An intelligent method for formulating natural gas transportation plans based on operations research and optimization techniques

Abstract

The present invention relates to an intelligent compilation method for natural gas transportation plans based on operations research optimization technology, including: initializing node information; configuring the actual supply and actual demand of natural gas for the current day, and estimating the daily estimated supply and demand from the current day to the end of the month; configuring various constraint parameters and conducting a rationality check; when the check passes, selecting constraint conditions, decision variables, and an objective function 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: under the condition of satisfying constraint conditions such as supply-demand balance constraint and path transportation capacity constraint, the present invention can generate an optimal daily gas transmission plan from the current day to the end of the current month in real time, which can effectively improve the planning efficiency and reduce the calculation burden of business personnel and 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 an intelligent method for formulating a natural gas transportation plan based on operations research optimization technology. Background Art

[0002] Currently, the daily gas transportation plan of natural gas shippers is basically manually formulated based on the experience of business personnel, which has great limitations, mainly reflected in the following aspects: First, the method of determining the gas transportation volume through manual experience or manual calculation is inefficient and difficult to respond to sudden situations such as route maintenance or changes in market supply and demand situations in a timely, accurate, and efficient manner. Second, the daily gas transportation plan determined based on manual experience may not be able to accurately fit the target gas transportation volume decomposed from the monthly plan to the current day, which may cause an extension of the gas transportation cycle and an increase in the pipeline transportation cost. Third, the gas transportation plan determined based on manual experience may not be able to fully meet the supply-demand balance in the presence of objective physical constraints such as the upper and lower limits of pipeline transportation, which may lead to the situation that the gas consumption demand of some gas customers on the current day cannot be fully met or the risk of unbalanced path assessment. Fourth, when there are many gas sources and users, problems such as the relationship matching between gas sources and users and the distribution of transportation volumes involve complex logical relationships, large computational amounts, and high solution difficulties, and may not be effectively solved only through manual experience or manual calculation. Summary of the Invention

[0003] The object of the present invention is to overcome the deficiencies of the prior art and propose an intelligent method for formulating a natural gas transportation plan based on operations research optimization technology.

[0004] In the first aspect, an intelligent method for formulating a natural gas transportation plan based on operations research optimization technology is provided, including:

[0005] Step 1, initialize node information, where 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;

[0006] Step 2, configure the actual natural gas supply volume of the current day according to the daily supply plan reported by each supply point, and configure the actual natural gas demand volume of the current day according to the natural gas demand reported by each demand point; then estimate the daily estimated supply volume and demand volume from the current day to the end of the month based on the monthly gas transportation plan;

[0007] Step 3, configure various limit parameters according to the monthly gas transportation plan;

[0008] Step 4, perform a rationality test on the limit parameters;

[0009] Step 5, when the test passes, select constraint conditions, decision variables, and an objective function 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 constraint parameters. The output result is the daily gas transmission volume from each supply point to each 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 daily gas transmission plan and the estimated gas transmission plan for the remaining days of the month for reference by business personnel; otherwise, reconfigure the constraint parameters.

[0013] Preferably, in Step 2, the day of filling 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, on the basis of the path specified in the monthly gas transmission plan, a virtual path connecting all supply points on the current day is added for each demand point. For the estimated submission days, it is restricted to only use the paths specified in the monthly gas transmission plan for gas supply.

[0014] Preferably, in Step 3, the various constraint parameters include: the upper and lower limits of the daily gas transmission volume of the pipeline, the daily flow target, the fixed flow requirement, the pipeline storage gas volume, the upper and lower limits of the monthly plan deviation volume, and the available paths specified in the monthly plan.

[0015] Preferably, in Step 5, the constraint conditions include: the constraint for defining the monthly actual flow, the constraint for meeting the actual demand, the constraint for meeting the estimated demand, the upper limit constraint for the actual supply, the upper limit constraint for the estimated supply, the relationship constraint between the end-of-month settlement flow and the monthly plan flow, the relationship constraint between the daily settlement flow and the cumulative planned daily plan volume up to the current day, the fixed flow requirement constraint, the transportation capacity constraint for each path, the lower limit constraint for the transportation capacity of the newly added virtual path, the constraint for preferentially using the pipeline storage gas volume, and the daily flow target constraint.

