Natural gas pipeline network scheduling method, device, equipment, storage medium and product

Abstract

The invention discloses a natural gas pipeline network scheduling method, device and equipment, a storage medium and a product. The method comprises the following steps: acquiring a pipeline network gas transmission task, an initial operation state, an initial operation scheme and a pipeline network simulation model; inputting the pipe network gas transmission task, the initial operation state and the initial operation scheme into a pipe network simulation model, and obtaining first predicted operation data output by the pipe network simulation model; and analyzing the first predicted operation data according to a preset evaluation index, and if an abnormal working condition exists in the first predicted operation data, optimizing the initial operation scheme to obtain a target operation scheme. According to the natural gas pipeline network scheduling method disclosed by the invention, prediction is carried out by utilizing the pipeline network simulation model, potential problems can be found in time, and the current operation scheme is optimized to eliminate potential abnormal working conditions, so that automatic and intelligent scheduling of the pipeline network is realized, and the reliability and efficiency of overall operation are improved; and stable operation of the natural gas conveying system under various uncertain factors is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling optimization, and in particular to a natural gas pipeline network scheduling method, device, equipment, storage medium and product. Background Art

[0002] In the context of global carbon emission reduction, natural gas is considered to be an ideal transition fuel for the transition to a green energy structure due to its clean and efficient characteristics, and its demand is growing year by year. Due to the huge differences in the geographical distribution of natural gas reserves, the widespread use of natural gas is inseparable from a huge transportation system, among which low-cost and high-safety pipeline transportation accounts for 93% of the total global natural gas consumption. After building a huge natural gas pipeline network, full utilization and efficient operation of the pipeline network is a major challenge. In addition, due to the instability of gas source production, the volatility of user gas consumption, and the uncertainty of equipment failure, pipelines need to assume transportation and storage functions to cope with short-term uncertainties, and need to further coordinate gas storage, LNG satellite stations and other peak-shaving facilities to jointly cope with larger operational fluctuations. This also puts higher requirements on the operation and scheduling of the natural gas pipeline network, which is obviously inseparable from the guarantee of efficient and advanced natural gas pipeline operation and scheduling optimization technology.

[0003] However, in the current pipeline network operation and dispatching, manual experience is still the most important basis for dispatching operations, and how to adjust and how much to adjust depends on manual subjectivity. However, in the face of such a large and complex natural gas pipeline network system, it is difficult for humans to fully consider the various components and influencing factors in the pipeline network system and propose the best operation plan. Often, it solves the current problem and causes new problems. Through long-term continuous corrections, it is ensured that the operation status of the pipeline network fluctuates within a reasonable operating range. This will obviously lead to huge unreliability in the operation of the pipeline network and make it difficult to operate safely and efficiently. Summary of the invention

[0004] The present invention provides a natural gas pipeline network scheduling method, device, equipment, storage medium and product to achieve optimized scheduling of the natural gas pipeline network.

[0005] According to one aspect of the present invention, a natural gas pipeline network scheduling method is provided, comprising:

[0006] Obtain the pipeline network's gas transmission tasks, initial operation status, initial operation plan and pipeline network simulation model;

[0007] Inputting the pipeline network gas transmission task, the initial operation state and the initial operation plan into the pipeline network simulation model, and obtaining first predicted operation data output by the pipeline network simulation model;

[0008] Analyze the first predicted operation data according to the preset evaluation indicators. If there are abnormal operating conditions in the first predicted operation data, optimize the initial operation plan to obtain the target operation plan.

[0009] Further, before obtaining the pipeline network gas transmission task, the initial operating state, the initial operation plan, and the pipeline network simulation model, it also includes:

[0010] Obtain the pipeline system data and establish the pipeline network simulation model according to the pipeline system data.

[0011] Further, the preset evaluation indicators include the gas source uploading pressure, the user downloading pressure, and the pipeline section gas inventory. Analyzing the first predicted operation data according to the preset evaluation indicators includes:

[0012] Determine the gas source uploading pressure value, the user downloading pressure value, and the pipeline section gas inventory value corresponding to the first predicted operation data;

[0013] If the gas source uploading pressure value, the user downloading pressure value, and the pipeline section gas inventory value all meet the corresponding threshold requirements, determine that there are no abnormal operating conditions in the first predicted operation data; otherwise, determine that there are abnormal operating conditions in the first predicted operation data.

