A natural gas pipeline network emergency dispatching optimization method, device and equipment

Abstract

The embodiment of the specification relates to the technical field of data processing, in particular to a natural gas pipeline network emergency dispatch optimization method, device and equipment. Data in a natural gas pipeline network and natural gas demand and actual supply of each user in the pipeline network are acquired, then user satisfaction of each user is judged, and user satisfaction variance is minimized as much as possible; then a natural gas pipeline network emergency dispatch optimization model is constructed, so that a natural gas pipeline network emergency dispatch optimization scheme is quickly and efficiently formulated; and an optimal solution set of optimal user satisfaction variance and overall user satisfaction is acquired through the optimization model, and summary data is generated for display. On the basis of being able to distinguish different categories of users, two indexes of minimum individual user satisfaction variance and maximum overall user satisfaction are taken as targets, so that user categories can be identified to meet different user demands, and the method is not limited by the amount of pressure-reduced natural gas, and can meet various complex emergency dispatch scenarios.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of data processing technology, and in particular to a method, apparatus and equipment for optimizing emergency dispatching of natural gas pipeline networks. Background Technology

[0002] Currently, natural gas is becoming a leading trend in the global energy transition, highlighting the critical importance and future development potential of natural gas pipeline systems. However, with the increasing frequency of global instability and extreme weather events, the scenarios for natural gas dispatch optimization have become more complex, necessitating the adoption of new emergency dispatch optimization methods for natural gas pipeline networks. Furthermore, the dispatching process for natural gas pipeline network operation largely relies on human experience, allocating natural gas through dispatchers' expertise. However, this approach often results in problems such as high energy consumption at compressor stations and low flow allocation efficiency.

[0003] Currently, there are two technical solutions to this problem. The first is the average reduction method, which reduces the natural gas supply to all customers in the affected area on an average basis. The second is to optimize the emergency dispatch of natural gas pipelines based on heuristic algorithms. This method uses iterative calculations to differentiate the reduction based on the importance of customers in the affected area. However, the average reduction method cannot distinguish the importance of customers in the affected area. For example, residential and hospital users should be considered more important than commercial and industrial users. While the average reduction method avoids the risk of supply interruption by reducing the supply to all users on an average basis, it suffers from insufficient supply to important users. On the other hand, the heuristic algorithm-based emergency dispatch optimization method limits the reduction amount for each user. When the maximum reduction amount is less than the amount of natural gas loss caused by the emergency, the algorithm cannot solve the problem.

[0004] Therefore, there is an urgent need for an optimization method for emergency dispatching of natural gas pipeline networks, which can be used to address different scenarios. Summary of the Invention

[0005] To address the problem that existing technologies cannot distinguish the different importance of customers within an affected area, embodiments of this specification provide a method, apparatus, and equipment for optimizing emergency dispatching of natural gas pipeline networks. This enables the identification of different user categories to meet the needs of different users, while also being unrestricted by the amount of natural gas that can be reduced, thus satisfying various complex emergency dispatching scenarios.

[0006] The specific technical solutions of the embodiments in this specification are as follows:

[0007] On the one hand, the embodiments of this specification provide a method for optimizing emergency dispatch of natural gas pipeline networks, including:

[0008] Acquire natural gas pipeline network data, as well as the natural gas demand and actual supply for each user, and determine the user satisfaction for each user based on the natural gas demand and actual supply.

[0009] An emergency dispatch optimization model for the natural gas pipeline network is constructed based on the natural gas pipeline network data and the user satisfaction data.

[0010] The emergency dispatch optimization model of the natural gas pipeline network is iteratively solved using the multi-objective particle swarm optimization algorithm to obtain the optimal user satisfaction variance and the Pareto optimal solution set of overall user satisfaction.

[0011] Save the optimal Pareto solution set of the optimal user satisfaction variance and the overall user satisfaction, and generate summary data for display.

[0012] Furthermore, determining each user's satisfaction based on the stated natural gas demand and actual supply further includes,

[0013] The constraint on user satisfaction is,

[0014]

[0015] Among them, Q iSUP Q represents the actual natural gas supply to user node i. iDEM Let represent the natural gas demand of user node i.