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

[0017] The continuous variables include: the gas transmission volume from supply point s to demand point d on the t-th day starting from the planned day The gas transmission volume from supply point s to demand point d on the planned day The monthly actual volume Y from supply point s to demand point d sd and the unused pipeline storage gas supply volume la of supply point s on the t-th day st the long-term violation volume of the monthly gas transmission volume from supply point s to demand point d below the first threshold of the monthly plan volume the long-term violation volume of the monthly gas transmission volume from supply point s to demand point d above the second threshold of the monthly plan volume the short-term violation volume of the monthly gas transmission volume from supply point s to demand point d below the first threshold of the cumulative daily average plan volume The short - term violation volume from supply point s to demand point d that is higher than the second threshold of the cumulative daily average planned volume The volume of gas transported from supply point s to demand point d on the t - th day that is higher than the upper limit of the pipeline restriction The value where the flow rate between supply point s and demand point d on the t - th day does not reach the daily flow rate target The unmet demand volume Ls at demand point d on the t - th day dt And the volume of gas transported vf from supply point a to demand point d in the newly added virtual path on the actual reporting date ad , where the second threshold is greater than the first threshold; the Boolean variables include and vx ad , Used to represent whether there is gas transportation from supply point s to demand point d on the t - th day starting from the planned day, vx ad Used to represent whether there is gas transportation from supply point a to demand point d in the newly added virtual path on the actual reporting date.

[0018] Preferably, in step 5, the overall optimization objective corresponding to the objective function is to minimize the volume of gas actually transported in each pipeline that is lower than the first threshold of the monthly planned volume. The overall optimization objective consists of several optimization sub - objectives, including:

[0019] Minimize the volume of gas transported in the newly added virtual path, minimize the long - term violation volume part in the monthly target fitting deviation volume, minimize the short - term violation volume part in the monthly target fitting deviation volume, minimize the value of the unused gas extraction volume at the pipeline access point, minimize the value of the pipeline gas transportation exceeding the pipeline set upper limit, the daily flow rate of the path reaches the daily flow rate target value, and the estimated period needs to meet the daily demand volume.

[0020] In a second aspect, there is provided an intelligent natural gas transportation plan compilation system based on operations research optimization technology for implementing any of the methods in the first aspect, including:

[0021] An initialization module for initializing node information, where the nodes include supply points and demand points; and representing the network topology relationship from supply points to demand points in the form of a distance matrix;

[0022] A first configuration module for configuring the actual gas supply volume on the current day according to the supply plan reported by each supply point, and configuring the actual gas demand volume on the current day according to the natural gas demand reported by each demand point; then estimating the daily estimated supply volume and demand volume from the current day to the end of the month based on the monthly gas transportation plan;

[0023] A second configuration module for configuring various limit parameters according to the monthly gas transportation plan;

[0024] A verification module for performing a rationality check on the limit parameters;

[0025] A construction module, configured to select constraint conditions, decision variables, and an objective function to construct a mixed-integer programming model when the inspection is passed;

[0026] A solving module, configured to solve the mixed-integer programming model, if there is a solution, obtain the output result, and if there is no solution, reconfigure the constraint parameters; the output result is the daily gas transmission volume from each supply point to each demand point.

[0027] 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 the method according to any one of the first aspects.

[0028] In a fourth aspect, an electronic device is provided, including:

[0029] A memory, configured to store the computer program;

[0030] A processor, configured to execute the computer program to implement the method according to any one of the first aspects.

