Natural gas transportation plan intelligent compilation method based on operation optimization technology

Abstract

The invention relates to a natural gas transportation plan intelligent compilation method based on an operation planning optimization technology. The method comprises the following steps: initializing node information; configuring actual natural gas supply quantity and actual demand quantity on the current day, and estimating daily estimated supply quantity and demand quantity from the current day to the end of the month; configuring various limiting parameters and carrying out rationality test; when the test is passed, a constraint condition, a decision variable and a target function are selected to construct a mixed integer programming model; and the mixed integer programming model is solved, if a solution exists, an output result is obtained, and if no solution exists, the limiting parameters are reconfigured. The method has the beneficial effects that the optimal daily gas transmission scheme from the current day to the end of the current month is generated in real time under the condition that constraint conditions such as supply and demand balance constraint and path transmission capacity constraint are met, the planning efficiency can be effectively improved, and the calculation burden of business personnel and the operation cost of enterprises are reduced.

Description

Technical Field

[0001] The invention relates to the field of natural gas transportation, and more specifically, to a method for intelligently compiling a natural gas transportation plan based on operations optimization technology. Background Art

[0002] At present, the daily gas transmission plan of natural gas shippers is basically manually compiled based on the experience of business personnel, which has great limitations, mainly reflected in the following aspects: First, the method of determining the gas transmission volume through manual experience or manual calculation is inefficient, and it is difficult to respond to emergencies such as route maintenance or changes in market supply and demand in a timely, accurate and efficient manner. Second, the daily gas transmission plan determined based on manual experience may not be able to accurately fit the target gas transmission volume of the day decomposed from the monthly plan, which may cause the extension of the gas transmission cycle and the increase of pipeline transportation costs. Third, the gas transmission plan determined based on manual experience may not be able to fully meet the supply and demand balance in the presence of objective physical constraints such as upper and lower limits of pipeline transmission, which may lead to the inability of some gas users to fully meet their gas demand on the day or generate assessment risks of path imbalance. Fourth, when there are many gas sources and users, the relationship matching between gas sources and users, the distribution of transmission volume and other issues involve complex logical relationships, large calculation amount, and high difficulty in solving. They may not be effectively solved only through manual experience or manual calculation. Summary of the invention

[0003] The purpose of the present invention is to address the deficiencies of the prior art and to propose a method for intelligently compiling a natural gas transportation plan based on operations research and optimization technology.

[0004] In a first aspect, a method for intelligently compiling a natural gas transportation plan based on operations research optimization technology is provided, comprising: Step 1: Initialize node information, including supply points and demand points; and express the network topology relationship from supply points to demand points in the form of a distance matrix; Step 2: Allocate the actual natural gas supply for the day according to the daily supply plan submitted by each supply point, and allocate the actual natural gas demand for the day according to the daily natural gas demand submitted by each demand point; then, based on the gas transmission plan for the month, estimate the daily estimated supply and demand from the current day to the end of the month; Step 3: Configure various restriction parameters according to the gas transmission plan for the month; 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 build a mixed integer programming model; Step 6: 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 daily gas transmission volume from each supply point to each demand point.

[0005] As a preference, it also includes: Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal gas transmission plan for the day and the estimated gas transmission plan for the remaining days of the month for reference by business personnel. Otherwise, reconfigure the restriction parameters.

[0006] Preferably, in step 2, the day of filling in the daily plan is used as the actual submission date, and all dates from the next day to the end of the month are used as estimated submission dates; for the actual submission date, based on the path specified in the gas transmission plan for that month, a virtual path connecting all supply points on that day is added for each demand point; for the estimated submission date, gas supply is restricted to the paths specified in the gas transmission plan for that month.

[0007] Preferably, in step 3, the various restriction parameters include: upper and lower limits of daily pipeline gas transmission volume, daily flow target, fixed flow requirement, pipeline storage and withdrawal volume, upper and lower limits of monthly plan deviation, and available paths specified in the monthly plan.

