A natural gas scheduling day plan optimization method and device

Abstract

The application relates to a natural gas scheduling daily plan optimization method and device. The method uses obtained initial data in a current statistical period, scheduling total plan data, constraint condition upper and lower limit data, current completed plan quantity data and unit price data in different stages of purchase, storage and transportation as dynamic parameter data in the statistical period, inputs the dynamic parameter data into a first stage target function with a maximum benefit in the statistical period as an optimization target, a second stage target function with minimum pipe network daily balance quantity in the statistical period as an optimization target and a preset constraint condition set, solves a natural gas daily scheduling optimization model constructed by the first stage target function, the second stage target function and the constraint condition set, obtains a first stage target function value of the first stage target function, a second stage target function value of the second stage target function and a daily plan optimization result, and can rollingly optimize scheduling daily plans in a remaining time period in the statistical period, thereby saving time and efficiently ensuring smooth implementation of the scheduling plan in the statistical period.

Description

Technical Field

[0001] This invention relates to the fields of natural gas dispatching and computer technology, and in particular to a method and apparatus for optimizing daily plans for natural gas dispatching. Background Technology

[0002] Currently, the natural gas dispatching process often involves communication and coordination between staff and relevant personnel from various sectors and companies, such as resources, sales, and trading. This relies on manual experience and uses Excel spreadsheets to arrange dispatching plans for the next day or at most the next week. However, natural gas production and operation is a continuous, uninterrupted system where different cycles interact and influence each other. The pipeline and tank inventories of the previous day will affect the production dispatching of the next day. The workload of calculating the next day's plan based solely on manual experience is already very large. Even if the plan for the next week is calculated, the monthly plan deviation may increase or affect operating efficiency because it does not take into account the pipeline inflow and outflow balance of each day at the end of the month. Any slight adjustment in the data will require re-coordinating all parties and re-formulating the plan.

[0003] In realizing the concept disclosed herein, the inventors discovered at least the following technical problems in the existing related technologies: the existing daily plans for natural gas dispatching are usually balanced by human experience, but human beings can usually only formulate the plan for the next day and cannot predict the long-term daily plan. Moreover, they cannot simultaneously take into account the impact of pipeline safety and stable operation, production and operation efficiency, and the mutual influence between physical constraints such as production capacity, pipeline transportation capacity, and injection and production capacity. The planning efficiency is extremely low. Even if the plan for the next week can be calculated, the monthly plan deviation will increase or affect the operating efficiency because the pipeline inflow and outflow balance of each day at the end of the month is not taken into account. This makes the adaptability of the dispatching to adjust the plan poor. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, embodiments of this disclosure provide a method and apparatus for optimizing daily natural gas dispatching plans.

[0005] In a first aspect, embodiments of the present invention provide a method for optimizing daily natural gas dispatch plans, comprising:

[0006] Acquire the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period;

[0007] A natural gas daily dispatch optimization model is constructed by using a first-stage objective function that maximizes benefits within a statistical period, a second-stage objective function that minimizes the daily balance of the pipeline network within a statistical period, and a set of preset constraints.

[0008] The dynamic parameter data within the statistical period is input into the optimization model for solution, to obtain the first-stage objective function value of the first-stage objective function, the second-stage objective function value of the second-stage objective function, and the daily plan optimization results.

[0009] In some optional embodiments, the step of inputting the dynamic parameter data within the statistical period into the optimization model for solving to obtain the first-stage objective function value of the first-stage objective function, the second-stage objective function value of the second-stage objective function, and the daily plan optimization results includes: solving the natural gas daily scheduling optimization model using a two-stage method;

[0010] In the first stage, under the preset set of constraints, the dynamic parameter data is input into the first stage objective function for solving to obtain the first stage objective function value;

[0011] In the second stage, the objective function value of the first stage is converted into constraints and added to the preset constraint set. The preset constraint set is then updated. Under the updated constraint set, the dynamic parameter data is input into the objective function of the second stage for solving, thereby obtaining the objective function value of the second stage and the daily plan optimization results.

[0012] In some optional embodiments, the objective function of the first stage is expressed as follows:

[0013] z max =∑(Sales Revenue - Production Costs - Trading Costs - Pipeline Transportation Costs - Gas Storage Injection and Production Costs),

[0014] Among them, z max To achieve the target operating efficiency of natural gas dispatch based on the daily plan optimization results, the sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost represent, in order, the daily natural gas sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost for the current day and future days within the statistical period.

[0015] The objective function for the second stage is expressed as follows:

[0016] z min =∑(pipeline inlet air - pipeline outlet air) + λ×ξ,

[0017] Among them, z min The target daily balance of the pipeline network for natural gas scheduling based on the daily plan optimization results is ξ, which is a slack variable and λ is a penalty coefficient. The pipeline inlet and pipeline outlet represent the total daily inlet and total outlet of the pipeline network in the current day and future days within the statistical period, respectively.

[0018] In some optional embodiments, the constraint expression transformed from the objective function value in the first stage is as follows:

[0019] Sales revenue - production costs - trading costs - pipeline transportation costs - gas storage injection and production costs ≥

[0020] The objective function value in the first stage is -ξ.

[0021] Here, ξ is a slack variable.

[0022] In some optional embodiments, the preset set of constraints includes:

[0023] A set of physical constraints; or

[0024] The set of physical constraints and the set of business constraints.

[0025] In some optional embodiments, for any day within the statistical period, including the current day and any future day, the set of physical constraints includes the following physical constraints:

[0026] ∑ node inflow = ∑ node outflow

[0027] End-of-period balance = Beginning-of-period balance + Daily balance.

[0028] Ending tank inventory = Beginning tank inventory + Daily change in tank inventory

[0029] Minimum production gas volume ≤ Gas source production gas volume ≤ Maximum production gas volume

[0030] Minimum inlet gas volume ≤ Inlet gas volume ≤ Maximum inlet gas volume

[0031] Minimum gas transmission capacity ≤ Pipeline segment gas transmission volume ≤ Maximum gas transmission capacity

[0032] Minimum injection / production rate ≤ Injection / production rate ≤ Maximum injection / production rate

[0033] Minimum gas consumption ≤ User sales volume ≤ Maximum gas consumption

[0034] Wherein, the node inflow and node outflow represent the inflow and outflow of gas at the nodes in the pipeline network, respectively; the nodes in the pipeline network represent the stations; the end-of-period daily balance, the beginning-of-period daily balance, and the current-day daily balance represent the natural gas balance of the pipeline network at the end of any given day, at the end of the day before any given day, and on the given day; the end-of-period tank inventory, the beginning-of-period tank inventory, and the change in tank inventory on the given day represent the LNG station tank inventory at the end of any given day, at the end of the day before any given day, and the change in LNG station tank inventory on the given day; the gas source production volume represents the amount of natural gas produced by the oilfield; the imported gas volume represents the amount of natural gas imported into the pipeline network; the injection / production gas volume represents the injection / production gas volume of the gas storage facilities in the pipeline network; and the user sales volume represents the amount of natural gas consumed by the user.