[0016] Furthermore, natural gas pipeline network data includes at least:

[0017] The gas supply volume of each natural gas source, the connecting stations, the mileage of each pipeline segment, the pipeline to which it belongs, the transport volume of the pipeline segment, the starting station, and the ending station.

[0018] Furthermore, the natural gas pipeline network data further includes,

[0019] Set the pipeline segment transportation capacity limit based on the pipeline segment transportation volume:

[0020]

[0021] Where i represents the starting station of the pipeline segment, j represents the ending station of the pipeline segment, and f ij This represents the pipeline throughput of segment (i,j). This represents the maximum transport capacity of pipe segment (i,j). This represents the minimum pipeline capacity for pipe segment (i,j);

[0022] Based on the starting and ending stations, set station entry and exit balance constraints:

[0023]

[0024] Where s represents any station; This indicates the production capacity of the gas source g connected to station s; This represents the sales volume of customer c connected to station s; This indicates the gas delivery volume of the intake pipe section connected to station s; This indicates the gas delivery volume of the gas outlet pipe section connected to station s.

[0025] Furthermore, the construction of a natural gas pipeline network emergency dispatch optimization model based on the natural gas pipeline network data and the user satisfaction data further includes,

[0026] The emergency dispatch optimization model for the natural gas pipeline network is constructed as follows:

[0027]

[0028] Among them, Q iSUP Q represents the actual natural gas supply of user node i. iDEM w represents the natural gas demand of user node i. i This represents the weight coefficient of user node i.

[0029] Furthermore, the iterative solution of the natural gas pipeline network emergency dispatch optimization model based on the multi-objective particle swarm optimization algorithm further includes,

[0030] An initial population is constructed, where the position of each particle in the initial population represents the actual supply of natural gas, and the value range of each dimension of the particle is [0, Q]. iDEM ];

[0031] Calculate the variance of user satisfaction for all particles in the initial population and the overall user satisfaction, and perform fast non-dominated sorting of the particles based on the calculation results;

[0032] The position of the particle is updated based on the fast non-dominated sorting result, and the solution with the lower non-dominated sorting level is selected when selecting descendant particles.

[0033] Repeat the above steps until the number of times the algorithm runs reaches the preset threshold, and generate the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

[0034] Furthermore, updating the particle's position based on the fast non-dominated sorting result further includes,

[0035] Based on the fast non-dominated sorting, a particle is randomly selected in the first layer to be determined as the current optimal Lbest and the globally optimal particle Gbest.

[0036] In the last layer, a particle is randomly selected to be the current worst Lworst and the global worst particle Gworst.

[0037] Establish position x i The update formula is:

[0038]

[0039] Among them, v i denoted as particle velocity; c1 and c2 are inertia coefficients preset based on experience; r is a random real number in the interval [0, 1].

[0040] Furthermore, calculating solutions at lower non-dominated levels based on the fast non-dominated sorting further includes,

[0041] If the non-dominated levels are the same, the solution with the larger crowding distance is selected.

[0042] On the other hand, embodiments of this specification also provide a natural gas pipeline network emergency dispatch optimization device, the device comprising:

[0043] The data acquisition module is used to acquire natural gas pipeline network data as well as the natural gas demand and actual supply of each user, and to determine the user satisfaction of each user based on the natural gas demand and actual supply.

[0044] The model building module is used to build an emergency dispatch optimization model for the natural gas pipeline network based on the natural gas pipeline network data and the user satisfaction.

[0045] The model solving module is used to iteratively solve the emergency dispatch optimization model of the natural gas pipeline network based on the multi-objective particle swarm optimization algorithm, and obtain the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

[0046] The data display module is used to save the optimal user satisfaction variance and the Pareto optimal solution set of overall user satisfaction, and generate summary data for display.

[0047] On the other hand, embodiments of this specification also provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-described method.

[0048] On the other hand, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0049] Finally, this specification also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method.