[0008] Preferably, in step 5, the constraints include: monthly actual flow definition constraint, actual demand satisfaction constraint, estimated demand satisfaction constraint, actual supply upper limit constraint, estimated supply upper limit constraint, relationship constraint between end-of-month settlement flow and monthly planned flow, relationship constraint between daily settlement flow and cumulative planned daily planned volume as of that day, fixed flow requirement constraint, transportation capacity constraint for each path, lower limit constraint for newly added virtual path transportation capacity, priority use of pipe storage and withdrawal volume constraint, and daily flow target constraint.

[0009] Preferably, in step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: Supply point To the demand point Gas delivery , Plan the supply point for the day To the demand point Gas delivery , from the supply point To the demand point The actual monthly volume , storage supply point In the The amount of gas supply in the pipeline that has not been used up , Supply Point To the demand point The monthly gas transmission volume is lower than the first threshold of the monthly planned volume for a long time , Supply Point To the demand point The monthly gas transmission volume is higher than the second threshold of the monthly planned volume for a long time. , Supply Point To the demand point Short-term violation volume below the first threshold of the cumulative daily average planned volume , Supply Point To the demand point Short-term violation volume above the second threshold of the cumulative daily average planned volume , in the Tiancong supply point To the demand point The gas volume exceeds the upper limit of the pipeline , Supply Point and demand points In the The daily traffic volume did not reach the daily traffic target value. , Demand Points In the Days of unmet demand Added supply points in the virtual path on the actual submission date To the demand point Gas delivery , the second threshold is greater than the first threshold; the Boolean variable includes and , Used to indicate the date of the plan Supply point To the demand point Is there gas transmission? Used to indicate the supply points added in the virtual path on the actual submission date To the demand point Is there gas transmission?

[0010] Preferably, in step 5, the overall optimization objective corresponding to the objective function is to minimize the gas volume actually transported by each pipeline that is lower than the first threshold of the monthly planned volume, and the overall optimization objective is composed of several optimization sub-objectives, including: Minimize the gas transmission volume of the newly added virtual path, minimize the long-term violation part of the monthly target fitting deviation, minimize the short-term violation part of the monthly target fitting deviation, minimize the value of unused gas extraction volume at the pipeline storage and access points, minimize the value of pipeline gas transmission exceeding the set upper limit of the pipeline, and the daily flow of the path reaches the daily flow target value and the estimated period needs to meet the daily demand.

[0011] In a second aspect, a natural gas transportation plan intelligent compilation system based on operations optimization technology is provided, which is used to execute any method described in the first aspect, including: An initialization module is used to initialize node information, where the nodes include supply points and demand points; and to represent the network topology relationship from the supply point to the demand point in the form of a distance matrix; The first configuration module is used to configure the actual daily natural gas supply according to the daily supply plan submitted by each supply point, and to configure the actual daily natural gas demand according to the daily natural gas demand submitted by each demand point; and then estimate the daily estimated supply and demand from the current day to the end of the month based on the gas transmission plan of the current month; The second configuration module is used to configure various restriction parameters according to the gas transmission plan of the month; A testing module, used for testing the rationality of the limiting 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 daily gas transmission volume from each supply point to each demand point.

[0012] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0013] In a fourth aspect, an electronic device is provided, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement any method as described in the first aspect.

[0014] The beneficial effects of the present invention are: 1. The operations optimization model proposed in the present invention can be based on the natural gas transmission plan for the month, the completion of the monthly plan as of the day, the route maintenance status, the supply reported by the gas source on the day, the demand reported by the user on the day and other information, with the goal of minimizing the fitting deviation with the monthly plan and ensuring that the actual gas transmission volume of each pipeline meets the assessment requirements of the national pipeline network. Under the condition of satisfying constraints such as supply and demand balance constraints and route transmission capacity constraints, the optimal daily gas transmission plan from the day to the end of the month is generated in real time, which can effectively improve planning efficiency and reduce the calculation burden of business personnel and enterprise operating costs.