[0035] In some optional embodiments, for any day within the statistical period, including the current day and any future day, the set of business constraints includes the following business constraints:

[0036] Planned progress × First percentage ≤ Cumulative completion + Future date specified ≤ Planned progress × Second percentage

[0037] Daily pipeline inflow / outflow balance ≤ Total air inflow × Third ratio

[0038] Cumulative daily balance amount ≤ average daily intake amount during the cumulative period × fourth proportion

[0039] Shipping schedule + Beginning tank inventory = Gasification volume + Loading volume + Ending tank inventory

[0040] Daily balance of pipeline network = pipeline inlet gas - pipeline outlet gas - pipeline consumption gas.

[0041] Wherein, "cumulative completion" represents the sum of daily scheduling completions up to the day before the day in the statistical period; "future day specification" represents the value of scheduling completions on the day in the statistical period; "plan" represents the scheduling plan for the statistical period; "daily pipeline inflow / outflow balance" and "total air intake" represent the daily inflow / outflow balance and total air intake of the pipeline on any given day, respectively; and "cumulative daily balance" and "average daily air intake within the cumulative period" represent the daily balance of the pipeline within the statistical period, including the day in the statistical period and the days before the day in the statistical period, respectively. The cumulative sum of quantities and the average daily gas intake, the shipping schedule refers to the shipping date of LNG transported by ship and the volume transported on the shipping date, the initial tank inventory, the ending tank inventory, the gasification volume, and the loading volume refer to the initial tank inventory, the ending tank inventory, the gas volume entering the pipeline section after gasification, and the gas volume loaded for sale on the shipping date, respectively, and the daily balance of the pipeline network, the gas intake of the pipeline network, the gas output of the pipeline network, and the gas consumption of the pipeline network refer to the balance of the pipeline network, the gas intake volume, the gas output volume, and the gas consumption required for the operation of the pipeline network on any given day, respectively.

[0042] In some optional embodiments, the method further includes:

[0043] Enter each business constraint in the set of business constraints step by step;

[0044] The daily natural gas scheduling optimization model is debugged based on the business constraints in the set of business constraints input step by step.

[0045] In some optional embodiments, the step of debugging the natural gas daily scheduling optimization model based on the business constraints in the set of business constraints input step by step includes:

[0046] For the input business constraints, determine whether there is a contradiction between the input business constraints and the previously input constraints;

[0047] In the event of a contradiction, an error message is returned, and based on the error message, the input business constraints and / or the input constraints are debugged until there is no contradiction between the debugged constraints.

[0048] In some optional embodiments, the method further includes:

[0049] Retrieve the analysis dimensions specified by the user;

[0050] The daily plan optimization results for the current day and future days within the statistical period are analyzed to obtain and display the analysis results for the specified dimensions.

[0051] In a second aspect, embodiments of the present invention provide a natural gas dispatching daily plan optimization device, comprising:

[0052] The acquisition module is used to acquire the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period;

[0053] The module is used to construct a natural gas daily dispatch optimization model, which has a first-stage objective function with the goal of maximizing benefits within a statistical period, a second-stage objective function with the goal of minimizing the daily balance of the pipeline network within a statistical period, and a set of preset constraints.

[0054] The calculation module is used to input the dynamic parameter data within the statistical period into the optimization model for solving, and to obtain the first objective value of the first-stage objective function, the second objective value of the second-stage objective function, and the daily plan optimization results.

[0055] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0056] Memory, used to store computer programs;

[0057] A processor, when executing a program stored in memory, implements the method described in any of the above.

[0058] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the preceding claims.

[0059] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0060] The natural gas dispatching daily plan optimization method and apparatus provided in this invention utilize the current initial data, total dispatching plan, upper and lower limit data of constraints, currently completed planned volume data, and unit price data of different stages of procurement, storage, and transportation within the statistical period as dynamic parameter data within the statistical period. This dynamic parameter data is then input into a natural gas daily dispatching optimization model constructed with a first-stage objective function that aims to maximize benefits within the statistical period, a second-stage objective function that aims to minimize the daily balance of the pipeline network within the statistical period, and a preset set of constraints. The model is then solved to obtain the first-stage objective function value of the first-stage objective function, the second-stage objective function value of the second-stage objective function, and the daily plan optimization result. The natural gas dispatching daily plan optimization method of this invention, when establishing the optimization model, takes maximizing operational efficiency and minimizing the daily balance of the pipeline network as optimization objectives. It comprehensively considers the constraints of key areas of concern in production and operation, including production, pipeline transportation, gas storage, and LNG receiving stations. It realizes the construction of a multi-objective, multi-cycle intelligent dispatching optimization model with the goal of safe and stable operation and ensuring production and operation efficiency. Using this optimization model to solve and output the daily plan optimization results, the natural gas dispatching daily plan within the statistical period can be continuously optimized to ensure the smooth implementation of the dispatching plan within the statistical period. It effectively solves the technical problems that manual methods can usually only formulate the next day's plan and cannot predict the long-term daily plan. Furthermore, it cannot simultaneously take into account the safe and stable operation of the pipeline network, production and operation efficiency, and various constraints, resulting in extremely low planning efficiency and poor accuracy. It provides an overall balance direction for natural gas production and operation command and improves the efficiency of natural gas daily plan preparation. Attached Figure Description

[0061] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0063] Figure 1 The system architecture of the natural gas dispatching daily planning optimization method applicable to embodiments of the present invention is illustrated schematically;

[0064] Figure 2 A flowchart illustrating the daily natural gas dispatching plan optimization method according to an embodiment of the present invention is shown.

[0065] Figure 3 A detailed implementation flowchart of the natural gas dispatching daily plan optimization method according to an embodiment of the present invention is illustrated.

[0066] Figure 4 A schematic block diagram of a natural gas dispatching daily planning optimization device according to an embodiment of the present invention is shown.