[0031] The beneficial effects of the present invention are:

[0032] The operation research optimization model proposed by the present invention can be based on information such as the monthly natural gas transmission plan, the monthly plan completion situation as of the current day, the path maintenance situation, the supply volume reported by the gas source on the current day, and the demand volume reported by the users on the current day. With the goal of minimizing the fitting deviation from the monthly planned volume and ensuring that the actual gas transmission volume of each pipeline meets the assessment requirements of the national pipeline network, under the constraint conditions such as supply-demand balance constraints and path transmission capacity constraints, an optimal daily gas transmission plan from the current day to the end of the month can be generated in real time, which can effectively improve the planning efficiency and reduce the calculation burden of business personnel and the enterprise operation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of a method for intelligent compilation of a natural gas transportation plan based on operation research optimization technology provided by an embodiment of the present invention;

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

[0035] Figure 3 It is a flowchart of another method for intelligent compilation of a natural gas transportation plan based on operation research optimization technology provided by an embodiment of the present invention;

[0036] Figure 4 It is a structural schematic diagram of a system for intelligent compilation of a natural gas transportation plan based on operation research optimization technology provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The present invention will be 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 those of ordinary skill in the art, without departing from the principle of the present invention, several modifications can still 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 an intelligent method for formulating a natural gas transportation plan based on operations research optimization technology. The purpose of the present application is to calculate the optimal daily gas transportation plan from the current day to the end of the month with the optimization objectives of minimizing the deviation from the monthly plan, minimizing the excess gas supply at the gas storage and extraction points, and minimizing the excess gas transmission in the pipeline, based on data such as the monthly natural gas transportation plan, the completion of the monthly transportation plan as of the current 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.

[0040] Specifically, as Figure 1 shown, an intelligent method for formulating a natural gas transportation plan based on operations research optimization technology. It includes:

[0041] Step 1, initialize the node information, where 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.

[0042] In Step 1, during initialization, it is necessary to obtain the basic information of the supply points (supply point name, gas source point name included in the supply point, whether the gas source is an adjustable gas source, upload point name included in the supply point, etc.) and the basic information of the demand points (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.

[0043] It should be noted that during pipeline transportation, at the upload point, the natural gas supplied by the gas source point is transported into the pipeline system, and after pipeline transportation, at the download point, the natural gas is received from the pipeline system and transported to the customers. 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, where the relationships between the gas source and the upload point and between 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.

[0044] Step 2: Configure the actual daily natural gas supply volume according to the daily supply plans reported by each supply point, and configure the actual daily natural gas demand volume according to the daily natural gas demands reported by each demand point; then, based on the monthly gas transmission plan, estimate the daily estimated supply volume and demand volume from the current day to the end of the month.

[0045] Among them, the estimated supply volume and demand volume are estimated by natural gas business personnel according to the actual business situation. Generally, the average daily supply volume (demand volume) obtained by dividing the uncompleted supply volume (demand volume) in the monthly plan by the remaining days of the current month is used as the daily estimated value for the following days.

[0046] In addition, in Step 2, the current day of filling in the daily plan is taken as the actual reporting day, and all dates from the next day to the end of the month are taken as the estimated reporting days, and the actual gas transmission plan for the current day and the daily estimated gas transmission plans from the next day to the end of the month are generated. For the actual reporting day, based on the paths specified in the monthly gas transmission plan, virtual paths connecting all supply points on the current day are added for each demand point. When compiling the plan, the paths specified in the monthly plan will be preferentially used to supply gas to the demand points. If the demand cannot be met, the virtual paths will be used to supply gas, and the gas supply volume of the virtual paths will be mapped to the original monthly plan paths according to the mapping rules. For the estimated reporting days, it is restricted to supply gas only using the paths specified in the monthly gas transmission plan.

[0047] The monthly target volume fitting in the optimization objective uses the planned volume and actual occurrence volume from the demand points to the supply points as the fitting criteria. If there is a situation of storing gas in the pipeline network in the daily plan, the algorithm does not consider it, and the business personnel will make a manual report 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 requirements for the gas storage and extraction volume of the actual supply points, and the gas volume taken from the pipeline network needs to be preferentially used in the plan.

[0048] Step 3: Configure various limit parameters according to the monthly gas transmission plan.

[0049] Specifically, according to the monthly gas transmission plan and the actual gas transmission situation of the current month, parameters such as 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 extraction volume, the upper and lower limits of the monthly plan deviation volume, and the available paths specified in the monthly plan are configured. Based on the actual business rules, the following regulations are made for some parameters in this embodiment: the daily flow target that the path is expected to reach is set to 1 / day, the upper limit of the monthly plan deviation volume is 105%, and the lower limit of the monthly plan deviation volume is 90%.