[0050] Using the embodiments in this specification, data from the natural gas pipeline network, along with the natural gas demand and actual supply for each user, are acquired. Then, user satisfaction for each user is assessed based on these demand and supply figures, aiming to minimize the variance of individual user satisfaction. Next, an emergency dispatch optimization model for the natural gas pipeline network is constructed based on the acquired network data and user satisfaction data, enabling rapid and efficient development of emergency dispatch optimization plans. After obtaining the optimization model, a multi-objective particle swarm optimization algorithm is used to iteratively solve the model, obtaining the optimal Pareto optimal solution set for both the variance of individual user satisfaction and the overall user satisfaction. Finally, the optimal Pareto optimal solution set for both is saved and summarized for display. This approach achieves the goal of minimizing the variance of individual user satisfaction and maximizing overall user satisfaction, while also being able to distinguish between different user categories and meeting diverse user needs. Furthermore, it is not limited by the amount of natural gas that can be reduced, thus satisfying various complex emergency dispatch scenarios. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 The figure shown is a schematic diagram of the implementation system of a natural gas pipeline emergency dispatch optimization method in an embodiment of this specification;

[0053] Figure 2 The diagram shown is a flowchart illustrating the emergency dispatch optimization method for natural gas pipeline networks in an embodiment of this specification.

[0054] Figure 3 The diagram shown is a schematic representation of the iterative solution process for the emergency dispatch optimization model of the natural gas pipeline network based on the multi-objective particle swarm optimization algorithm in an embodiment of this specification.

[0055] Figure 4 The diagram shown is a schematic representation of the specific structure of the natural gas pipeline emergency dispatch optimization device in this embodiment.

[0056] Figure 5 The diagram shown is a structural schematic of the computer device in an embodiment of this specification.

[0057] [Explanation of Figure Markers]:

[0058] 101. Terminal;

[0059] 102. Server;

[0060] 401. Data Acquisition Module;

[0061] 402. Model building module;

[0062] 403. Model Solving Module;

[0063] 404, Data Display Module;

[0064] 502. Computer equipment;

[0065] 504. Processing equipment;

[0066] 506. Storage resources;

[0067] 508. Drive system;

[0068] 510. Input / output module;

[0069] 512. Input devices;

[0070] 514. Output devices;

[0071] 516. Presentation equipment;

[0072] 518. Graphical User Interface;

[0073] 520. Network interface;

[0074] 522. Communication link;

[0075] 524. Communication bus. Detailed Implementation

[0076] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the embodiments of this specification.

[0077] It should be noted that the terms "first," "second," etc., in the description, claims, and accompanying drawings of the embodiments herein 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 where appropriate so that embodiments of the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0078] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification all comply with the relevant provisions of national laws and regulations.

[0079] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0080] like Figure 1 The diagram illustrates an implementation system for a natural gas pipeline emergency dispatch optimization method according to an embodiment of this specification, including a terminal 101 and a server 102. The terminal 101 and server 102 can communicate via a network, which may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, user equipment (e.g., computing devices), and a backend system.

[0081] The project manager can input natural gas pipeline network data, as well as the natural gas demand and actual supply for each user, into server 102 via terminal 101. Server 102 determines the user satisfaction for each user based on the natural gas demand and actual supply, then constructs an emergency dispatch optimization model for the natural gas pipeline network, obtains the optimal user satisfaction variance and the Pareto optimal solution set for overall user satisfaction, and displays the optimization suggestions to the project manager via terminal 101. Optionally, server 102 can be a node in a cloud computing system (not shown in the figure), or each server can be a separate cloud computing system, including multiple computers interconnected by a network and operating as a distributed processing system.

[0082] In addition, it should be noted that, Figure 1The examples shown are merely one application environment provided by the embodiments in this specification. In practical applications, other application environments may also be included, and this specification does not impose any limitations.

[0083] To address the problems existing in the prior art, this specification provides an optimization method for emergency dispatching of natural gas pipeline networks. Through a triangular privacy computation method and function encryption, it ensures that the model provider and data provider only need to encrypt and entrust their data to the user, without participating in other complex calculations. The process of calculating the classification results (i.e., the decryption algorithm), which involves the largest computational load, is executed by the user.