[0067] Figure 5 A schematic block diagram of the electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0069] The inventors have discovered that currently, the natural gas dispatching process often relies on communication and coordination between staff and relevant personnel from various sectors and companies, such as resources, sales, and trading. This depends on manual experience and the use of Excel spreadsheets to plan dispatching for the next day or at most the following week. Taking a calendar month as an example, manually creating daily natural gas dispatching plans has the following problems:

[0070] The considerations are not comprehensive, and the balance is mainly based on manual experience. It is impossible to take into account the main factors affecting production and operation, such as pipeline balance and efficiency. At the same time, physical constraints such as production capacity, pipeline transportation capacity, and injection and production capacity also affect each other. It is impossible to take everything into account by relying solely on manual experience.

[0071] The calculation workload is large. Using Excel spreadsheets to prepare the daily natural gas plan for the next week is time-consuming and prone to errors. Business personnel need to communicate and coordinate with relevant personnel from various departments and professional companies, such as resources, sales, and trading. A small change in one part can affect the whole, and a small change in data may require coordination with all relevant parties. The adaptability to plan adjustments is poor.

[0072] The forecasting cycle is short. Natural gas production and operation is a continuous, uninterrupted system in which different cycles interact and influence each other. The pipeline and tank inventory of the previous day will affect the production scheduling of the next day. Manual methods can only calculate the scheduling situation of the next day or the next week, and cannot predict the completion of the plan in the long term to the end of the month.

[0073] To address the problem that existing natural gas dispatching daily planning processes typically only allow for the formulation of plans for the next day, making it impossible to predict long-term daily plans, and failing to simultaneously consider the impact on pipeline safety and stability, production and operational efficiency, as well as physical constraints such as production capacity, pipeline transportation capacity, and injection and production capacity, resulting in extremely low planning efficiency, even if plans for the next week can be calculated, the lack of consideration for pipeline inflow and outflow balances on each day within the statistical period can lead to increased deviations in the dispatching plan or negatively impact operations, this invention provides a method and apparatus for optimizing natural gas dispatching daily plans.

[0074] First, such as Figure 1 As shown in the figure, the embodiment of the present invention schematically illustrates the system architecture of the natural gas dispatching daily plan optimization method and apparatus applicable to the embodiment of the present invention.

[0075] Reference Figure 1 As shown, the system architecture 100 of the natural gas dispatching daily planning optimization method and apparatus applicable to embodiments of the present invention includes: terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0076] Operators can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages. Messages sent can be query information, and messages received can be query results. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0077] Terminal devices 101, 102, and 103 can be various electronic devices with displays that support web browsing, such as electronic devices including but not limited to smartphones, tablets, laptops, desktop computers, smartwatches, etc.

[0078] Server 105 can be a server that provides various services, such as a backend management server that provides data processing and service support for web pages accessed by operators using terminal devices 101, 102, and 103 (this is just an example). The backend management server can analyze and process the received access requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated based on the requests and instructions entered by the operator) to the terminal devices.

[0079] It should be noted that the natural gas scheduling daily plan optimization method provided in this embodiment of the invention can generally be executed by server 105 or a terminal device with a certain computing power. Correspondingly, the natural gas supply path allocation device provided in this embodiment of the invention can generally be installed in server 105 or the aforementioned terminal device with a certain computing power. The natural gas scheduling daily plan optimization method provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the natural gas supply path allocation device provided in this embodiment of the invention can also be installed in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0080] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0081] like Figure 2 As shown, the natural gas dispatching daily plan optimization method provided in this embodiment of the invention includes the following steps:

[0082] Step S201: Obtain the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period;

[0083] Step S202: Construct a natural gas daily dispatch optimization model using the first-stage objective function with the goal of maximizing benefits within the statistical period, the second-stage objective function with the goal of minimizing the daily balance of the pipeline network within the statistical period, and a set of preset constraints.

[0084] Step S203: Input the dynamic parameter data within the statistical period into the optimization model for solving, and obtain the first-stage objective function value of the first-stage objective function, the second-stage objective function value of the second-stage objective function, and the daily plan optimization results.

[0085] In this method, the initial data refers to the inventory of relevant data from the previous statistical period used as the initial data for the current statistical period when formulating the scheduling plan for the current statistical period. This data is the initial data. Similarly, the initial data for any day within the statistical period is the inventory of relevant data at the end of the previous day. The upper and lower limit data of the constraints include the maximum and minimum values ​​of constraints such as gas source generation volume and imported gas volume in the physical constraint set and the business constraint set. The currently completed planned volume data refers to the total planned scheduling volume completed on the day before the planned deadline within the statistical period. The unit price data for different stages of procurement, storage, and transportation refers to the unit price data generated at each stage of raw material procurement and natural gas storage and transportation.

[0086] This method solves the optimization model to obtain the scheduling plans for the current day and future days within the statistical period, and uses the scheduling plans for the current day and future days within the statistical period as the daily plan optimization results; the daily scheduling plan may include the supply and sales volume of natural gas resources scheduled for each day, the injection and production volume of gas storage facilities, the daily balance volume of the pipeline network, the flow rate of pipeline segments, the inventory of pipeline segments, and the changes in the tank inventory of LNG stations, etc.

[0087] This method, when constructing a daily natural gas dispatch optimization model, takes maximizing operational efficiency and minimizing the daily pipeline balance as optimization objectives. It comprehensively considers constraints affecting key areas of concern in production operations, including production, pipeline transportation, gas storage facilities, and LNG receiving terminals. This achieves a multi-objective, multi-period intelligent dispatch dynamic optimization model with the goals of safe and stable operation and ensuring production and operational efficiency. The optimization model is used to solve and output the daily plan optimization results, thereby continuously optimizing the daily natural gas dispatch plan for the statistical period to ensure the smooth implementation of the dispatch plan. This effectively solves the technical problems of manual planning, which typically only allows for next-day planning, cannot predict long-term daily plans, and cannot simultaneously consider pipeline safety and stability, production and operational efficiency, and various constraints, resulting in extremely low planning efficiency and poor accuracy. The daily natural gas dispatch plan optimization method provided by this invention can provide an overall balance direction for production operation command and improve the efficiency of daily natural gas plan preparation.

[0088] Meanwhile, the method in this embodiment of the invention comprehensively considers important indicators affecting production operations, such as production capacity, storage and transportation facility capacity, and sales capacity, including physical capacity constraints such as production, pipeline transportation, gas storage facilities, and LNG receiving terminals. It utilizes a natural gas skip-reading daily plan optimization model to perform rolling optimization of daily natural gas scheduling, achieving multi-cycle dynamic optimization of natural gas dispatch and ensuring the smooth implementation of monthly plans. This solves problems such as incomplete manual planning, low efficiency, and short prediction cycles, improving computational efficiency and accuracy. It is comprehensive, has a fast solution speed, and a long prediction cycle. Adjustments to the plan only require recalculation by modifying parameters to achieve a balance between supply and demand, resulting in high computational efficiency and fully leveraging its corrective function to ensure the smooth implementation of monthly plans.