[0015] 2. The operations optimization model proposed in the present invention can be based on the natural gas transmission plan for the month, the completion of the monthly plan as of the day, the route maintenance status, the supply reported by the gas source on the day, the demand reported by the user on the day and other information. It aims to minimize the fitting deviation with the monthly plan and ensure that the actual gas transmission volume of each pipeline meets the assessment requirements of the national pipeline network. Under the condition of satisfying constraints such as supply and demand balance constraints and route transmission capacity constraints, the optimal daily gas transmission plan from the day to the end of the month is generated in real time, which can effectively improve planning efficiency and reduce the calculation burden of business personnel and enterprise operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for intelligently compiling a natural gas transportation plan based on operations research and optimization technology provided by an embodiment of the present invention; Figure 2 A framework diagram of an operations research optimization model provided by an embodiment of the present invention; Figure 3 A flowchart of another method for intelligently compiling a natural gas transportation plan based on operations research and optimization technology provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a natural gas transportation plan intelligent compilation system based on operations optimization technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0018] Embodiment 1: In order to solve the problems of the prior art, Example 1 of the present application provides a method for intelligently compiling a natural gas transportation plan based on operations optimization technology. The purpose of this application is to calculate the optimal daily gas transmission plan from the beginning of the day to the end of the month based on data such as the natural gas monthly transportation plan, the completion status of the monthly transportation plan as of the day, the daily natural gas demand reported by the demand points, the daily natural gas supply reported by the supply points, and pipeline maintenance information, with the optimization goals of minimizing the deviation from the monthly plan, minimizing the excess gas supply at the gas storage and access points, and minimizing the excess gas transmission in the pipeline.

[0019] Specifically, Figure 1 As shown in the figure, a method for intelligent preparation of natural gas transportation plan based on operations optimization technology is provided. It includes: Step 1: Initialize node information, including supply points and demand points; and express the network topology relationship from supply points to demand points in the form of a distance matrix.

[0020] In step 1, during initialization, it is necessary to obtain the basic information of the supply point (supply point name, gas source point name included in the supply point, whether the gas source point is an adjustable gas source, upload point name included in the supply point, etc.) and the basic information of the demand point (demand point name, download point name included in the demand point, customer name and customer contact included in the demand point, etc.). 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.

[0021] It should be noted that during pipeline transportation, the natural gas supplied by the gas source point is transported to the pipeline system at the upload point. After being transported through the pipeline network, the natural gas is received from the pipeline system at the download point and delivered to the customer. The tuple consisting of the gas source and the upload point (gas source, upload point) is defined as the supply point, and the tuple consisting of the download point and the customer (customer, download point) is defined as the demand point, where the relationship between the gas source and the upload point and the download point and the customer are all many-to-many relationships. The path refers to the connection relationship from the supply point to the demand point.

[0022] Step 2: Allocate the actual natural gas supply for the day according to the daily supply plan submitted by each supply point, and allocate the actual natural gas demand for the day according to the daily natural gas demand submitted by each demand point; then, based on the gas transmission plan for the month, estimate the daily estimated supply and demand from the current day to the end of the month.

[0023] Among them, the estimated supply and estimated demand are estimated by natural gas business personnel based on actual business conditions. Generally, the average daily supply (demand) obtained by dividing the unfinished part of the monthly plan by the remaining days of the month is used as the estimated value for subsequent days.

[0024] In addition, in step 2, the day of filling in the daily plan is used as the actual submission day, and all dates from the next day to the end of the month are used as estimated submission days, and the actual gas transmission plan for the day and the daily estimated gas transmission plan from the next day to the end of the month are generated. For the actual submission day, based on the path specified in the gas transmission plan of the month, a virtual path connecting all supply points on the day is added for each demand point. When preparing the plan, the path specified in the monthly plan will be used to supply gas to the demand point. If the demand cannot be met, the virtual path will be used to supply gas, and the gas supply of the virtual path will be mapped to the original monthly plan path according to the mapping rules. For the estimated submission day, it is restricted to only use the path specified in the gas transmission plan of the month for gas supply.

[0025] The monthly target quantity fitting in the optimization target uses the planned quantity and actual quantity from the demand point to the supply point as the fitting standard. If there is a situation of storing gas in the pipeline network in the daily plan, the algorithm will not be considered, and the business personnel will manually submit it based on the results output by the algorithm; if there is a situation of taking gas from the pipeline network, the business personnel need to add virtual supply point information, provide the pipeline storage and gas withdrawal requirements of the actual supply point, and the gas volume taken from the pipeline network needs to be used first in the plan.

[0026] Step 3: Configure various restriction parameters according to the gas transmission plan for the month.