[0050] Step 4: Conduct a rationality check on the limit parameters.

[0051] For example, the content of the rationality check 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 the same day; otherwise, the supply-demand balance cannot be achieved. For a single demand point, the sum of the supply volumes of the supply points on its connection path must be greater than or equal to the total demand volume of this point on the same day. The fixed gas transmission volume set manually cannot exceed the upper and lower limits of the pipeline gas transmission volume or the limits of the supply and demand volume on the same day, and the upper limit of the pipeline gas transmission volume must be greater than the lower limit. If the parameter rationality check cannot be satisfied, return to step two.

[0052] Step 5. When the inspection passes, as Figure 2 shown, select the constraint conditions, decision variables, and objective function according to the business requirements and the actually output parameters to construct a mixed-integer programming model.

[0053] The data used in the model mainly includes:

[0054] Supply point information: the basic information of each supply point, including whether it is an adjustable gas source.

[0055] Demand point information: the basic information of each demand point.

[0056] Supply chain topology structure: including the location topology network relationship between each supply point and each demand point.

[0057] Gas transmission plan information for the current month: whether each path is matched in the current month, and the planned gas transmission volume of the matched paths in the current month. Actual historical gas transmission information: the historical daily gas transmission volume of each path as of the current day.

[0058] Daily actual reported demand information: the demand volume reported by each demand point on the current day.

[0059] Daily estimated demand information: the estimated daily demand volume of each demand point from the next day to the end of the month.

[0060] Daily actual reported supply information: upload point id, gas source id, date, daily supply volume.

[0061] Daily estimated supply information: upload point id, gas source id, date, daily supply volume.

[0062] Manually set flow table: the fixed gas transmission volume of each path manually set by business personnel.

[0063] Pipeline gas storage and extraction information: the gas storage and extraction volume of the pipeline manually set by business personnel.

[0064] Transportation parameters: parameters such as the upper and lower limits of the path flow and the daily average flow target.

[0065] Abstract the above information into specific parameters and input them into the model. Table 1 defines all the parameters used in the optimization model:

[0066] Table 1 Model Parameters

[0067]

[0068]

[0069] In addition, Table 2 defines all decision variables used in the optimization model:

[0070] Table 2 Decision Variables

[0071]

[0072]

[0073] Decision variables are the objects to be optimized in the model. Designing appropriate decision variables can assist in model optimization and ensure that the model outputs the correct gas transmission plan.

[0074] The overall optimization objective of the present invention is: to minimize the gas volume actually transported by each pipeline below 90% of the plan, 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 to reduce the assessment risk of path imbalance. The objective function designed by the present invention is:

[0075]

[0076] Each part of the above objective represents respectively:

[0077] (1) Minimize the gas transmission volume of the newly added LNG virtual path.

[0078] (2) Minimize the long-term violation part in the monthly target fitting deviation, so that at the end of the month settlement, the actual gas transmission volume of each pipeline can be as high as possible above 90% of the plan and less than 105% of the plan.

[0079] (3) Minimize the short-term violation part in the monthly target fitting deviation, that is, minimize the cumulative violation up to the planned day, so that the current execution plan of the system is in a good state and the flexibility and stability of the system are improved.

[0080] (4) Minimize the value of the unused gas extraction volume at the pipe access gas point, that is, the system preferentially uses the stored gas in the pipe.

[0081] (5) Minimize the value of the pipeline gas transmission exceeding the pipeline set upper limit.

[0082] (6) The daily flow of the path should reach the daily flow target value as much as possible.

[0083] (7) The prediction period needs to meet the daily demand as much as possible. This part is a penalty term when the daily demand in the prediction period is not met.

[0084] The overall optimization objective of the model is: to find the appropriate values of decision variables so that, under the premise of meeting the constraint conditions, the value of the above objective function reaches the minimum.

[0085] The design formula of the objective function measures indicators such as the monthly plan deviation degree, the satisfaction degree of the estimated demand, the system flexibility and stability, etc. 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 item in the objective function are preferentially set after multiple repeated experimental tests, and appropriate penalty coefficients can make the model achieve better results.