[0089] In step S203, dynamic parameter data within the statistical period are input into the optimization model for solving to obtain the first-stage objective function value of the first-stage objective function, the second-stage objective function value of the second-stage objective function, and the daily plan optimization results, including solving the natural gas daily scheduling optimization model using the two-stage method.

[0090] In the first stage, under the preset set of constraints, the dynamic parameter data is input into the first stage objective function for solving, and the first stage objective function value is obtained.

[0091] In the second stage, the objective function value of the first stage is converted into constraints and added to the preset constraint set. The preset constraint set is then updated. Under the updated constraint set, the dynamic parameter data is input into the objective function of the second stage for solving, and the objective function value of the second stage and the daily plan optimization results are obtained.

[0092] Optionally, the first-stage objective function of the optimization model constructed in this embodiment is as follows:

[0093] z max =∑(Sales Revenue - Production Costs - Trading Costs - Pipeline Transportation Costs - Gas Storage Injection and Production Costs),

[0094] Among them, z max To achieve the target operating efficiency of natural gas dispatch based on the daily plan optimization results, the sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost represent, in order, the daily natural gas sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost for the current day and future days within the statistical period.

[0095] Furthermore, within this optimization model, the objective function for the second stage is expressed as follows:

[0096] z min =∑(pipeline inlet air - pipeline outlet air) + λ×ξ,

[0097] Among them, zmin The target daily balance of the pipeline network for natural gas scheduling based on the daily plan optimization results is ξ, which is a slack variable and λ is a penalty coefficient. The pipeline inlet and pipeline outlet represent the total daily inlet and total outlet of the pipeline network in the current day and future days within the statistical period, respectively.

[0098] Furthermore, in the second stage of solving the objective function of the optimization model, the objective function value of the first stage is converted into one of the constraints, and this constraint is added to the preset constraint set. This means that during the second stage of solving the optimization model, the constraints include not only the constraints in the preset constraint set but also the constraints converted from the objective function value of the first stage. The constraint converted from the objective function value of the first stage can be expressed as follows:

[0099] Sales revenue - production costs - trading costs - pipeline transportation costs - gas storage injection and production costs ≥

[0100] The objective function value in the first stage is -ξ.

[0101] Here, ξ is a slack variable.

[0102] Specifically, the above process can be implemented as follows: in the first stage, the objective function value of the first stage is obtained. This objective function value can be understood as the optimal solution that satisfies the objective function of the first stage. Then, in the second stage, in order to satisfy the objective function of the second stage, the optimal solution that satisfies the objective function of the second stage can be found in the neighborhood of the objective function value of the first stage. This optimal solution is used as the daily plan optimization result of the optimization model. Since the objective function of the second stage of the model is to minimize the daily balance of the pipeline network, the purpose is to ensure safe and stable operation. Therefore, the solution result satisfies both objective functions, so that the final daily plan optimization result can maximize benefits while minimizing the daily balance of the pipeline network, thus ensuring safe and stable operation.

[0103] In the second stage, the objective function value of the first stage is transformed into constraints. To ensure the achievement of the goal of minimizing the daily balance of the pipeline network, a certain degree of relaxation is allowed in the objective function value of the first stage. Among them, the relaxation variables and penalty coefficients restrict the neighborhood. The penalty coefficient is a positive number that is as large as possible. In order to satisfy the goal of minimizing the objective function, the relaxation, and the neighborhood in the second stage, the specific values ​​of the relaxation variables and penalty coefficients in this optimization model can be selected by the user.

[0104] In one possible implementation, the preset set of constraints includes a set of physical constraints; or a set of physical constraints and a set of business constraints.

[0105] Optionally, for any day within the statistical period, including the current day and any future day, the set of physical constraints includes the following physical constraints:

[0106] ∑ node inflow = ∑ node outflow

[0107] End-of-period balance = Beginning-of-period balance + Daily balance.

[0108] Ending tank inventory = Beginning tank inventory + Daily change in tank inventory

[0109] Minimum production gas volume ≤ Gas source production gas volume ≤ Maximum production gas volume

[0110] Minimum inlet gas volume ≤ Inlet gas volume ≤ Maximum inlet gas volume

[0111] Minimum gas transmission capacity ≤ Pipeline segment gas transmission volume ≤ Maximum gas transmission capacity

[0112] Minimum injection / production rate ≤ Injection / production rate ≤ Maximum injection / production rate

[0113] Minimum gas consumption ≤ User sales volume ≤ Maximum gas consumption

[0114] Among them, node inflow and node outflow represent the inflow and outflow of gas at nodes in the pipeline network, respectively. Nodes in the pipeline network represent stations. End-of-period daily balance, beginning-of-period daily balance, and current-day daily balance represent the natural gas balance of the pipeline network at the end of any day, the end of the day before any day, and the current day, respectively. End-of-period tank inventory, beginning-of-period tank inventory, and current-day tank inventory change represent the LNG station tank inventory at the end of any day, the end of the day before any day, and the current-day LNG station tank inventory change, respectively. Gas source production volume represents the amount of natural gas produced by the oilfield. Imported gas volume represents the amount of natural gas imported into the pipeline network. Injection / production gas volume represents the injection / production gas volume of gas storage facilities in the pipeline network. User sales volume represents the amount of natural gas consumed by users.

[0115] Specifically, the maximum and minimum values ​​of each physical quantity in the aforementioned physical constraints can be upper and lower limits adopted based on actual natural gas operations, rather than the physical limits of each component of the pipeline network. This is because actual operating conditions must be considered, and for the purpose of ensuring safe operation, the upper and lower limits are usually provided by operational personnel. Based on the parameter values ​​pre-input by the operational personnel, these limits are retrieved from storage media such as servers storing parameter data when adding constraints, and subsequent operations are performed. Therefore, in practical applications, if changes in business requirements cause changes in parameters and constraints, adjustments to the plan only require recalculating the parameters to achieve a balance between sales and purchases. This method is highly efficient, fully utilizes its corrective function, and ensures the smooth implementation of the monthly plan.