[0027] Specifically, according to the gas transmission plan for the month and the actual gas transmission situation of the month, the upper and lower limits of the daily gas transmission volume of the pipeline, the daily flow target, the fixed flow requirement, the gas storage and withdrawal volume of the pipeline, the upper and lower limits of the monthly plan deviation, the available paths specified in the monthly plan and other parameters are configured. Based on actual business rules, this embodiment stipulates some parameters as follows: the daily flow target expected to be achieved by the path is set to 1 / day, the upper limit of the monthly plan deviation is 105%, and the lower limit of the monthly plan deviation is 90%.

[0028] Step 4: Perform a rationality check on the restriction parameters.

[0029] For example, the rationality test includes: the total supply of all gas source points on a certain day should be greater than or equal to the total demand of all customers on that day, otherwise the supply and demand balance cannot be achieved; for a single demand point, the sum of the supply points of its connection path must be greater than or equal to the sum of the demand of that point on that day; the manually set fixed gas transmission volume cannot exceed the upper and lower limits of the pipeline gas transmission volume or the supply and demand limits of the day, and the upper limit of the pipeline gas transmission volume must be greater than the lower limit of the pipeline gas transmission volume, etc. If the parameter rationality test cannot be met, return to step 2.

[0030] Step 5: When the test is passed, Figure 2 As shown in the figure, according to the business requirements and the actual output parameters, the constraints, decision variables and objective functions are selected to build a mixed integer programming model.

[0031] The data used in the model mainly include: Supply point information: basic information of each supply point, including whether it is an adjustable gas source.

[0032] Demand point information: basic information of each demand point.

[0033] Supply chain topology: includes the location topology network relationship between each supply point and each demand point.

[0034] Gas transmission plan information for the current month: whether each path is matched for the current month, and the planned gas transmission volume for the matched paths for the current month.

[0035] Actual historical gas transmission information: historical daily gas transmission volume of each route up to the current day.

[0036] Daily actual reported demand information: the demand reported by each demand point on that day.

[0037] Daily estimated demand information: Estimated daily demand for each demand point from the next day to the end of the month.

[0038] Actual daily supply information submitted: upload point ID, gas source ID, date, daily supply volume.

[0039] Daily estimated supply information: upload point ID, gas source ID, date, daily supply volume.

[0040] Manually set flow table: fixed gas flow for each path set manually by business personnel.

[0041] Pipeline storage and gas withdrawal information: Pipeline storage and gas withdrawal volume manually set by business personnel.

[0042] Transportation parameters: upper and lower limits of route flow, daily average flow target and other parameters.

[0043] The above information is abstracted into specific parameters to input into the model. Table 1 defines all the parameters used in the optimization model: Table 1 Model parameters

[0044] In addition, Table 2 defines all decision variables used in the optimization model: Table 2 Decision variables

[0045] Decision variables are the optimization objects of the model. Designing appropriate decision variables can assist model optimization and ensure that the model outputs the correct gas transmission plan.

[0046] The overall optimization goal of the present invention is to minimize the actual gas volume of each pipeline that is less than 90% of the planned volume, to make the transportation volume of each pipeline reach the target as much as possible, to reduce the deviation from the monthly plan, and thus reduce the assessment risk of path imbalance. The objective function designed by the present invention is:

[0047] The various parts of the above objectives represent: (1) Minimize the gas transmission volume of the newly added LNG virtual path.

[0048] (2) Minimize the long-term violation portion of the monthly target fitting deviation so that at the end of the month, the actual gas transmission volume of each pipeline can be as high as 90% of the plan and less than 105% of the plan.

[0049] (3) Minimize the short-term violation part of the monthly target fitting deviation, that is, minimize the cumulative violation as of the planning day, so that the current execution plan of the system is in good condition and the flexibility and stability of the system are improved.

[0050] (4) Minimize the value of the unused gas volume at the pipe storage and access points, that is, the system gives priority to using pipe storage gas.

[0051] (5) Minimize the value of pipeline gas transmission exceeding the upper limit set by the pipeline.

[0052] (6) The daily flow of the route should reach the daily flow target value as much as possible.

[0053] (7) The estimated period needs to meet the daily demand as much as possible. This part is the penalty item when the estimated period demand is not met.