[0014] Further, optimizing the initial operation plan includes:

[0015] Obtain the scheduling optimization model and use the initial operation plan as the decision variable of the scheduling optimization model;

[0016] Determine the current optimization goal and the current constraint conditions according to the current scheduling conditions;

[0017] Adjust the initial operation plan until both the current optimization goal and the current constraint conditions meet the corresponding requirements, and use the adjusted initial operation plan as the target operation plan.

[0018] Further, after using the adjusted initial operation plan as the target operation plan, it also includes:

[0019] Input the pipeline network gas transmission task, the initial operating state, and the target operation plan into the pipeline network simulation model, obtain the second predicted operation data output by the pipeline network simulation model, and optimize the target operation plan according to the second predicted operation data.

[0020] Further, optimizing the target operation plan according to the second predicted operation data includes:

[0021] Determine whether the second predicted operation data meets the current optimization objective and the current constraint conditions. If not, re-optimize the target operation plan until the set convergence condition is met.

[0022] According to another aspect of the present invention, there is provided a natural gas pipeline network scheduling device, including:

[0023] A pipeline network gas transmission task, initial operation state, initial operation plan, and pipeline network simulation model acquisition module, configured to acquire a pipeline network gas transmission task, an initial operation state, an initial operation plan, and a pipeline network simulation model;

[0024] A first predicted operation data acquisition module, configured to input the pipeline network gas transmission task, the initial operation state, and the initial operation plan into the pipeline network simulation model, and acquire first predicted operation data output by the pipeline network simulation model;

[0025] A target operation plan determination module, configured to analyze the first predicted operation data according to a preset evaluation index. If there are abnormal working conditions in the first predicted operation data, optimize the initial operation plan to obtain a target operation plan.

[0026] Optionally, the device further includes a pipeline network simulation model establishment module, configured to acquire pipeline system data and establish the pipeline network simulation model according to the pipeline system data.

[0027] Optionally, the preset evaluation index includes the gas source uploading pressure, the user downloading pressure, and the pipeline segment inventory. The target operation plan determination module is further configured to:

[0028] Determine the gas source uploading pressure value, the user downloading pressure value, and the pipeline segment inventory value corresponding to the first predicted operation data;

[0029] If the gas source uploading pressure value, the user downloading pressure value, and the pipeline segment inventory value all meet the corresponding threshold requirements, determine that there are no abnormal working conditions in the first predicted operation data; otherwise, determine that there are abnormal working conditions in the first predicted operation data.

[0030] Optionally, the target operation plan determination module is further configured to:

[0031] Acquire a scheduling optimization model, and use the initial operation plan as a decision variable of the scheduling optimization model;

[0032] Determine the current optimization objective and the current constraint conditions according to the current scheduling working conditions;

[0033] Adjust the initial operation plan until the current optimization objective and the current constraint conditions both meet the corresponding requirements, and use the adjusted initial operation plan as the target operation plan.

[0034] Optionally, the device further includes a target operation plan optimization module, configured to input the gas pipeline transportation task, the initial operation state, and the target operation plan into the gas pipeline simulation model, obtain second predicted operation data output by the gas pipeline simulation model, and optimize the target operation plan according to the second predicted operation data.

[0035] Optionally, the target operation plan optimization module is further configured to:

[0036] Determine whether the second predicted operation data meets the current optimization objective and the current constraint conditions. If not, re-optimize the target operation plan until the set convergence condition is met.

[0037] According to another aspect of the present invention, there is provided an electronic device, including:

[0038] At least one processor; and

[0039] A memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the natural gas pipeline scheduling method according to any embodiment of the present invention.

[0041] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the natural gas pipeline scheduling method according to any embodiment of the present invention when executed.

[0042] According to another aspect of the present invention, there is provided a computer program product including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps of the natural gas pipeline scheduling method according to any embodiment of the present invention are implemented.