[0116] Furthermore, for any day within the statistical period, including the current day and any future day, the set of business constraints includes the following business constraints:

[0117] Planned progress × First percentage ≤ Cumulative completion + Future date specified ≤ Planned progress × Second percentage

[0118] Daily pipeline inflow / outflow balance ≤ Total air inflow × Third ratio

[0119] Cumulative daily balance amount ≤ average daily intake amount during the cumulative period × fourth proportion

[0120] Shipping schedule + Beginning tank inventory = Gasification volume + Loading volume + Ending tank inventory

[0121] Daily balance of pipeline network = gas inlet of pipeline network - gas outlet of pipeline network - gas consumption of pipeline network;

[0122] Among them, "cumulative completion" represents the sum of daily scheduling completed before any day within the statistical period; "future day specified" represents the value of scheduling completed on any day; "plan" represents the scheduling plan for the statistical period; "daily pipeline inflow / outflow balance" and "total gas inflow" represent the daily inflow / outflow balance and total gas inflow of the pipeline on any given day, respectively; "cumulative daily balance" and "cumulative average daily gas inflow" represent the sum of daily balance and average daily gas inflow of the pipeline within the statistical period, including any given day and previous days, respectively; "shipment schedule" represents the transportation date of LNG transported by ship and the transportation volume on that day; "beginning tank inventory," "ending tank inventory," "gasification volume," and "loading volume" represent the beginning tank inventory, ending tank inventory, gasification volume entering the pipeline section, and gas volume loaded for sale on the transportation date, respectively; and "daily pipeline balance," "pipeline gas inflow," "pipeline gas outflow," and "pipeline gas consumption" represent the balance, inflow, outflow, and gas consumption required for pipeline operation on any given day, respectively.

[0123] In one possible implementation, the first, second, third, and fourth ratios can all be set according to specific business needs, such as the requirements provided by business personnel, the operating conditions of the natural gas pipeline network, and standards.

[0124] Optionally, the first ratio can be 97%, the second ratio can be 103%, the third ratio can be 3%, and the fourth ratio can be 8%.

[0125] In some optional embodiments, the method provided by the embodiments of the present invention further includes: inputting each business constraint in the set of business constraints step by step; and debugging the daily natural gas scheduling optimization model according to the business constraints in the set of business constraints input step by step.

[0126] Furthermore, based on the various business constraints in the set of business constraints input step by step, the daily natural gas scheduling optimization model is debugged, including:

[0127] For the input business constraints, determine whether there is a contradiction between the input business constraints and the input constraints.

[0128] In the event of a contradiction, an error message is returned, and based on the error message, the input business constraints and / or the already input constraints are debugged until there are no contradictions between the debugged constraints.

[0129] In practical applications, natural gas operations involve numerous operating conditions and market factors that need to be considered. The constraints involved can be adjusted according to business needs, and there are often many constraints that may conflict with each other. Therefore, a phased approach is adopted to add constraints, allowing for staged model adjustments. For example, combined constraints can be added for total quantities across various dimensions. Pre-calculation data validation is also implemented, pre-judging for missing data or inconsistencies between constraints before calculation. Furthermore, error messages and calculation logs at key nodes are added to assist in model debugging.

[0130] Optionally, debugging the daily natural gas dispatch optimization model may include: verifying whether there are any missing parameters used to solve or construct various constraints. For example, if the value of a certain parameter is empty and cannot be solved, an error can be reported or a warning can be returned to remind business personnel to make corrections.

[0131] In some optional embodiments, the method provided by the embodiments of the present invention may further include: obtaining the analysis dimension specified by the user; analyzing the daily plan optimization results of the current day and future days within the statistical period, obtaining the analysis results of the dimension, and displaying them.

[0132] In practical applications, business personnel will conduct necessary analyses based on the daily plan optimization results. Due to the large amount of data, the analysis dimensions commonly used by business personnel can be pre-set, such as the resource supply of each province. After obtaining the daily plan optimization results, the resource supply data of each province can be displayed to business personnel in a preset form, such as curves or charts, to assist business personnel in making resource optimization and allocation decisions.

[0133] Optional, such as Figure 3 As shown, the detailed process of the natural gas dispatching daily plan optimization method in this embodiment of the invention can be as follows:

[0134] Step S301: Natural Gas Business Abstraction and Assumptions;

[0135] Specifically, the entire natural gas production, transportation, sales, storage and trading industry chain is abstracted into various business components, including stations, pipelines, gas sources, customers, gas storage facilities, etc. There are connections between these business components. Assume that each component is given certain characteristics, such as the business components being connected through nodes, nodes representing stations, and the inflow of a node equaling its outflow.

[0136] Step S302: Construct the business component connection matrix;

[0137] Specifically, a connection matrix is ​​constructed based on the connection relationships of each business component. The entire natural gas industry chain system is interconnected, and connected business components can be found by corresponding codes of upstream and downstream nodes of pipeline components.

[0138] Step S303: Determine the decision variables and objective function, and establish an intelligent scheduling dynamic optimization model;

[0139] Specifically, based on the actual operation of the natural gas industry chain, and on the premise of ensuring safe production and operation and the balance of pipeline inflow and outflow, a multi-objective, multi-period optimization model is established with the optimization objectives of maximizing benefits and minimizing imbalance. This model can be called the natural gas daily dispatch optimization model.

[0140] The decision variables of the model are the daily quantities of various resources, sales volume, gas storage injection and production volume, daily pipeline balance volume, flow rate of each pipeline segment, pipeline inventory of each pipeline segment, and tank inventory. Considering the pipeline inventory of each pipeline segment, the flow rate of each pipeline segment is set as a 01 variable, where 01 represents the flow direction, 0 is reverse and 1 is forward.

[0141] The above optimization model can be solved using a two-stage method, which can specifically include:

[0142] In the first stage, under the preset set of constraints, the dynamic parameter data is input into the first stage objective function for solving, and the first stage objective function value is obtained.

[0143] In the second stage, the objective function value of the first stage is converted into constraints and added to the preset constraint set. The preset constraint set is then updated. Under the updated constraint set, the dynamic parameter data is input into the objective function of the second stage for solving, and the objective function value of the second stage and the daily plan optimization results are obtained.

[0144] The objective function of the first stage of the model is to maximize benefits to ensure operational efficiency. The objective function of the first stage can be expressed as follows:

[0145] z max =∑(Sales Revenue - Production Costs - Trading Costs - Pipeline Transportation Costs - Gas Storage Injection and Production Costs),

[0146] Among them, z maxTo determine the target operating efficiency of natural gas dispatch based on the daily plan optimization results, sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost are respectively represented as the daily natural gas sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost for the current day and future days within the statistical period.