[0054] The overall optimization goal of the model is to find appropriate decision variable values ​​so that the value of the above objective function reaches the minimum value while satisfying the constraints.

[0055] The design formula of the objective function measures indicators such as the degree of monthly plan deviation, the degree of satisfaction of estimated demand, system flexibility and stability. The model will output a solution that minimizes the value of the objective function under the premise of meeting the constraints. In addition, the penalty coefficients of each item in the objective function are set optimally after repeated experimental tests. Appropriate penalty coefficients can enable the model to achieve better results.

[0056] All constraints involved in the present invention are defined as follows: (1) Definition of monthly actual flow rate: equal to the sum of historical actual flow rate plus the cumulative planned flow rate within the period (including pipeline storage and withdrawal volume).

[0057]

[0058] (2) The actual demand fully meets the constraints. During the actual planning period, the demand at each demand point needs to be met, and the supply sources of the demand points include the routes specified in the monthly plan and the newly added LNG virtual routes.

[0059]

[0060] (3) The estimated demand should meet the constraints as much as possible. During the estimated planning period, the demand at each demand point should be met as much as possible.

[0061]

[0062] (4) Actual supply limit constraint. During the actual planning cycle, the usage of each supply point cannot exceed the supply of that point, and the supply does not need to be fully used. (During the actual planning cycle, LNG adjustable supply points supply gas not only through the monthly planning path, but also through the virtual path)

[0063] (5) Estimated supply upper limit constraint. During the estimated planning period, the usage of each supply point cannot exceed the supply of that point, and the supply does not need to be fully used (during the estimated planning period and the actual planning period, other supply points except adjustable supply points are only supplied through the path in the monthly plan).

[0064]

[0065] (6) Constraints on the relationship between the month-end settlement flow and the monthly planned flow.

[0066]

[0067] (7) The relationship between the daily settlement flow and the cumulative planned daily volume up to that day.

[0068]

[0069] (8) Fixed flow requirement constraints. If a fixed flow requirement is set manually, it must be met.

[0070]

[0071] (9) Transport capacity constraints of each path. The lower limit of the path transport capacity is a hard constraint, and the upper limit of the path transport capacity is a soft constraint.

[0072]

[0073] (10) A new LNG virtual route is added to set a lower limit constraint on transportation capacity.

[0074]

[0075] (11) Prioritize the use of pipe storage and access gas volume constraints.

[0076]

[0077] (12) Daily flow target constraint: Once a route is enabled, the daily flow should be as close to the daily flow target as possible.

[0078]

[0079] Various constraints ensure that the output plan solved by the model will not exceed various restrictions, ensuring the smooth operation of the natural gas supply chain.

[0080] Step 6: 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 daily gas transmission volume from each supply point to each demand point.

[0081] Call the COPT solver to optimize and solve the planning model and obtain the output results. If the model has no solution (overly stringent parameter settings may lead to no solution), return to step 3 to reconfigure the parameters.

[0082] Embodiment 2: Based on Example 1, Example 2 of the present application provides a more specific method for intelligent preparation of natural gas transportation plans based on operations optimization technology. According to the daily demand reported by the demand points and the daily supply reported by the supply points actually obtained by a natural gas consignor in July 2024, in conjunction with the gas transmission plan for July manually formulated by business personnel, a daily gas transmission plan for that month is generated, and it is compared and analyzed with the actual plan formulated by the business personnel.

[0083] Specifically, Figure 3 As shown, the method includes: Step 1: Initialize node information, including supply points and demand points; and express the network topology relationship from supply points to demand points in the form of a distance matrix.

[0084] Step 2: Allocate the actual natural gas supply for the day according to the daily supply plan submitted by each supply point, and allocate the actual natural gas demand for the day according to the daily natural gas demand submitted by each demand point; then, based on the gas transmission plan for the month, estimate the daily estimated supply and demand from the current day to the end of the month.

[0085] Step 3: Configure various restriction parameters according to the gas transmission plan for the month.

[0086] Step 4: Perform a rationality check on the restriction parameters.

[0087] Step 5: When the test passes, select constraints, decision variables and objective functions to build a mixed integer programming model.

[0088] Step 6: 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 daily gas transmission volume from each supply point to each demand point.