[0043] The natural gas pipeline network scheduling method disclosed by the present invention first obtains the gas transmission tasks of the pipeline network, the initial operating state, the initial operating plan, and the pipeline network simulation model; then inputs the gas transmission tasks of the pipeline network, the initial operating state, and the initial operating plan into the pipeline network simulation model to obtain the first predicted operating data output by the pipeline network simulation model; finally, analyzes the first predicted operating data according to the preset evaluation index. If there are abnormal working conditions in the first predicted operating data, the initial operating plan is optimized to obtain the target operating plan. The natural gas pipeline network scheduling method disclosed by the present invention can timely discover potential problems through prediction using the pipeline network simulation model, and eliminate potential abnormal working conditions by optimizing the current operating plan, thereby realizing the automatic and intelligent scheduling of the pipeline network, improving the reliability and efficiency of the overall operation, and ensuring the stable operation of the natural gas transmission system under various uncertain factors.

[0044] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Brief Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 is a flowchart of a natural gas pipeline network scheduling method provided in Embodiment 1 of the present invention;

[0047] Figure 2 is a flowchart of a natural gas pipeline network scheduling method provided in Embodiment 2 of the present invention;

[0048] Figure 3 is a schematic structural diagram of a natural gas pipeline network scheduling device provided in Embodiment 3 of the present invention;

[0049] Figure 4 is a schematic structural diagram of an electronic device for implementing the natural gas pipeline network scheduling method of Embodiment 4 of the present invention. Detailed Embodiments

[0050] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0052] Embodiment 1

[0053] Figure 1 It is a flowchart of a natural gas pipeline network scheduling method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of scheduling a natural gas pipeline network. This method can be executed by a natural gas pipeline network scheduling device, which can be implemented in the form of hardware and / or software, and the natural gas pipeline network scheduling device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0054] S110. Obtain the gas transmission task of the pipeline network, the initial operating state, the initial operating plan, and the pipeline network simulation model.

[0055] Among them, the operating plan is the operating plan of the stations along the pipeline and the equipment in the stations. For example, for a compressor station, the operating plan includes the on / off state of the compressor and the compression ratio of the compressor at different times within a period of time. For a gas storage reservoir, the operating plan includes the injection volume and production volume at different times within a period of time. The initial operating plan is the operating plan set before the scheduling optimization. The gas transmission task of the pipeline network is the gas consumption demand of users and the gas production volume of gas sources at different times within a period of time. The pipeline network simulation model is a simulation calculation model established for the natural gas pipeline network currently undergoing optimized scheduling, which includes a pipeline network structure model, a state equation describing the thermodynamic properties of the fluid, a continuity equation, a momentum equation, and an energy equation describing the change of fluid physical property parameters, as well as calculation equations for the equipment in the stations along the line.

[0056] In this embodiment, the gas transmission task of the pipeline network and the initial operation plan are manually set data. The initial operation state is the state of the natural gas pipeline network to be optimized before the optimization scheduling, and the pipeline network simulation model is pre-established according to the pipeline system data of the natural gas pipeline network to be optimized currently.

[0057] S120. Input the gas transmission task of the pipeline network, the initial operation state and the initial operation plan into the pipeline network simulation model to obtain the first predicted operation data output by the pipeline network simulation model.

[0058] Among them, the first predicted operation data is the simulation result of the pipeline network simulation model, that is, the operation conditions predicted by the model for a period of time in the future, including the upstream loading pressure of the gas source, the downstream unloading pressure of users, the pressure, flow rate and temperature data of the stations along the pipeline, and the operation parameters such as the inventory in the pipe section at different moments in the future.

[0059] In this embodiment, after obtaining the gas transmission task of the pipeline network, the initial operation state, the initial operation plan and the pipeline network simulation model, the pipeline network simulation model can be used for simulation calculation. When using the pipeline network simulation model for simulation calculation, it is necessary to set the parameters of the pipeline network simulation model, including the time length of the simulation prediction and the time interval between two adjacent simulation predictions, and it is also necessary to set the initial conditions and the dynamic boundary conditions for a period of time. Among them, the initial conditions are the initial pressure distribution and initial flow rate of each node and pipe section in the pipeline network at the start of the simulation, and the dynamic boundary conditions are the gas transmission task and operation plan of the pipeline network for a period of time in the future. Input the obtained gas transmission task of the pipeline network and the initial operation plan as the dynamic boundary conditions into the pipeline network simulation model to predict the operation conditions of the pipeline network for a period of time in the future, and the first predicted operation data can be obtained.