[0084] Figure 2 The diagram shown is a flowchart illustrating the natural gas pipeline emergency dispatch optimization method in an embodiment of this specification. The diagram depicts the natural gas pipeline emergency dispatch optimization process. The order of steps listed in the embodiment is merely one possible execution order among many and does not represent the only possible order. In actual system or device products, the methods shown in the embodiments or accompanying drawings can be executed sequentially or in parallel.

[0085] Specific examples Figure 2 As shown, the method may include:

[0086] Step 201: Obtain natural gas pipeline network data and the natural gas demand and actual supply for each user, and determine the user satisfaction for each user based on the natural gas demand and actual supply;

[0087] Step 202: Construct an emergency dispatch optimization model for the natural gas pipeline network based on the natural gas pipeline network data and the user satisfaction data;

[0088] Step 203: Iteratively solve the emergency dispatch optimization model of the natural gas pipeline network based on the multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

[0089] Step 204: Save the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction, and generate summary data for display.

[0090] Using the embodiments in this specification, data from the natural gas pipeline network, along with the natural gas demand and actual supply for each user, are acquired. Then, user satisfaction for each user is assessed based on these demand and supply figures, aiming to minimize the variance of individual user satisfaction. Next, an emergency dispatch optimization model for the natural gas pipeline network is constructed based on the acquired network data and user satisfaction data, enabling rapid and efficient development of emergency dispatch optimization plans. After obtaining the optimization model, a multi-objective particle swarm optimization algorithm is used to iteratively solve the model, obtaining the optimal Pareto optimal solution set for both the variance of individual user satisfaction and the overall user satisfaction. Finally, the optimal Pareto optimal solution set for both is saved and summarized for display. This approach achieves the goal of minimizing the variance of individual user satisfaction and maximizing overall user satisfaction, while also being able to distinguish between different user categories and meeting diverse user needs. Furthermore, it is not limited by the amount of natural gas that can be reduced, thus satisfying various complex emergency dispatch scenarios.

[0091] In the embodiments of this specification, the input data constructs an optimization model with the objectives of minimizing user satisfaction variance and maximizing overall user satisfaction, while satisfying inflow / outflow balance constraints and pipeline flow limits. This addresses the shortcomings of traditional average pressure reduction methods in responding to emergencies and dynamic changes in natural gas supply and demand, such as the inability to identify key users and poor flexibility. Heuristic algorithms also suffer from limitations in applicable scenarios. Furthermore, the optimization method constructed in this specification utilizes a multi-objective particle swarm optimization algorithm to iteratively solve the model, obtaining the Pareto optimal solution set for both user satisfaction variance and overall user satisfaction. This allows for the identification of key users and avoids user-specific gas reduction constraints. By solving the model using the multi-objective particle swarm optimization algorithm, the final result achieves both sufficient natural gas supply for different types of users and effective improvement in overall user satisfaction.

[0092] According to one embodiment of this specification, in order to ensure that user satisfaction is not lower than a threshold, it is necessary to set constraints on user satisfaction. Determining each user's satisfaction based on the stated natural gas demand and actual supply further includes...

[0093] The constraint on user satisfaction is,

[0094]

[0095] Among them, Q iSUP Q represents the actual natural gas supply to user node i. iDEM Let represent the natural gas demand of user node i.

[0096] According to one embodiment of this specification, the natural gas pipeline network data includes at least the following:

[0097] The gas supply volume of each natural gas source, the connecting stations, the mileage of each pipeline segment, the pipeline to which it belongs, the transport volume of the pipeline segment, the starting station, and the ending station.

[0098] According to one embodiment of this specification, in order to construct an optimization model that conforms to actual conditions, it is necessary to further constrain the natural gas pipeline network based on its transportation capacity. The natural gas pipeline network data further includes...