[0089] Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal gas transmission plan for the day and the estimated gas transmission plan for the remaining days of the month for reference by business personnel. Otherwise, reconfigure the restriction parameters.

[0090] In step 7, business requirements refer to special requirements that require confirmation by business personnel and are not universal.

[0091] In addition, the optimal daily gas transmission plan generated by the model is compared with the daily gas transmission plan formulated by the business personnel. The comparison results of path fitting (paths with a path fitting rate of more than 90% refer to paths where the daily gas transmission volume in the daily plan is greater than 90% of the average daily planned gas transmission volume of the path under the monthly plan; paths with a path fitting rate of [90%-105%] refer to paths where the daily gas transmission volume in the daily plan is greater than 90% and less than 105% of the average daily planned gas transmission volume of the path under the monthly plan) are shown in Table 3. Compared with the current business situation, generating daily plans based on the model can increase the proportion of paths with a gas transmission volume fitting rate in the range of [90%-105%] by 15.79%, and increase the proportion of paths with a fitting rate of more than 90% by 7.52%, with significant effects.

[0092] Table 3 Comparison results of path fitting

[0093] The above objective function values ​​are calculated for the optimal daily gas transmission plan results generated by the model and the daily gas transmission plan formulated by the business personnel (where the long-term negative deviation is the part of the long-term violation in the second part of the total objective function that is less than 90% of the daily average plan; the long-term positive deviation is the part of the long-term violation in the second part of the total objective function that is greater than 105% of the daily average plan; the short-term negative deviation is the part of the short-term violation in the third part of the total objective function that is less than 90% of the daily average plan; the short-term positive deviation is the part of the short-term violation in the third part of the total objective function that is greater than 105% of the daily average plan), and the comparison results are shown in Table 4. Compared with the current business situation, the daily plan generated based on the model can reduce the total objective function value by 29330.17, a decrease of 68.17%, and except for the short-term positive deviation, all other deviations have been reduced, and the model effect is significant.

[0094] Table 4 Comparison results of objective function values

[0095] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0096] Embodiment 3: Based on Example 1, Example 3 of the present application provides a natural gas transportation plan intelligent compilation system based on operations optimization technology, including: An initialization module is used to initialize node information, where the nodes include supply points and demand points; and to represent the network topology relationship from the supply point to the demand point in the form of a distance matrix; The first configuration module is used to configure the actual daily natural gas supply according to the daily supply plan submitted by each supply point, and to configure the actual daily natural gas demand according to the daily natural gas demand submitted by each demand point; and then estimate the daily estimated supply and demand from the current day to the end of the month based on the gas transmission plan of the current month; The second configuration module is used to configure various restriction parameters according to the gas transmission plan of the month; A testing module, used for testing the rationality of the limiting 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 daily gas transmission volume from each supply point to each demand point.

[0097] In addition, if Figure 4 As shown, the intelligent compilation system of natural gas transportation plan can be divided into data layer, model algorithm layer and system layer. The optimization model module in the model algorithm layer can construct an optimization model according to the data in the data layer, and then solve it through the solver module to obtain the daily transportation plan of the natural gas pipeline network.

[0098] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiment 1. Therefore, the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be repeated in this application.

Claims

1. A method for intelligently compiling a natural gas transportation plan based on operations research and optimization technology, characterized in that: include: Step 1: Initialize node information, where the nodes include supply points and demand points; The network topological relationship from supply point to demand point is represented in the form of distance matrix; Step 2: Allocate the actual natural gas supply for the day according to the daily supply plan submitted by each supply point, and allocate the actual natural gas demand for the day according to the daily natural gas demand submitted by each demand point; Then, based on the gas transmission plan for the month, estimate the daily supply and demand from that day to the end of the month; Step 3: Configure various restriction parameters according to the gas transmission plan for the month; 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 build a mixed integer programming model; Step 6: 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 daily gas transmission volume from each supply point to each demand point.

2. The method for intelligently compiling a natural gas transportation plan based on operations research and 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 gas transmission plan for the day and the estimated gas transmission plan for the remaining days of the month for reference by business personnel. Otherwise, reconfigure the restriction parameters.