[0060] Preferably, the real-time operation data of the pipeline network collected can be used as the initial conditions of the pipeline network simulation model and as the starting point of the simulation calculation to conform to the real-time operation situation of the pipeline network. Among them, the real-time operation data includes the production volume and upstream loading pressure of the gas source, the demand volume and downstream unloading pressure of users, the pressure, flow rate and temperature data of the stations along the pipeline, and the operation data of the stations and in-station equipment along the pipeline.

[0061] S130. Analyze the first predicted operation data according to the preset evaluation index. If there are abnormal working conditions in the first predicted operation data, optimize the initial operation plan to obtain the target operation plan.

[0062] In this embodiment, after obtaining the first predicted operation data, it can be analyzed according to the specific values of each parameter therein to judge whether each parameter is within the normal range. If there is abnormal data, it is judged that there are abnormal working conditions in the first predicted operation data, and the initial operation plan needs to be optimized.

[0063] Optionally, the optimization of the initial operation plan can be to establish a scheduling optimization model based on the natural gas pipeline network operation status evaluation index system, adopt optimization algorithms such as genetic algorithms or particle swarm algorithms to solve the optimal scheduling operation, and eliminate possible abnormal operating conditions in the future by optimizing and adjusting the initial operation plan. If the future operation parameters of the pipeline network can meet the requirements of different indicators and corresponding thresholds in the natural gas pipeline network operation status evaluation index system through optimizing and adjusting the initial operation plan, it indicates that the scheduling optimization adjustment is successful, and this simulation prediction will end. Wait until a preset time interval is met or a condition triggered manually, and then conduct the next simulation prediction.

[0064] Furthermore, before obtaining the gas transmission task, initial operation status, initial operation plan, and pipeline network simulation model of the pipeline network, it is also possible to: obtain pipeline system data and establish a pipeline network simulation model based on the pipeline system data.

[0065] In this embodiment, it is necessary to establish a pipeline network simulation model before performing simulation calculations using the pipeline network simulation model. When establishing the pipeline network simulation model, it is first necessary to collect pipeline system data. Among them, the natural gas pipeline system data includes basic parameters such as pipeline length, diameter, elevation, and topological structure, and also includes historical operation data, functions and equipment configuration parameters of stations along the line, gas usage requirements and historical demand data of users along the line, production capacity and historical production data of gas sources along the line. Among them, the stations along the line include compressor stations, gas distribution stations, and gas storage facilities. After obtaining the pipeline system data, a pipeline network simulation model can be established, and typical operating conditions of the pipeline network can be established using historical data. By comparing historical data and simulation results, the calculation accuracy of the simulation model can be verified, and parameter settings can be adjusted to improve the accuracy. Among them, the pipeline network simulation model includes a pipeline network structure model, an equation of state describing the thermodynamic properties of the fluid, a continuity equation, a momentum equation, and an energy equation describing the change of fluid physical property parameters, and calculation equations of equipment in stations along the line.

[0066] The natural gas pipeline network scheduling method disclosed in the present invention first obtains the gas transmission task, initial operation status, initial operation plan, and pipeline network simulation model of the pipeline network, then inputs the gas transmission task, initial operation status, and initial operation plan into the pipeline network simulation model to obtain the first predicted operation data output by the pipeline network simulation model, and finally analyzes the first predicted operation data according to preset evaluation indicators. If there are abnormal operating conditions in the first predicted operation data, the initial operation plan is optimized to obtain the target operation plan. The natural gas pipeline network scheduling method disclosed in the present invention can timely discover potential problems through prediction using the pipeline network simulation model, and eliminate potential abnormal operating conditions by optimizing the current operation plan, thereby realizing the automated and intelligent scheduling of the pipeline network, improving the reliability and efficiency of the overall operation, and ensuring the stable operation of the natural gas transmission system under various uncertain factors.