[0099] Set the pipeline segment transportation capacity limit based on the pipeline segment transportation volume:

[0100]

[0101] Where i represents the starting station of the pipeline segment, j represents the ending station of the pipeline segment, and f ij This represents the pipeline throughput of segment (i,j). This represents the maximum transport capacity of pipe segment (i,j). This represents the minimum pipeline capacity for pipe segment (i,j);

[0102] Based on the starting and ending stations, set station entry and exit balance constraints:

[0103]

[0104] Where s represents any station; This indicates the production capacity of the gas source g connected to station s; This represents the sales volume of customer c connected to station s; This indicates the gas delivery volume of the intake pipe section connected to station s; This indicates the gas delivery volume of the gas outlet pipe section connected to station s.

[0105] According to one embodiment of this specification, in order to construct an optimization model that aims to minimize the variance of user satisfaction and maximize overall user satisfaction, the construction of a natural gas pipeline network emergency dispatch optimization model based on the natural gas pipeline network data and the user satisfaction further includes...

[0106] The emergency dispatch optimization model for the natural gas pipeline network is constructed as follows:

[0107]

[0108] Among them, Q iSUP Q represents the actual natural gas supply of user node i. iDEM w represents the natural gas demand of user node i. i This represents the weight coefficient of user node i. The larger the value, the more important the user is.

[0109] According to one embodiment of this specification, such as Figure 3 As shown, the iterative solution of the natural gas pipeline network emergency dispatch optimization model based on the multi-objective particle swarm optimization algorithm further includes,

[0110] Step 301: Construct an initial population, where the position of each particle in the initial population represents the actual supply of natural gas, and the value range of each dimension of the particle is [0, Q]. iDEM ];

[0111] Step 302: Calculate the variance of user satisfaction of all particles in the initial population and the overall user satisfaction, and perform fast non-dominated sorting of the particles based on the calculation results;

[0112] Step 303: Update the position of the particle according to the fast non-dominated sorting result, and select the solution with the lower non-dominated sorting level when selecting descendant particles;

[0113] Step 304: Repeat the above steps until the number of times the algorithm runs reaches the preset threshold, and generate the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

[0114] Updating the position of the particle based on the fast non-dominated sorting result further includes...

[0115] Based on the fast non-dominated sorting, a particle is randomly selected in the first layer to be determined as the current optimal Lbest and the globally optimal particle Gbest.

[0116] In the last layer, a particle is randomly selected to be the current worst Lworst and the global worst particle Gworst.

[0117] Establish position x i The update formula is:

[0118]

[0119] Among them, v i denoted as particle velocity; c1 and c2 are inertia coefficients preset based on experience; r is a random real number in the interval [0, 1].

[0120] Calculating solutions at lower non-dominated levels based on the fast non-dominated sorting further includes...

[0121] If the non-dominated levels are the same, the solution with the larger crowding distance is selected.

[0122] For example, after the natural gas pipeline emergency dispatch model is constructed, it is solved using a multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set for user satisfaction variance and overall user satisfaction. An initial population of size 30 is randomly generated. The position of each particle represents the actual natural gas supply to the user, the dimension of the particle is the number of users, and the value range of each dimension is [0, Q]. iDEM ].

[0123] The variance of user satisfaction for all particles in the initial population and the overall user satisfaction are calculated. Based on the calculation results, the particles are sorted using a fast non-dominated ranking method, which is a ranking method based on the Pareto dominance concept. In multi-objective optimization, if a solution is better than or equal to another solution on all objectives, and is strictly better than the other solution on at least one objective, then a solution is said to Pareto dominate the other solution.

[0124] In the first layer of the fast non-dominated sorting results, a particle is randomly selected to be both the current best (Lbest) and the globally best (Gbest) particle. In the last layer, a particle is randomly selected to be both the current worst (Lworst) and the globally worst (Gworst) particle. Let r be a random real number in the interval [0, 1]. When r < 0.9, the particle updates its position after forward learning; otherwise, the particle updates its position after backward learning. (Velocity v) i With position x i The specific update formula is as follows:

[0125]

[0126] The variance of user satisfaction and overall user satisfaction are calculated, and the results are merged with those of the previous generation for fast non-dominated sorting and crowding lookup. When selecting descendant particles, solutions with lower non-dominated levels are prioritized; if levels are the same, solutions with larger crowding distances are selected, ultimately retaining 30 particles as the Pareto optimal solution set.

