matching and gas transmission volume setting. The data used in the model mainly includes:

[0051] Natural gas demand: The natural gas demand of each natural gas customer in the current month. When actually using the model for path optimization, the actually reported demand of the natural gas customer is used here; when making a plan in advance, the prediction result of the natural gas sales prediction model can be used here, or it can be adjusted by the business personnel according to the actual situation;

[0052] Natural gas supply: The natural gas supply of each gas source point in the current month. When actually using the model for path optimization, the actually reported supply of the gas source point household is used here; when making a plan in advance, it can be adjusted by the business personnel according to the actual situation here;

[0053] Basic information of the natural gas procurement chain: including various basic information such as the names and types of gas sources, customers, upload points, and download points;

[0054] Topological structure of the natural gas procurement chain: including the topological network relationship of the positions among gas sources, customers, upload points, and download points, as well as the actual distances between supply points and demand points;

[0055] Natural gas pipeline network transportation capacity: including pipeline gas transmission volume restrictions, pipeline optimal gas transmission volume, configurable number of paths for demand points, and manually set path information that must be matched or disconnected.

[0056] The above information is abstracted into specific parameters to input into the model. Table 1 defines all the parameters used in the optimization model:

[0057] Table 1 Model parameters

[0058]

[0059] In addition, the output results of the present invention are the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume. These output results are defined as decision variables for model optimization. Table 2 defines all decision variables used in the optimization model:

[0060] Table 2 Decision variables

[0061]

[0062] The decision variables are the optimization objects of the model. This application designs appropriate decision variables to ensure that the model correctly outputs the path matching plan and the path gas transmission plan.

[0063] In descending order of priority, the optimization objectives of the operations research model used in the present invention include:

[0064] (1) Minimize the weighted distance from the gas transmission point to the user (with the gas transmission volume as the weight).

[0065] (2) Once a path is activated, the path flow should reach the daily flow target as much as possible, that is, the value of the part of the path flow that does not reach the daily flow target after the path is activated is minimized.

[0066] (3) Once a path is enabled, the path flow should try to reach the minimum flow requirement set at the demand point, that is, minimize the value of the path flow that does not reach the minimum flow requirement set at the demand point after the path is enabled.

[0067] (4) The path matching relationship of this month is kept consistent with that of last month as much as possible, that is, the difference between the path matching relationship of this month and that of last month is minimized.

[0068] (5) Minimize the ratio of the gas supply volume at the adjustable gas source point to the gas demand at the demand point.

[0069] According to the above optimization objectives, the objective function of the optimization model is defined as:

[0070]

[0071] Each term in the formula measures one of the above optimization objectives. The overall optimization objective of the model is: find the appropriate values of the decision variables so that, under the premise of meeting the constraint conditions, the value of the objective function reaches the minimum.

[0072] The design formula of the objective function measures indicators such as weighted gas transmission distance, system flexibility, and system stability. The model will output a solution that minimizes the value of the objective function under the premise of meeting the constraint conditions; in addition, the penalty coefficients of each term in the objective function are preferentially set after multiple repeated experimental tests. Appropriate penalty coefficients can make the model achieve better results.

[0073] Define all the constraint conditions involved in the natural gas shipper path optimization problem as follows:

[0074] (1) Path gas transmission volume limit constraint. If a supply point is matched with a demand point, the gas transmission volume on this matching path must be greater than or equal to the minimum flow requirement of the path and must be less than or equal to the demand of the users at this demand point; if a supply point is not matched with a demand point, the gas transmission volume on the corresponding path is 0.

[0075]

[0076] (2) Upper and lower limit constraints on the number of matching paths for demand points. Demand points are divided into different categories according to the size of the demand, and different upper and lower limits on the number of paths are configured for different categories of demand points. The upper and lower limits on the number of paths that demand point d can be configured with are represented by and respectively.

[0077]

[0078] (3) Gas source coverage constraint, that is, cover the adjustable gas sources to as many demand points as possible. The specific rule is: for demand points with a daily average demand greater than the minimum daily average demand limit of system flexibility , there must be at least one supply point containing an adjustable gas source connected to the demand point.

[0079]

[0080] (4) Supply point supply balance constraint. The total amount of gas transported from a supply point to each demand point should be equal to the supply amount reported by this supply point.

[0081]

[0082] (5) Customer demand satisfaction constraint. The total amount of gas transported from each supply point to a demand point should be equal to the demand reported by this demand point to fully satisfy the customer demand.

[0083]

[0084] (6) Fixed path matching relationship constraint. This constraint is manually configured by business personnel to meet some special business requirements. Supply points and demand points The fixed matching relationship between them stipulates that part of the natural gas demand of the demand points this month must be provided by the supply points and stipulates that the supply volume is .