[0067] Embodiment 2

[0068] Figure 2 This is a flowchart of a natural gas pipeline network scheduling method provided in the second embodiment of the present invention. This embodiment is a refinement of the above embodiment. As Figure 2 shown, the method includes:

[0069] S210. Obtain the gas transmission task of the pipeline network, the initial operating state, the initial operating plan, and the pipeline network simulation model.

[0070] Among them, the operating plan is the operating plan of the stations along the pipeline and the equipment in the stations. For example, for a compressor station, the operating plan includes the on-off state of the compressor and the compression ratio of the compressor at different times within a period of time. For a gas storage reservoir, the operating plan includes the gas injection volume and gas production volume at different times within a period of time. The initial operating plan is the operating plan set before the scheduling optimization. The gas transmission task of the pipeline network is the gas demand of users and the gas production volume of the gas source at different times within a period of time. The pipeline network simulation model is a simulation calculation model established for the natural gas pipeline network currently undergoing optimization scheduling, which includes a pipeline network structure model, an equation of state describing the thermodynamic properties of the fluid, a continuity equation, a momentum equation, an energy equation describing the change of fluid physical property parameters, and calculation equations of the equipment in the stations along the line.

[0071] In this embodiment, the gas transmission task of the pipeline network and the initial operating plan are manually set data. The initial operating state is the state of the natural gas pipeline network currently undergoing optimization scheduling before the optimization scheduling. The pipeline network simulation model is pre-established according to the pipeline system data of the natural gas pipeline network currently undergoing optimization scheduling.

[0072] S220. Input the gas transmission task of the pipeline network, the initial operating state, and the initial operating plan into the pipeline network simulation model, and obtain the first predicted operating data output by the pipeline network simulation model.

[0073] Among them, the first predicted operating data is the simulation result of the pipeline network simulation model, that is, the operating conditions predicted by the model in the future period of time, including the upstream loading pressure of the gas source, the downstream unloading pressure of users, the pressure, flow rate, and temperature data of the stations along the pipeline, and operating parameters such as the pipe inventory of the pipe section at different times in the future period of time.

[0074] In this embodiment, after obtaining the gas transmission task of the pipeline network, the initial operating state, the initial operation plan, and the pipeline network simulation model, the pipeline network simulation model can be used for simulation calculation. When using the pipeline network simulation model for simulation calculation, it is necessary to set the parameters of the pipeline network simulation model, including the time length of the simulation prediction and the time interval between two adjacent simulation predictions. It is also necessary to set the initial conditions and the dynamic boundary conditions for a period of time. Among them, the initial conditions are the initial pressure distribution and initial flow rate of each node and pipe segment in the pipeline network at the beginning of the simulation, and the dynamic boundary conditions are the gas transmission task and operation plan of the pipeline network for a period of time in the future. The obtained gas transmission task and initial operation plan of the pipeline network are used as dynamic boundary conditions and input into the pipeline network simulation model to predict the operation status of the pipeline network for a period of time in the future, and the first predicted operation data can be obtained.

[0075] S230. Determine the gas source uploading pressure value, user downloading pressure value, and pipe segment inventory value corresponding to the first predicted operation data. If the gas source uploading pressure value, user downloading pressure value, and pipe segment inventory value all meet the corresponding threshold requirements, it is determined that there is no abnormal condition in the first predicted operation data; otherwise, it is determined that there is an abnormal condition in the first predicted operation data.

[0076] In this embodiment, to analyze the first predicted operation data, an evaluation index system for the operating state of the natural gas pipeline network can be established in advance to judge and identify abnormal condition points in the predicted future operation data. The evaluation index system for the operating state of the natural gas pipeline network consists of preset evaluation indexes, including but not limited to the gas source uploading pressure, user downloading pressure, and pipe segment inventory. According to the evaluation index system for the operating state of the natural gas pipeline network, it is determined whether the first predicted operation data meets the threshold requirements corresponding to each preset evaluation index, so as to determine whether there is an abnormal condition in the first predicted operation data.