[0147] The objective function of the second stage of the optimization model is to minimize the daily balance of the pipeline network to ensure safe and stable operation. The objective function of the second stage can be expressed as follows:

[0148] z min =∑(pipeline inlet air - pipeline outlet air) + λ×ξ,

[0149] Among them, z min The target pipeline balance quantity for natural gas dispatching based on the daily plan optimization results is ξ, where ξ is a slack variable and λ is a penalty coefficient. The pipeline inlet and outlet represent the total daily inlet and outlet volume of the pipeline within the statistical period for the current day and future days, respectively. Here, ξ is a slack variable, and λ is a penalty coefficient, which is a positive number that is as large as possible.

[0150] In the second stage, the first objective function value is converted into a constraint condition and added to the preset constraint condition set. In order to ensure the achievement of the minimum daily balance target of the pipeline network, the first objective function value is allowed to relax to a certain extent. Optionally, the specific value of the relaxation variable can be selected according to the actual situation, and this invention does not limit it.

[0151] The constraint condition for transforming the value of the first objective function can be expressed as follows:

[0152] Sales revenue - production costs - trading costs - pipeline transportation costs - gas storage injection and production costs ≥

[0153] The objective function value for the first stage is -ξ.

[0154] Step S304: Add physical constraints;

[0155] Specifically, the physical condition set in this method may include the following physical constraint expressions:

[0156] ∑ node inflow = ∑ node outflow

[0157] End-of-period balance = Beginning-of-period balance + Daily balance.

[0158] Ending tank inventory = Beginning tank inventory + Daily change in tank inventory

[0159] Minimum production gas volume ≤ Gas source production gas volume ≤ Maximum production gas volume

[0160] Minimum inlet gas volume ≤ Inlet gas volume ≤ Maximum inlet gas volume

[0161] Minimum gas transmission capacity ≤ Pipeline segment gas transmission volume ≤ Maximum gas transmission capacity

[0162] Minimum injection / production rate ≤ Injection / production rate ≤ Maximum injection / production rate

[0163] Minimum gas consumption ≤ User sales volume ≤ Maximum gas consumption

[0164] Wherein, the node inflow and node outflow represent the inflow and outflow of gas at the nodes in the pipeline network, respectively; the nodes in the pipeline network represent the stations; the end-of-period daily balance, the beginning-of-period daily balance, and the current-day daily balance represent the natural gas balance of the pipeline network at the end of any given day, at the end of the day before any given day, and on the given day; the end-of-period tank inventory, the beginning-of-period tank inventory, and the change in tank inventory on the given day represent the LNG station tank inventory at the end of any given day, at the end of the day before any given day, and the change in LNG station tank inventory on the given day; the gas source production volume represents the amount of natural gas produced by the oilfield; the imported gas volume represents the amount of natural gas imported into the pipeline network; the injection / production gas volume represents the injection / production gas volume of the gas storage facilities in the pipeline network; and the user sales volume represents the amount of natural gas consumed by the user.

[0165] Specifically, in the natural gas business, the entire natural gas production, transportation, sales, storage and trading industry chain can be abstracted into a variety of business components. Since the pipeline network includes physical components such as gas sources, gas storage facilities, LNG stations, users, stations and pipeline sections, the pipeline network can be abstracted into nodes and different business components connected to each node. Typically, a node can represent a physical station, and the business components connected to the node include pipeline section components, gas source components, gas storage facilities components and user components.

[0166] Step S305: Add other business constraints step by step;

[0167] Specifically, its set of business constraints may include the following constraints:

[0168] Planned progress × First percentage ≤ Cumulative completion + Future date specified ≤ Planned progress × Second percentage

[0169] Daily pipeline inflow / outflow balance ≤ Total air inflow × Third ratio

[0170] Cumulative daily balance amount ≤ average daily intake amount during the cumulative period × fourth proportion

[0171] Shipping schedule + Beginning tank inventory = Gasification volume + Loading volume + Ending tank inventory

[0172] Daily balance of pipeline network = pipeline inlet gas - pipeline outlet gas - pipeline consumption gas.

[0173] Wherein, "cumulative completion" represents the sum of daily scheduling completions up to the day before the day in the statistical period; "future day specification" represents the value of scheduling completions on the day in the statistical period; "plan" represents the scheduling plan for the statistical period; "daily pipeline inflow / outflow balance" and "total air intake" represent the daily inflow / outflow balance and total air intake of the pipeline on any given day, respectively; and "cumulative daily balance" and "average daily air intake within the cumulative period" represent the daily balance of the pipeline within the statistical period, including the day in the statistical period and the days before the day in the statistical period, respectively. The cumulative sum of quantities and the average daily gas intake, the shipping schedule refers to the shipping date of LNG transported by ship and the volume transported on the shipping date, the initial tank inventory, the ending tank inventory, the gasification volume, and the loading volume refer to the initial tank inventory, the ending tank inventory, the gas volume entering the pipeline section after gasification, and the gas volume loaded for sale on the shipping date, respectively, and the daily balance of the pipeline network, the gas intake of the pipeline network, the gas output of the pipeline network, and the gas consumption of the pipeline network refer to the balance of the pipeline network, the gas intake volume, the gas output volume, and the gas consumption required for the operation of the pipeline network on any given day, respectively.

[0174] Optionally, in the expression of the business constraint, the first ratio can be 3%, the second ratio can be 103%, the third ratio can be 3%, and the fourth ratio can be 8%.

[0175] Step S306: Input dynamic parameters such as upper and lower limits of capability;

[0176] Specifically, input the upper and lower limits of various resources, sales, injection and production, losses, etc., and the data on the initial gas intake and daily balance of the statistical period; tank inventory, shipping schedule; monthly plan, cumulative daily completion status, etc.

[0177] Step S307: Optimize the solution and debug the model;

[0178] Specifically, solving the daily natural gas scheduling optimization model in this embodiment involves solving mixed integer linear programming (MILP) and multi-objective programming (MOP) models. After adding physical constraints, operational constraints are added step by step. For each operational constraint, after adding the operational constraint, it is determined whether there is a contradiction between the operational constraint and the added constraints. If a contradiction exists, an error message is returned, and based on the error message, the added operational constraints and / or the added constraints are debugged until there are no contradictions between the debugged constraints.

[0179] For example, since this method involves many constraints and there may be contradictions between them, the constraints are added step by step, and the model is debugged in stages. Combined constraints can be added for the total amount of each dimension, and data verification before calculation is added. Before calculation, the missing data or contradictions between constraints are pre-judged. At the same time, error messages and calculation logs of key nodes are added to assist in model debugging.