[0086] All the constraint conditions involved in the present invention are defined as follows:

[0087] (1) Definition of monthly actual flow: It is equal to the sum of the historical actual flow and the cumulative planned flow within the period (including the gas storage and withdrawal volume of the pipeline).

[0088]

[0089] (2) Constraint of fully meeting the actual demand. During the actual planning period, the demand at each demand point needs to be met. The supply sources of the demand points include the paths specified in the monthly plan and the newly added LNG virtual paths.

[0090]

[0091] (3) Constraint of trying to meet the estimated demand. During the estimated planning period, the demand at each demand point should be met as much as possible.

[0092]

[0093] (4) Constraint of the upper limit of the actual supply. During the actual planning period, the usage amount at each supply point cannot exceed the supply amount of that point, and the supply amount does not need to be fully used. (During the actual planning period, the LNG adjustable supply points supply gas not only through the monthly plan paths but also through the virtual paths)

[0094]

[0095] (5) Constraint of the upper limit of the estimated supply. During the estimated planning period, the usage amount at each supply point cannot exceed the supply amount of that point, and the supply amount does not need to be fully used (for other supply points except the adjustable supply points during the estimated planning period and the actual planning period, they supply gas only through the paths in the monthly plan).

[0096]

[0097] (6) Constraint on the relationship between the end-of-month settlement flow and the monthly plan flow.

[0098]

[0099] (7) Relationship constraint between the daily settlement flow and the cumulative planned daily planned volume up to that day.

[0100]

[0101] (8) Fixed flow requirement constraint. If a fixed flow requirement is set manually, it needs to be met.

[0102]

[0103] (9) Conveyance capacity constraint for each path. Among them, the lower limit of the path conveyance capacity is a hard constraint, and the upper limit of the path conveyance capacity is a soft constraint.

[0104]

[0105] (10) Constraint on setting the lower limit of the transportation capacity for the newly added LNG virtual path.

[0106]

[0107] (11) Constraint on preferentially using the gas storage volume for pipeline access.

[0108]

[0109] (12) Daily flow target constraint. Once a path is enabled, the daily flow should approach the daily flow target value as much as possible.

[0110]

[0111] The above constraint conditions ensure that the output plan obtained by solving the model will not exceed the various limitations, guaranteeing the smooth operation of the natural gas supply chain.

[0112] 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 daily gas transmission volume from each supply point to each demand point.

[0113] Call the COPT solver to optimize and solve the planning 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.

[0114] Example 2:

[0115] Based on Embodiment 1, Embodiment 2 of the present application provides a more specific intelligent compilation method for natural gas transportation plans based on operations research optimization technology. According to the daily demand reported by demand points and the daily supply reported by supply points actually obtained by a natural gas shipper in July 2024, and in coordination with the gas transmission plan for July formulated manually by business personnel, the daily gas transmission plan for the current month is generated, and it is compared and analyzed with the actual plan formulated by business personnel.

[0116] Specifically, as Figure 3 shown, the method includes:

[0117] Step 1: Initialize node information, where 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.

[0118] Step 2: Configure the actual daily supply of natural gas according to the daily supply plan reported by each supply point, and configure the actual daily demand for natural gas according to the daily natural gas demand reported by each demand point; then, based on the gas transmission plan for the current month, estimate the daily estimated supply and demand from the current day to the end of the month.

[0119] Step 3: Configure various constraint parameters according to the gas transmission plan for the current month.

[0120] Step 4: Conduct a rationality check on the constraint parameters.

[0121] Step 5: When the check passes, select constraint conditions, decision variables, and objective functions to construct a mixed-integer programming model.

[0122] 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 daily gas transmission volume from each supply point to each demand point.

[0123] Step 7: Determine whether the output result meets the business requirements. If it meets, use the output result as the optimal daily gas transmission plan and the estimated gas transmission plan for the remaining dates of the current month for business personnel to refer to; otherwise, reconfigure the constraint parameters.

[0124] In Step 7, the business requirements refer to special requirements that need to be confirmed by business personnel and do not have universality.