[0077] Optionally, when analyzing the first predicted operation data, each parameter in the first predicted operation data can be used as an evaluation object, and the evaluation can be carried out using the evaluation index system for the operation state of the natural gas pipeline network. Each evaluation index is compared and analyzed one by one to determine whether the pipeline network operation parameters at different times in the future meet the threshold requirements. If each parameter in the first predicted operation data (including but not limited to the gas source upload pressure, user download pressure, and pipe segment gas inventory) meets the requirements of different indicators and corresponding thresholds in the evaluation index system for the operation state of the natural gas pipeline network, it indicates that there are no abnormal conditions violating the evaluation indicators in the future period of time, and there are no abnormal conditions in the first predicted operation data. This simulation prediction will end, and wait until a preset time interval is met or a condition triggered manually, and then the next simulation prediction will be carried out. Among them, the time interval is the time length between two adjacent simulation prediction calculations set by humans, and manual trigger means manually clicking to start the simulation prediction function. If at least one parameter among the parameters in the first predicted operation data does not meet the requirements of different indicators and corresponding thresholds in the evaluation index system for the operation state of the natural gas pipeline network, it indicates that there is one or more abnormal conditions violating the evaluation indicators in the future period of time, and there are abnormal conditions in the first predicted operation data, and further dispatching optimization will be carried out.

[0078] S240. If there are abnormal conditions in the first predicted operation data, obtain the dispatching optimization model and use the initial operation plan as the decision variable of the dispatching optimization model.

[0079] Among them, the dispatching optimization model is a mathematical model that uses optimization algorithms such as genetic algorithms or particle swarm algorithms to solve the best dispatching operation and find the optimal operation plan.

[0080] In this embodiment, if there are abnormal conditions in the first predicted operation data, the dispatching optimization model can be used, and optimization algorithms such as genetic algorithms or particle swarm algorithms can be used to solve the best dispatching operation, and the initial operation plan can be optimized and adjusted to eliminate possible abnormal conditions in the future. When optimizing the initial operation plan, the initial operation plan can be used as the decision variable in the dispatching optimization model, that is, the parameter object that is changed and adjusted during the optimization search.

[0081] S250. Determine the current optimization goal and current constraint conditions according to the current dispatching condition, adjust the initial operation plan until both the current optimization goal and the current constraint conditions meet the corresponding requirements, and use the adjusted initial operation plan as the target operation plan.

[0082] Among them, the current dispatching conditions include dispatching conditions such as emergency operation adjustment, operation plan adjustment, and operation optimization adjustment. The current optimization objective is the optimization objective of the dispatching optimization model that matches the current dispatching conditions. For example, for operation optimization adjustment, more attention is paid to the operation cost of the pipeline network system, and the minimum overall operation energy consumption of the pipeline network system can be used as the current optimization objective. The current constraint conditions are the corresponding constraint conditions established based on the data of the natural gas pipeline system and the evaluation index system of the operation status of the natural gas pipeline network.

[0083] Preferably, to improve the performance of the dispatching optimization model, the current constraint conditions can be processed by adding a penalty function to the current optimization objective, so as to ensure that the future operation parameters of the pipeline network meet the requirements such as pressure, flow rate, and pipeline inventory.

[0084] In this embodiment, to optimize the initial operation plan, an appropriate optimization algorithm can be selected according to the solution characteristics of the dispatching optimization model for solving the optimization model, and the optimized operation plan can be obtained until both the current optimization objective and the current constraint conditions meet the corresponding requirements, and the result after adjusting the initial operation plan is used as the target operation plan.

[0085] Furthermore, after taking the adjusted initial operation plan as the target operation plan, the following can also be done:

[0086] Input the gas transmission task of the pipeline network, the initial operation status, and the target operation plan into the pipeline network simulation model to obtain the second predicted operation data output by the pipeline network simulation model, and optimize the target operation plan according to the second predicted operation data.

[0087] In this embodiment, after optimizing the initial operation plan to obtain the target operation plan, the optimized target operation plan and the gas transmission task of the pipeline network can be input into the pipeline network simulation model again, so that the pipeline network simulation model performs simulation calculations again to predict the operation status of the pipeline network for a period of time in the future, and the second predicted operation data is obtained. And judge whether the optimized target operation plan can meet the requirements of optimized dispatching according to the specific parameter data of the second predicted operation data.