[0180] Step S308: Output the daily specified optimization results for natural gas;

[0181] Specifically, based on future needs such as increasing or decreasing pipeline and tank storage, the input parameters are flexibly adjusted. The model automatically finds the optimal solution that meets the physical capacity constraints of production, storage, transportation, and sales under the optimization objectives of ensuring the minimum daily balance of the natural gas pipeline network and optimizing production and operation efficiency, and outputs it as a daily specified plan.

[0182] Step S309: Result statistics and comparative analysis;

[0183] Specifically, based on future needs to adjust targets such as pipeline and tank storage, the input parameters are adjusted, and the targets are achieved by adjusting various schemes such as LNG, gas storage injection and production, and sales volume. The model automatically performs statistical and comparative analysis of the results to assist business personnel in making decisions such as resource optimization and allocation.

[0184] For example, in practical applications, it is known that recent weather changes will affect user demand. For instance, if a temperature drop is known next week during winter, it can be predicted that the gas supply to users will increase, requiring an increase of K units of pipeline inventory. Therefore, by adjusting relevant parameters, the lower limit of the pipeline inventory target for next week in the optimization results can be raised by at least K units, thereby meeting actual business needs. Correspondingly, this adjustment allows for recalculation of the daily plan optimization results, which can then be presented to business personnel to assist in making adjustments to resource optimization and allocation decisions.

[0185] The natural gas dispatching daily plan optimization method provided in this invention can continuously optimize the daily specified plans for the sales side, resource side, and regulation side within the remaining time period of the statistical cycle, with a long forecast period. Adjustments to the plan only require recalculation by modifying parameters to achieve a balance between supply and demand, resulting in high computational efficiency, fully leveraging the corrective function, and ensuring the smooth implementation of the monthly plan.

[0186] Based on operational data from actual applications, the optimization model in this embodiment of the invention actually includes more than 5,400 business components, with a computational scale of more than 200,000 constraints and more than 340,000 variables. By adding timestamps to determine time consumption, optimizing the model's solution accuracy, the handling of objective functions and constraints, and the number of integer variables, the model's solution efficiency is optimized, reducing the initial model's 2 hours to less than 5 minutes. This further verifies the following advantages of the natural gas dispatching daily plan optimization method provided in this embodiment of the invention: high computational efficiency, comprehensive consideration and fast solution speed, long prediction cycle, and the ability to achieve a balance between sales and purchases simply by modifying parameters and recalculating for plan adjustments, fully leveraging the corrective effect and ensuring the smooth implementation of the monthly plan.

[0187] Based on the above operations, the natural gas daily scheduling optimization method of this invention constructs a natural gas daily scheduling optimization model with the optimization objectives of maximizing operational efficiency and minimizing the daily balance of the pipeline network. It comprehensively considers constraints affecting key areas of concern in production operations, including production, pipeline transportation, gas storage facilities, and LNG receiving stations. This achieves a multi-objective, multi-cycle intelligent scheduling dynamic optimization model with the goals of safe and stable operation and ensuring production and operational efficiency. This allows for the rolling optimization of the daily natural gas scheduling plan for the statistical period to ensure its smooth implementation. It effectively solves the technical problems of manual scheduling, which typically only allows for next-day planning, cannot predict long-term daily plans, and cannot simultaneously consider pipeline safety and stability, production and operational efficiency, and various constraints, resulting in extremely low planning efficiency and poor accuracy. The natural gas daily scheduling optimization method provided by this invention can provide an overall balance direction for production operation command and improve the efficiency of preparing daily designated natural gas plans.

[0188] Furthermore, the natural gas dispatching daily plan optimization method of this invention takes into account multiple objectives, including stable operation of the natural gas pipeline network and production and operation benefits. It comprehensively considers important indicators affecting production and operation, such as production capacity, storage and transportation facility capacity, and sales capacity, including physical capacity constraints such as production, pipeline transportation, gas storage facilities, and LNG receiving stations. With the goal of safe and stable operation and ensuring production and operation benefits, a multi-objective, multi-period intelligent dispatching dynamic optimization model is constructed. This intelligent dispatching dynamic optimization model is used for rolling optimization of daily natural gas dispatching, achieving multi-period dynamic optimization of natural gas dispatching and ensuring the smooth implementation of monthly plans. It solves problems such as incomplete manual compilation, low efficiency, and short prediction cycles, improving computational efficiency and accuracy. It is comprehensive, has a fast solution speed, and a long prediction cycle. Adjustments to the plan only require recalculation by modifying parameters to achieve a balance between supply and demand, resulting in high computational efficiency and fully leveraging its corrective function to ensure the smooth implementation of monthly plans.

[0189] Based on the same inventive concept, this invention provides a natural gas dispatching daily plan optimization device. Since the principle of the problem solved by this optimization device is similar to the aforementioned natural gas dispatching daily plan optimization method, the implementation of this device can refer to the implementation of the aforementioned method, and the repeated parts will not be described again.

[0190] The natural gas dispatching daily plan optimization device provided in this embodiment of the invention refers to... Figure 4 As shown, it includes:

[0191] The acquisition module 41 is used to acquire the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period.

[0192] Module 42 is used to construct a natural gas daily dispatch optimization model, which has a first-stage objective function with the goal of maximizing benefits within the statistical period, a second-stage objective function with the goal of minimizing the daily balance of the pipeline network within the statistical period, and a set of preset constraints.

[0193] The calculation module 43 is used to input the dynamic parameter data within the statistical period into the optimization model for solving, and to obtain the first objective value of the first stage objective function, the second objective value of the second stage objective function, and the daily plan optimization results.

[0194] This invention also provides an electronic device. Figure 5 A schematic block diagram of the electronic device provided in an embodiment of the present invention is shown.

[0195] Reference Figure 5 As shown, the electronic device 500 provided in this embodiment of the invention includes a processor 501, a communication interface 502, a memory 503, and a communication bus 504. The processor 501, the communication interface 502, and the memory 503 communicate with each other through the communication bus 504. The memory 503 is used to store computer programs. When the processor 501 executes the program stored in the memory, it implements the natural gas scheduling daily plan optimization method as described above.

[0196] This invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the natural gas dispatching daily plan optimization method as described above.

[0197] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of the present invention.