[0127] As one embodiment of this specification, reference may also be made to, for example, Figure 4 The diagram shown is a schematic representation of the specific structure of the salary calculation result verification device based on the clustering algorithm in this embodiment.

[0128] Data acquisition module 401 is used to acquire natural gas pipeline network data and the natural gas demand and actual supply of each user, and to determine the user satisfaction of each user based on the natural gas demand and actual supply.

[0129] Model building module 402 is used to build an emergency dispatch optimization model for the natural gas pipeline network based on the natural gas pipeline network data and the user satisfaction.

[0130] Model solving module 403 is used to iteratively solve the emergency dispatch optimization model of the natural gas pipeline network based on the multi-objective particle swarm algorithm to obtain the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

[0131] The data display module 404 is used to save the optimal user satisfaction variance and the Pareto optimal solution set of overall user satisfaction and generate summary data for display.

[0132] Since the principle of the above-mentioned device in solving the problem is similar to that of the above-mentioned method, the implementation of the above-mentioned system can refer to the implementation of the above-mentioned method, and the repeated parts will not be described again.

[0133] like Figure 5 The diagram shown is a structural schematic of a computer device according to an embodiment of this specification. The computer device in this embodiment can run the methods described in this specification.

[0134] Computer device 502 may include one or more processing devices 504, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 502 may also include any storage resource 506 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, storage resource 506 may include any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage resource can use any technology to store information.

[0135] Furthermore, any storage resource can provide volatile or non-volatile retention of information.

[0136] Furthermore, any storage resource can represent a fixed or removable component of the computer device 502. In one case, when the processing device 504 executes associated instructions stored in any storage resource or combination of storage resources, the computer device 502 can perform any operation of the associated instructions. The computer device 502 also includes one or more drive systems 508 for interacting with any storage resource, such as a hard disk drive system, an optical disk drive system, etc.

[0137] Computer device 502 may also include an input / output module 510 (I / O) for receiving various inputs (via input device 512) and providing various outputs (via output device 514). A specific output mechanism may include a presentation device 516 and an associated graphical user interface 518 (GUI). In other embodiments, the input / output module 510 (I / O), input device 512, and output device 514 may be omitted, and the device may function solely as a computer device within a network. Computer device 502 may also include one or more network interfaces 520 for exchanging data with other devices via one or more communication links 522. One or more communication buses 524 couple the components described above together.

[0138] Communication link 522 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0139] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0140] This specification also provides computer-readable instructions, wherein when a processor executes the instructions, the program therein causes the processor to perform the above-described method.

[0141] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0142] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0143] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the embodiments in this specification.

[0144] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0145] In the embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.

[0147] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this specification, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] This specification describes the principles and implementation methods of the embodiments using specific examples. The above descriptions of the embodiments are only for the purpose of helping to understand the methods and core ideas of the embodiments in this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments in this specification. Therefore, the content of this specification should not be construed as a limitation on the embodiments in this specification.

Claims

1. A method for optimizing emergency dispatch of natural gas pipeline networks, characterized in that, The method includes: Acquire natural gas pipeline network data, as well as the natural gas demand and actual supply for each user, and determine the user satisfaction for each user based on the natural gas demand and actual supply. An emergency dispatch optimization model for the natural gas pipeline network is constructed based on the natural gas pipeline network data and the user satisfaction data. The emergency dispatch optimization model of the natural gas pipeline network is iteratively solved using the multi-objective particle swarm optimization algorithm to obtain the optimal user satisfaction variance and the Pareto optimal solution set of overall user satisfaction. Save the optimal Pareto solution set of the optimal user satisfaction variance and the overall user satisfaction, and generate summary data for display.

2. The natural gas pipeline emergency dispatch optimization method according to claim 1, characterized in that, The assessment of each user's satisfaction based on the stated natural gas demand and actual supply further includes... The constraint on user satisfaction is, Among them, Q iSUP Q represents the actual natural gas supply to user node i. iDEM Let represent the natural gas demand of user node i.