[0088] Optionally, the method for optimizing the target operation plan according to the second predicted operation data can be: judge whether the second predicted operation data meets the current optimization objective and the current constraint conditions. If not, re-optimize the target operation plan until the set convergence condition is met.

[0089] Specifically, according to the second predicted operation data output by the pipeline network simulation model, the operation plan in the dynamic boundary conditions required for the simulation calculation can be replaced with the optimized target operation plan to obtain the second predicted operation data. If the second predicted operation data does not meet the current optimization objective and the current constraint conditions, the target operation plan is re-optimized. By combining the scheduling optimization model of the natural gas pipeline network and the pipeline network simulation model, alternately performing optimization adjustment and simulation calculation, and continuously iterating, the adjusted optimization plan can not only meet the requirements of the constraint conditions but also obtain a better optimization objective value.

[0090] Preferably, the relative deviation of the optimization objective values corresponding to the target operation plans after two adjacent optimizations can be set to be less than 1% as the final convergence condition. When the operation plans after two adjacent optimization adjustments meet the convergence condition, the alternate calculation is stopped, and the latest optimized operation plan is obtained as the final operation plan that can eliminate possible abnormal working conditions in the future.

[0091] The natural gas pipeline network scheduling method provided by the embodiments of the present invention can timely discover potential problems by using the pipeline network simulation model for prediction, and eliminate potential abnormal working conditions by optimizing the current operation plan, thereby realizing the automatic and intelligent scheduling of the pipeline network, improving the reliability and efficiency of the overall operation, and ensuring the stable operation of the natural gas transmission system under various uncertain factors.

[0092] Embodiment III

[0093] Figure 3 FIG. is a structural schematic diagram of a natural gas pipeline network scheduling device provided by Embodiment III of the present invention. As Figure 3 shown, the device includes: a pipeline network gas transmission task, an initial operation state, an initial operation plan, and a pipeline network simulation model acquisition module 310, a first predicted operation data acquisition module 320, and a target operation plan determination module 330.

[0094] The pipeline network gas transmission task, the initial operation state, the initial operation plan, and the pipeline network simulation model acquisition module 310 are used to acquire the pipeline network gas transmission task, the initial operation state, the initial operation plan, and the pipeline network simulation model.

[0095] The first predicted operation data acquisition module 320 is used to input the pipeline network gas transmission task, the initial operation state, and the initial operation plan into the pipeline network simulation model to acquire the first predicted operation data output by the pipeline network simulation model.

[0096] The target operation plan determination module 330 is used to analyze the first predicted operation data according to a preset evaluation index. If there are abnormal working conditions in the first predicted operation data, the initial operation plan is optimized to obtain the target operation plan.

[0097] Optionally, the device further includes a pipeline network simulation model establishment module, configured to obtain pipeline system data and establish the pipeline network simulation model according to the pipeline system data.

[0098] Optionally, the preset evaluation indicators include the gas source uploading pressure, the user downloading pressure, and the pipeline segment inventory. The target operation plan determination module 330 is further configured to:

[0099] Determine the gas source uploading pressure value, the user downloading pressure value, and the pipeline segment inventory value corresponding to the first predicted operation data; if the gas source uploading pressure value, the user downloading pressure value, and the pipeline segment inventory value all meet the corresponding threshold requirements, determine that there is no abnormal condition in the first predicted operation data, otherwise determine that there is an abnormal condition in the first predicted operation data.

[0100] Optionally, the target operation plan determination module 330 is further configured to:

[0101] Obtain a scheduling optimization model, and use the initial operation plan as the decision variable of the scheduling optimization model; determine the current optimization objective and the current constraint conditions according to the current scheduling condition; adjust the initial operation plan until both the current optimization objective and the current constraint conditions meet the corresponding requirements, and use the adjusted initial operation plan as the target operation plan.

[0102] Optionally, the device further includes a target operation plan optimization module, configured to input the pipeline network gas transmission task, the initial operation state, and the target operation plan into the pipeline network simulation model, obtain the second predicted operation data output by the pipeline network simulation model, and optimize the target operation plan according to the second predicted operation data.