[0125] In addition, a comparative analysis is conducted between the optimal daily gas transmission plan generated by the model and the daily gas transmission plan formulated by business personnel. The comparison results of the path fitting situation (the paths with a path fitting rate above 90% refer to the paths where the daily gas transmission volume in the daily plan is greater than 90% of the average daily planned gas transmission volume of this path under the monthly plan; the paths with a path fitting rate in the range of (90%-105%] refer to the 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 this path under the monthly plan) are shown in Table 3. Compared with the business status quo, generating the daily plan based on the model can increase the proportion of paths with a gas transmission fitting rate in the range of (90%-105%] by 15.79% and increase the proportion of paths with a fitting rate above 90% by 7.52%, with remarkable effects.

[0126] Table 3 Comparison Results of Path Fitting Situation

[0127]

[0128] For the optimal daily gas transmission plan results generated by the model and the daily gas transmission plan formulated by business personnel, the aforementioned objective function values are calculated (where the long-term negative deviation is the part of the second part of the total objective function's long-term violation amount that is less than 90% of the average daily planned volume; the long-term positive deviation is the part of the second part of the total objective function's long-term violation amount that is greater than 105% of the average daily planned volume; the short-term negative deviation is the part of the third part of the total objective function's short-term violation amount that is less than 90% of the average daily planned volume; the short-term positive deviation is the part of the third part of the total objective function's short-term violation amount that is greater than 105% of the average daily planned volume). The comparison results are shown in Table 4. Compared with the business status quo, generating the daily plan based on the model can reduce the total objective function value by 29330.17, with a decrease amplitude of 68.17%. Moreover, except for the short-term positive deviation, all other deviation amounts have been reduced, and the model has remarkable effects.

[0129] Table 4 Comparison Results of Objective Function Values

[0130]

[0131] 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.

[0132] Embodiment 3:

[0133] Based on Embodiment 1, Embodiment 3 of this application provides an intelligent compilation system for natural gas transportation plans based on operational research optimization technology, including:

[0134] An initialization module, used to initialize node information, where 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;

[0135] The first configuration module is used to configure the actual daily natural gas supply volume according to the daily supply plans reported by each supply point, and configure the actual daily natural gas demand volume according to the daily natural gas demands reported by each demand point; then, based on the monthly gas transmission plan, estimate the daily estimated supply volume and demand volume from the current day to the end of the month.

[0136] The second configuration module is used to configure various limit parameters according to the monthly gas transmission plan.

[0137] The inspection module is used to conduct a rationality inspection on the limit parameters.

[0138] 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.

[0139] The solution module is used to 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 daily gas transmission volume from each supply point to each demand point.

[0140] In addition, as Figure 4 shown, the intelligent compilation system for natural gas transportation plans can be divided into a data layer, a model algorithm layer, and a 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 for the natural gas pipeline network.

[0141] 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 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 current month, estimate the daily estimated supply and demand from the current day to the end of the month; 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 the estimated submission days; for the actual submission day, based on the path specified in the gas transmission plan for the current month, a virtual path connecting all supply points on the current day is added for each demand point; for the estimated submission day, the gas supply is restricted to the path specified in the gas transmission plan for the current month; if there is a situation of taking gas from the pipeline network in the daily plan, 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; 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; 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 the unused gas extraction volume at the pipe storage and access points, minimize the value of the 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; in step 5, the constraints include: monthly actual flow definition constraints, actual demand satisfaction constraints, estimated demand satisfaction constraints, actual supply upper limit constraints, estimated supply upper limit constraints, the relationship between the month-end settlement flow and the monthly planned flow, the relationship between the daily settlement flow and the cumulative planned daily plan as of that day, fixed flow requirement constraints, the transportation capacity constraints of each path, the lower limit constraints of the transportation capacity of the newly added virtual path, the priority use of pipe storage and access constraints and daily flow target constraints; in step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: Supply point to demand point Gas delivery , Plan the supply point for the day To the demand point Gas volume , from the supply point Monthly actual quantity to demand point , storage supply point In the Unused gas supply in pipeline , 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? 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 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.

4. A natural gas transportation plan intelligent compilation system based on operations optimization technology, characterized in that: The method for executing any one of claims 1 to 3 comprises: 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.

5. 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 3.

6. 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 3.

Images