[0198] According to embodiments disclosed in this invention, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0199] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0200] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for optimizing daily natural gas dispatch plans, characterized in that, include: Acquire the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period; A natural gas daily dispatch optimization model is constructed by using a first-stage objective function that maximizes benefits within a statistical period, a second-stage objective function that minimizes the daily balance of the pipeline network within a statistical period, and a set of preset constraints. The two-stage method is used to solve the natural gas daily dispatch optimization model; In the first stage, under the preset set of constraints, the dynamic parameter data is input into the first stage objective function for solving to obtain the first stage objective function value; In the second stage, the objective function value of the first stage is converted into constraints and added to the preset constraint set. The preset constraint set is then updated. Under the updated constraint set, the dynamic parameter data is input into the objective function of the second stage for solving, thereby obtaining the objective function value of the second stage and the daily plan optimization results. The objective function for the first stage is expressed as follows: ； Among them, z max To achieve the target operating efficiency of natural gas dispatch based on the daily plan optimization results, the sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost represent, in order, the daily natural gas sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost for the current day and future days within the statistical period. The objective function for the second stage is expressed as follows: ； Among them, z min The target pipeline daily balance volume for natural gas scheduling is determined based on the aforementioned daily plan optimization results. It is a slack variable. It is a penalty coefficient, and the pipeline air intake and pipeline air output represent the total daily air intake and total daily air output of the pipeline in the current day and future days within the statistical period, respectively; The constraint expression transformed from the objective function value in the first stage is as follows: ； in, It is a slack variable.

2. The method as described in claim 1, characterized in that, The preset set of constraints includes: A set of physical constraints; or The set of physical constraints and the set of business constraints.

3. The method as described in claim 2, characterized in that, For any day within the statistical period, including the current day and any future day, the set of physical constraints includes the following physical constraints: ， ， ， ， ， ， ， Minimum gas consumption ≤ User sales volume ≤ Maximum gas consumption Wherein, the node inflow and node outflow represent the inflow and outflow of gas at the nodes in the pipeline network, respectively; the nodes in the pipeline network represent gas stations; the end-of-period daily balance, the beginning-of-period daily balance, and the current-day daily balance represent the natural gas balance of the pipeline network at the end of any given day, at the end of the day before any given day, and on the given day; the end-of-period tank inventory, the beginning-of-period tank inventory, and the change in tank inventory on the given day represent the LNG station tank inventory at the end of any given day, at the end of the day before any given day, and the change in LNG station tank inventory on the given day; the gas source production volume represents the amount of natural gas produced by the oilfield; the imported gas volume represents the amount of natural gas imported into the pipeline network; the injection / production gas volume represents the injection / production gas volume of the gas storage facilities in the pipeline network; and the user sales volume represents the amount of natural gas consumed by the user.

4. The method as described in claim 2, characterized in that, For any day within the statistical period, including the current day and any future day, the set of business constraints includes the following business constraints: ， ， ， ， ， Wherein, "cumulative completion" represents the sum of daily scheduling completions up to the day before the day in the statistical period; "future day specification" represents the value of scheduling completions on the day in the statistical period; "plan" represents the scheduling plan for the statistical period; "daily pipeline inflow / outflow balance" and "total air intake" represent the daily inflow / outflow balance and total air intake of the pipeline on any given day, respectively; and "cumulative daily balance" and "average daily air intake within the cumulative period" represent the daily balance of the pipeline within the statistical period, including the day in the statistical period and the days before the day in the statistical period, respectively. The cumulative sum of quantities and the average daily gas intake, the shipping schedule refers to the shipping date of LNG transported by ship and the volume transported on the shipping date, the initial tank inventory, the ending tank inventory, the gasification volume, and the loading volume refer to the initial tank inventory, the ending tank inventory, the gas volume entering the pipeline section after gasification, and the gas volume loaded for sale on the shipping date, respectively, and the daily balance of the pipeline network, the gas intake of the pipeline network, the gas output of the pipeline network, and the gas consumption of the pipeline network refer to the balance of the pipeline network, the gas intake volume, the gas output volume, and the gas consumption required for the operation of the pipeline network on any given day, respectively.

5. The method as described in claim 2, characterized in that, Also includes: Enter each business constraint in the set of business constraints step by step; The daily natural gas scheduling optimization model is debugged based on the business constraints in the set of business constraints input step by step.

6. The method as described in claim 5, characterized in that, Based on the business constraints in the set of business constraints input step by step, the daily natural gas scheduling optimization model is debugged, including: For the input business constraints, determine whether there is a contradiction between the input business constraints and the previously input constraints; In the event of a contradiction, an error message is returned, and based on the error message, the input business constraints and / or the input constraints are debugged until there is no contradiction between the debugged constraints.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Retrieve the analysis dimensions specified by the user; The daily plan optimization results for the current day and future days within the statistical period are analyzed to obtain and display the analysis results for the specified dimensions.

8. A natural gas dispatching daily plan optimization device, characterized in that, include: The acquisition module is used to acquire the current initial data, overall scheduling plan, upper and lower limit data of constraints, current completed planned quantity data, and unit price data of different stages of procurement, storage, and transportation within the statistical period, as dynamic parameter data within the statistical period. The module is used to construct a daily natural gas dispatch optimization model, which includes a first-stage objective function that optimizes the maximization of benefits within a statistical period, a second-stage objective function that optimizes the minimum daily balance of the pipeline network within a statistical period, and a set of preset constraints. The calculation module is used to solve the natural gas daily scheduling optimization model using a two-stage method; In the first stage, under the preset set of constraints, the dynamic parameter data is input into the first stage objective function for solving to obtain the first stage objective function value; In the second stage, the objective function value of the first stage is converted into constraints and added to the preset constraint set. The preset constraint set is then updated. Under the updated constraint set, the dynamic parameter data is input into the second stage objective function for solving, yielding the second stage objective function value and the daily plan optimization result. The first stage objective function is as follows: ； Among them, z max To achieve the target operating efficiency of natural gas dispatch based on the daily plan optimization results, the sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost represent, in order, the daily natural gas sales revenue, production cost, trading cost, pipeline transportation cost, and gas storage injection and production cost for the current day and future days within the statistical period. The objective function for the second stage is expressed as follows: ； Among them, z min The target pipeline daily balance volume for natural gas scheduling is determined based on the aforementioned daily plan optimization results. It is a slack variable. It is a penalty coefficient, and the pipeline air intake and pipeline air output represent the total daily air intake and total daily air output of the pipeline in the current day and future days within the statistical period, respectively; The constraint expression transformed from the objective function value in the first stage is as follows: ； in, It is a slack variable.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-7.

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