[0103] Optionally, the target operation plan optimization module is further configured to:

[0104] Judge whether the second predicted operation data meets the current optimization objective and the current constraint conditions. If not, re-optimize the target operation plan until the set convergence condition is met.

[0105] The natural gas pipeline network scheduling device provided by the embodiments of the present invention can execute the natural gas pipeline network scheduling method provided by any embodiment of the present invention, and has the corresponding function modules and beneficial effects for executing the method.

[0106] Embodiment 4

[0107] Figure 4The schematic structural diagram of the electronic device 10 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0108] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0109] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0110] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the natural gas pipeline network scheduling method.

[0111] In some embodiments, the natural gas pipeline network scheduling method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the natural gas pipeline network scheduling described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the natural gas pipeline network scheduling method by any other suitable means (e.g., by means of firmware).

[0112] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0116] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is generated by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

Claims

1. A natural gas pipeline network scheduling method, characterized in that: include: Obtain the pipeline network's gas transmission tasks, initial operation status, initial operation plan and pipeline network simulation model; Inputting the pipeline network gas transmission task, the initial operation state and the initial operation plan into the pipeline network simulation model, and obtaining first predicted operation data output by the pipeline network simulation model; The first predicted operation data is analyzed according to preset evaluation indicators, and if abnormal operating conditions exist in the first predicted operation data, the initial operation plan is optimized to obtain a target operation plan.

2. The method according to claim 1, characterized in that Before obtaining the pipeline network gas transmission task, initial operation status, initial operation plan and pipeline network simulation model, it also includes: The pipeline system data is acquired, and the pipeline network simulation model is established according to the pipeline system data.

3. The method according to claim 1, characterized in that The preset evaluation indicators include gas source upload pressure, user download pressure and pipeline section inventory. The first predicted operation data is analyzed according to the preset evaluation indicators, including: Determine the gas source upload pressure value, the user download pressure value and the pipeline section inventory value corresponding to the first predicted operation data; If the gas source upload pressure value, the user download pressure value and the pipe section inventory value all meet the corresponding threshold requirements, it is determined that there is no abnormal operating condition in the first predicted operation data, otherwise it is determined that there is an abnormal operating condition in the first predicted operation data.

4. The method according to claim 1, characterized in that: Optimizing the initial operation plan includes: Acquire a scheduling optimization model, and use the initial operation plan as a decision variable of the scheduling optimization model; Determine the current optimization goal and current constraints based on the current scheduling conditions; The initial operation plan is adjusted until the current optimization objective and the current constraint conditions both meet corresponding requirements, and the adjusted initial operation plan is used as the target operation plan.

5. The method according to claim 4, characterized in that After the adjusted initial operation plan is used as the target operation plan, the method further includes: The pipeline network gas transmission task, the initial operation state and the target operation plan are input into the pipeline network simulation model, second predicted operation data output by the pipeline network simulation model is obtained, and the target operation plan is optimized according to the second predicted operation data.

6. The method according to claim 5, characterized in that Optimizing the target operation plan according to the second predicted operation data includes: It is determined whether the second predicted operation data satisfies the current optimization target and the current constraint condition. If not, the target operation plan is optimized again until the set convergence condition is met.

7. A natural gas pipeline network dispatching device, characterized in that: include: A module for acquiring pipeline network gas transmission tasks, initial operation status, initial operation plan and pipeline network simulation model, which is used to acquire pipeline network gas transmission tasks, initial operation status, initial operation plan and pipeline network simulation model; A first predicted operation data acquisition module, used for inputting the pipeline network gas transmission task, the initial operation state and the initial operation plan into the pipeline network simulation model, and acquiring first predicted operation data output by the pipeline network simulation model; The target operation plan determination module is used to analyze the first predicted operation data according to preset evaluation indicators, and if there are abnormal operating conditions in the first predicted operation data, optimize the initial operation plan to obtain a target operation plan.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the natural gas pipeline network scheduling method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the natural gas pipeline network scheduling method according to any one of claims 1 to 6 when executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the natural gas pipeline network scheduling method according to any one of claims 1 to 6 are implemented.

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