3. The method for intelligently compiling a natural gas transportation plan based on operations research optimization technology according to claim 2 is characterized in that: In step 2, the day of filling in the daily plan is taken as the actual submission day, and all dates from the next day to the end of the month are taken as the estimated submission days; for the actual submission day, based on the path specified in the gas transmission plan of the month, a virtual path connecting all supply points on the day is added for each demand point; for the estimated submission day, gas supply is restricted to the paths specified in the gas transmission plan of the month.

4. The method for intelligently compiling a natural gas transportation plan based on operations research optimization technology according to claim 3 is characterized in that: In step 3, the various restriction parameters include: upper and lower limits of daily pipeline gas transmission volume, daily flow target, fixed flow requirement, pipeline storage and withdrawal volume, upper and lower limits of monthly plan deviation, and available paths specified in the monthly plan.

5. The method for intelligently compiling a natural gas transportation plan based on operations research optimization technology according to claim 4 is characterized in that: In step 5, the constraints include: monthly actual flow definition constraint, actual demand satisfaction constraint, estimated demand satisfaction constraint, actual supply upper limit constraint, estimated supply upper limit constraint, month-end settlement flow and monthly planned flow constraint, daily settlement flow and cumulative planned daily planned flow constraint as of that day, fixed flow requirement constraint, transportation capacity constraint of each path, lower limit constraint of transportation capacity of newly added virtual paths, priority use of pipe storage and withdrawal volume constraint and daily flow target constraint.

6. The method for intelligently compiling a natural gas transportation plan based on operations research optimization technology according to claim 5 is characterized in that: In step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: Supply point To the demand point Gas volume , Plan the supply point for the day To the demand point Gas volume , from the supply point To the demand point The actual monthly volume , storage supply point In the The amount of gas supply in the pipeline that has not been used up , Supply Point To the demand point The monthly gas transmission volume is lower than the first threshold of the monthly planned volume for a long time , Supply Point To the demand point The monthly gas transmission volume is higher than the second threshold of the monthly planned volume for a long time. , Supply Point To the demand point Short-term violation volume below the first threshold of the cumulative daily average planned volume , Supply Point To the demand point Short-term violation volume above the second threshold of the cumulative daily average planned volume , in the Tiancong supply point To the demand point The gas volume exceeds the upper limit of the pipeline , Supply Point and demand points In the The daily traffic volume did not reach the daily traffic target value. , Demand Points In the Days of unmet demand Added supply points in the virtual path on the actual submission date To the demand point Gas delivery , the second threshold is greater than the first threshold; the Boolean variable includes and , Used to indicate the date of the plan Supply point To the demand point Is there gas transmission? Used to indicate the supply points added in the virtual path on the actual submission date To the demand point Is there gas transmission? 7. The method for intelligently compiling a natural gas transportation plan based on operations research optimization technology according to claim 6 is characterized in that: In step 5, the overall optimization objective corresponding to the objective function is to minimize the gas volume actually transported by each pipeline that is lower than the first threshold of the monthly planned volume. The overall optimization objective is composed of several optimization sub-objectives, including: Minimize the gas transmission volume of the newly added virtual path, minimize the long-term violation part of the monthly target fitting deviation, minimize the short-term violation part of the monthly target fitting deviation, minimize the value of unused gas extraction volume at the pipeline storage and access points, minimize the value of pipeline gas transmission exceeding the set upper limit of the pipeline, and the daily flow of the path reaches the daily flow target value and the estimated period needs to meet the daily demand.

8. A natural gas transportation plan intelligent compilation system based on operations optimization technology, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: An initialization module is used to initialize node information, where the nodes include supply points and demand points; and to represent the network topology relationship from the supply point to the demand point in the form of a distance matrix; The first configuration module is used to configure the actual daily natural gas supply according to the daily supply plan submitted by each supply point, and to configure the actual daily natural gas demand according to the daily natural gas demand submitted by each demand point; and then estimate the daily estimated supply and demand from the current day to the end of the month based on the gas transmission plan of the current month; The second configuration module is used to configure various restriction parameters according to the gas transmission plan of the month; A testing module, used for testing the rationality of the limiting 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 daily gas transmission volume from each supply point to each demand point.

9. 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 7.

10. 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 7.

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