[0085]

[0086] (7) Fixed path non - matching relationship constraint. This constraint is manually configured by business personnel to handle special situations such as path maintenance. The fixed non - matching relationship between supply points and demand points stipulates that the natural gas demand of the demand points this month cannot be provided by the supply points and the path between them must be in a disconnected state.

[0087]

[0088] (8) Path consistency constraint with the previous month. This constraint is used to define the decision variable , which is used in the objective function to measure the similarity between the path matching relationship this month and that of the previous month. Keeping the path matching relationship as consistent as the previous month and reducing adjustments helps to lower the enterprise's operating costs.

[0089]

[0090] (9) Supply point exclusion relationship constraint. For the same demand point, at most one supply point with an exclusion relationship can be matched.

[0091]

[0092] (10) Minimum flow / ratio constraint. This constraint is used to define the flow value that does not meet the minimum flow requirement. This decision variable is used in the objective function to measure the degree to which the path meets the minimum flow requirement.

[0093]

[0094] (11) Daily average flow target value constraint. Once a path is enabled, the daily flow should be as close as possible to the daily flow target value.

[0095]

[0096] All the constraint conditions ensure that the output solution obtained by solving the model does not exceed various limitations, thus guaranteeing the stable operation of the natural gas supply chain.

[0097] Step 6: Solve the mixed-integer programming model. If there is a solution, obtain the output result; if there is no solution, reconfigure the constraint parameters. The output result is the optimal path matching relationship and the optimal gas transmission volume between the supply points and the demand points.

[0098] After determining the model parameters, decision variables, objective function, and constraint conditions, a mixed-integer programming model is established, and the mathematical programming solver COPT is called to optimize and solve this model to obtain the output result. If the model has no solution (overly stringent parameter settings may lead to no solution), return to Step 3 to reconfigure the parameters. Finally, the path matching plan for the current month and the gas transmission volume plan for each path are output. The result output by the model is the value of the aforementioned decision variables. The specific gas transmission plan is also represented by the decision variables. For example, if the value of the path from supply point s to demand point d in the result output by the model is 1, it means that in the plan for the current month, the path from s to d needs to be matched, and the corresponding gas transmission volume for the current month is ., .

[0099] Embodiment 2:

[0100] Based on Embodiment 1, Embodiment 2 of the present application provides a more specific method for optimizing the path of a natural gas shipper based on operations research optimization technology. According to the supply volume and demand volume information actually obtained by a certain natural gas shipper in July 2024, based on the optimization model of the present invention, the path matching and gas transmission plan results for the current month are generated, and a comparative analysis is carried out with the actual plan scheme formulated by the business personnel.

[0101] Specifically, as Figure 3 shown, this method includes:

[0102] Step 1: Initialize the node information and customer information. The nodes include supply points and demand points; and the network topology relationship from the supply points to the demand points is represented in the form of a distance matrix. The supply point is a binary group composed of a gas source and a uploading point, and the demand point is a binary group composed of a downloading point and a customer.

[0103] Step 2: Configure the natural gas supply volume for the current month according to the natural gas supply plans reported by each gas source, and configure the natural gas demand volume for the current month according to the natural gas demands reported by each customer.

[0104] Step 3: Configure various constraint parameters according to the actual situation of the current month.

[0105] Step 4: Conduct a rationality test on the constraint parameters.

[0106] Step 5: When the inspection passes, select constraint conditions, decision variables, and objective functions to construct a mixed-integer programming model.

[0107] Step 6: Solve the mixed-integer programming model. If there is a solution, obtain the output result; if there is no solution, reconfigure the limit parameters. The output result is the optimal path matching relationship and the optimal gas transmission volume between the supply point and the demand point.

[0108] Step 7: Determine whether the output result meets the business requirements. If it meets, use the output result as the optimal path matching and gas transmission plan for reference by business personnel; otherwise, reconfigure the limit parameters.

[0109] In Step 7, the business requirements refer to special requirements that need to be confirmed by business personnel and are not universal. For example, the number of paths that need to be matched at certain demand points is a special requirement not specified in the model, such as this demand point must match XX paths, etc.

[0110] In addition, compare the path matching and gas transmission plan generated by the model with the plan manually formulated by business personnel based on experience. The comparison results are shown in Table 3. Here, the total weighted gas transmission distance value and the flow value that do not reach the daily flow target are used to evaluate the model effect. Obviously, compared with the manually formulated plan, the plan generated based on the model can reduce the total target value by 23.04% and the weighted gas transmission distance by 23.35%, with significant effects.

[0111] Table 3 Comparison Results

[0112]

[0113] It should be noted that the same or similar parts in this embodiment and Embodiment 1 can be referred to each other and will not be elaborated in this application.

[0114] Embodiment 3:

[0115] Based on Embodiment 1, Embodiment 3 of this application provides a path optimization system for natural gas shippers based on operations research optimization technology, including:

[0116] An initialization module for initializing node information and customer information. The nodes include supply points and demand points, and represent the network topology relationship from the supply point to the demand point in the form of a distance matrix. The supply point is a binary group composed of a gas source and an upload point, and the demand point is a binary group composed of a download point and a customer.