3. The natural gas pipeline emergency dispatch optimization method according to claim 1, characterized in that, Natural gas pipeline network data should include at least the following: The gas supply volume of each natural gas source, the connecting stations, the mileage of each pipeline segment, the pipeline to which it belongs, the transport volume of the pipeline segment, the starting station, and the ending station.

4. The natural gas pipeline emergency dispatch optimization method according to claim 3, characterized in that, The natural gas pipeline network data further includes, Set the pipeline segment transportation capacity limit based on the pipeline segment transportation volume: Where i represents the starting station of the pipeline segment, j represents the ending station of the pipeline segment, and f ij This represents the pipeline transport capacity of pipe segment (i,j). This represents the maximum transport capacity of pipe segment (i,j). This represents the minimum pipeline capacity of pipe segment (i,j); Based on the starting and ending stations, set station entry and exit balance constraints: Where s represents any station; This indicates the production capacity of the gas source g connected to station s; This represents the sales volume of customer c connected to station s; This indicates the gas delivery volume of the intake pipe section connected to station s; This indicates the gas delivery volume of the gas outlet pipe section connected to station s.

5. The natural gas pipeline emergency dispatch optimization method according to claim 1, characterized in that, The natural gas pipeline network emergency dispatch optimization model, constructed based on the aforementioned natural gas pipeline network data and user satisfaction, further includes... The emergency dispatch optimization model for the natural gas pipeline network is constructed as follows: Where N is the total number of users, Q iSUP Q represents the actual natural gas supply of user node i. iDEM w represents the natural gas demand of user node i. i This represents the weight coefficient of user node i.

6. The method for optimizing emergency dispatch of natural gas pipeline networks according to claim 1, characterized in that, The iterative solution of the natural gas pipeline network emergency dispatch optimization model based on the multi-objective particle swarm optimization algorithm further includes, An initial population is constructed, where the position of each particle in the initial population represents the actual supply of natural gas, and the value range of each dimension of the particle is [0, Q]. iDEM ]; Calculate the variance of user satisfaction for all particles in the initial population and the overall user satisfaction, and perform fast non-dominated sorting of the particles based on the calculation results; The position of the particle is updated based on the fast non-dominated sorting result, and the solution with the lower non-dominated sorting level is selected when selecting descendant particles. Repeat the above steps until the number of times the algorithm runs reaches the preset threshold, and generate the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction.

7. The emergency dispatch optimization method for natural gas pipeline networks according to claim 6, characterized in that, Updating the position of the particle based on the fast non-dominated sorting result further includes... Based on the fast non-dominated sorting, a particle is randomly selected in the first layer to be determined as the current optimal Lbest and the globally optimal particle Gbest. In the last layer, a particle is randomly selected to be the current worst Lworst and the global worst particle Gworst. Establish position x i The update formula is: Among them, v i denoted as particle velocity; c1 and c2 are inertia coefficients preset based on experience; r is a random real number in the interval [0, 1].

8. The natural gas pipeline emergency dispatch optimization method according to claim 6, characterized in that, Calculating solutions at lower non-dominated levels based on the fast non-dominated sorting further includes... If the non-dominated levels are the same, the solution with the larger crowding distance is selected.

9. A natural gas pipeline network emergency dispatch optimization device, characterized in that, The device includes: The data acquisition module is used to acquire natural gas pipeline network data as well as the natural gas demand and actual supply of each user, and to determine the user satisfaction of each user based on the natural gas demand and actual supply. The model building module is used to build an emergency dispatch optimization model for the natural gas pipeline network based on the natural gas pipeline network data and the user satisfaction. The model solving module is used to iteratively solve the emergency dispatch optimization model of the natural gas pipeline network based on the multi-objective particle swarm optimization algorithm, and obtain the Pareto optimal solution set of the optimal user satisfaction variance and the overall user satisfaction. The data display module is used to save the optimal user satisfaction variance and the Pareto optimal solution set of overall user satisfaction, and generate summary data for display.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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