[0117] A first configuration module for configuring the monthly natural gas supply volume according to the natural gas supply plans reported by each gas source, and configuring the monthly natural gas demand volume according to the natural gas demands reported by each customer.

[0118] The first configuration module is used to configure various limit parameters according to the actual situation of the current month;

[0119] The inspection module is used to perform a rationality inspection on the limit parameters;

[0120] The construction module is used to select constraint conditions, decision variables, and objective functions to construct a mixed-integer programming model when the inspection is passed;

[0121] The solution module is used to solve the mixed-integer programming model. If there is a solution, the output result is obtained; if there is no solution, the limit parameters are reconfigured. The output result is the optimal path matching relationship and the optimal gas transmission volume between the supply point and the demand point.

[0122] Specifically, the system provided in this embodiment is the system corresponding to the method provided in Embodiment 1. Therefore, for the parts that are the same or similar in this embodiment and Embodiment 1, reference can be made to each other and will not be elaborated in this application.

Claims

1. A method for optimizing the path of natural gas shippers based on operations research optimization technology, characterized in that: include: Step 1: Initialize node information and customer information, where the node includes a supply point and a demand point; The network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; Step 2: Allocate the natural gas supply for the month according to the natural gas supply plan submitted by each gas source, and allocate the natural gas demand for the month according to the natural gas demand submitted by each customer; Step 3: Configure various restriction parameters according to the actual situation of the month; in step 3, configure pipeline gas volume restriction, daily average target flow, optimal pipeline gas volume, and the number of configurable paths for demand points, and set fixed path matching relationships and fixed path mismatching relationships; Step 4: Perform a rationality check on the restriction parameters; Step 5. When the test is passed, select constraints, decision variables and objective functions to construct a mixed integer programming model; the constraints include: path gas transmission volume restriction constraints, upper and lower limit constraints on the number of demand point matching paths, gas source coverage constraints, supply point supply balance constraints, customer demand satisfaction constraints, fixed path matching relationship constraints, fixed path mismatch relationship constraints, path and last month consistency constraints, supply point exclusion relationship constraints, minimum flow / ratio constraints and daily average flow target value constraints; the rule of gas source coverage constraints is: for daily average demand greater than the minimum daily average demand limit of system flexibility For each demand point, there must be at least one supply point with an adjustable gas source connected to the demand point; Step 6: Solve the mixed integer programming model, obtain the output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

2. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 1 is characterized in that: Also includes: Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal path matching and gas transmission plan for reference by business personnel. Otherwise, reconfigure the restriction parameters.

3. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 2 is characterized in that: In step 4, the rationality check includes: the total gas supply of the month should be greater than or equal to the total customer demand; the upper limit of the path gas supply must be greater than the lower limit of the path gas supply; the manually set flow rate must not exceed the path gas supply limit and must not be greater than the supply of the corresponding supply point of the path; the paths in the fixed matching relationship and the fixed non-matching relationship cannot overlap.

4. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 3 is characterized in that: In step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: supply point Transport to the point of demand Gas delivery , Supply Point and demand points The average daily traffic volume does not reach the daily traffic target value and supply points and demand points The path flow does not meet the value of the minimum flow requirement of the demand point ; The Boolean variables include and , Used to indicate supply points and demand points Is it connected? Used to indicate supply points and demand points Whether the connection relationship is inconsistent with last month.

5. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 3 is characterized in that: In step 5, the optimization objectives corresponding to the objective function include: minimizing the weighted distance of gas transmission from the gas transmission point to the user, minimizing the value of the path flow after the path is enabled that does not reach the daily flow target, minimizing the value of the path flow after the path is enabled that does not reach the minimum flow requirement set at the demand point, minimizing the difference between the path matching relationship of this month and the path matching relationship of last month, and minimizing the proportion of the gas transmission volume of the adjustable gas source point to the demand of the demand point.

6. A natural gas consignor route optimization system based on operations research optimization technology, characterized in that: The method for executing any one of claims 1 to 5 comprises: An initialization module is used to initialize node information and customer information, wherein the nodes include supply points and demand points; and the network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; The first configuration module is used to configure the natural gas supply volume of the month according to the natural gas supply plan submitted by each gas source, and configure the natural gas demand volume of the month according to the natural gas demand submitted by each customer; The first configuration module is used to configure various restriction parameters according to the actual situation of the month; A testing module, used to test the rationality of the restriction parameters; A construction module is used to select constraints, decision variables and objective functions to construct a mixed integer programming model when the test passes; A solution module is used to solve the mixed integer programming model, obtain an output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

7. A computer storage medium, characterized in that: The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 5.

8. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 5.

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