Deterministic risk identification method, device and equipment for natural gas pipeline network system and medium

Abstract

The invention discloses a natural gas pipeline network system deterministic risk identification method and device, equipment and a medium, and relates to the technical field of natural gas, and the method comprises the steps: carrying out the risk factor identification of a natural gas pipeline network system through a fishbone graph method and a risk logic tree method, and obtaining a corresponding deterministic risk factor; the deterministic risk factors comprise an equipment risk factor, a geological disaster risk factor and a man-made damage risk factor; constructing a natural gas pipeline network system optimization model comprising a target function and constraint conditions; and inputting the deterministic risk factors into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors. By means of the technical scheme, the problem that specific quantification of the carbon emission of the natural gas pipeline network system under the deterministic risk is lacked can be solved, multiple risk aspects and multiple key factors are considered, and risk identification of the natural gas pipeline network and optimization of the system topological structure are achieved.

Description

Technical Field

[0001] The present invention relates to the field of natural gas technology, and in particular to a method, device, equipment and medium for identifying deterministic risks in a natural gas pipeline network system. Background Art

[0002] In the operation and management of natural gas pipeline network systems, deterministic risks include but are not limited to aging and corrosion of equipment, the impact of geological disasters, and damage caused by human factors. The existence of deterministic risks not only threatens the safe and stable operation of the pipeline network system, but may also lead to serious environmental accidents and socioeconomic losses. At present, existing research on the deterministic risks of pipeline network systems is limited to the analysis of a single system component, resulting in certain differences between the research conclusions and the actual situation. In addition, when discussing the deterministic risks of natural gas pipeline network systems, the focus is mainly on the discussion of methodology, but the key factors in actual production are not fully valued. This phenomenon leads to a certain adaptability gap between the research results and the deterministic risk problems faced in the actual operation of the pipeline network.

[0003] From the above, it can be seen that how to solve the problem of lack of specific quantification of carbon emissions of natural gas pipeline systems under deterministic risks, consider multiple risk aspects and multiple key factors, and achieve risk identification of natural gas pipeline networks and optimization of system topology are issues to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for identifying deterministic risks of a natural gas pipeline network system, which can solve the problem of lack of specific quantification of carbon emissions of a natural gas pipeline network system under deterministic risks, consider multiple risk aspects and multiple key factors, and achieve risk identification of the natural gas pipeline network and optimization of the system topology. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a method for identifying deterministic risks of a natural gas pipeline network system, comprising:

[0006] The fishbone diagram method and the risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified, so as to obtain the corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors and human damage risk factors;

[0007] Constructing a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints;

[0008] The deterministic risk factors are input into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

[0009] Optionally, the fishbone diagram method and the risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified, so as to obtain corresponding deterministic risk factors, including:

[0010] Combine the fishbone diagram method with the risk logic tree method, and perform risk identification and risk tracing on the natural gas pipeline network system to be identified, so as to obtain the components with risk sources;

[0011] The components having risk sources are sorted out to obtain corresponding deterministic risk factors.

[0012] Optionally, the objective function is:

[0013] ;

[0014] in, is the objective function, is the user supply priority weight coefficient, the value range is [0,1], D is the total number of users in the system, is the gas supply priority coefficient of user d, is the actual gas volume obtained by user d under the system deterministic risk, is the planned gas volume of user d under system deterministic risk, is the actual carbon emissions of user d under system deterministic risk, is the maximum carbon emission of user d under system deterministic risk, S is the total number of gas sources in the pipeline network system, is the actual carbon emission of gas source s under system deterministic risk, is the maximum carbon emission of gas source s under system deterministic risk, Z is the total number of distribution stations in the system, is the actual carbon emissions of substation z under system deterministic risk, is the maximum carbon emission of distribution station z under system deterministic risk, R is the total number of gas storages in the system, is the actual carbon emissions of gas storage r under system deterministic risk, is the maximum carbon emission of gas storage r under system deterministic risk, T is the total number of liquefied natural gas receiving stations in the system, is the actual carbon emissions of LNG receiving station t under system deterministic risk, is the maximum carbon emission of LNG receiving station t under system deterministic risk.

[0015] Optionally, the gas source mass conservation constraint condition is:

[0016] ;

[0017] in, is the actual gas supply of gas source s, is the sum of the gas flow rates of all gas pipeline sections connected to the gas source s, is the gas pipeline section starting from the gas source s;

[0018] The actual production constraints of the gas source are:

[0019] ;

[0020] in, and are the upper and lower limits of the gas supply capacity of the gas source s respectively;

[0021] The gas source carbon emission constraint condition is:

[0022] ;

[0023] in, is the actual carbon emission of gas source s, is the maximum daily carbon emission of gas source s;

[0024] The user quality conservation constraint is:

[0025] ;

[0026] in, is the actual gas volume obtained by user d, is the sum of the gas flow rates of all gas pipeline sections directly connected to user d, is the gas pipeline section with user d as the terminal;

[0027] The actual production constraints of the user are:

[0028] ;

[0029] in, is the daily planned gas demand of user d;

[0030] The user carbon emission constraint condition is:

[0031] ;

[0032] in, is the maximum carbon emission per day of user d;

[0033] The mass conservation constraint condition of the sub-transmission station is:

[0034] ;

[0035] in, is the actual gas volume entering station z from upstream, is the gas pipeline section with station z as the terminal, is the actual gas volume exported from station z to the downstream, is the gas pipeline section starting from station z;

[0036] The carbon emission constraint conditions of the sub-transmission station are:

[0037] ;

[0038] in, is the actual carbon emission of substation z under the system deterministic risk scenario, is the maximum carbon emission of substation z under the system deterministic risk scenario;

[0039] The actual production constraints of the gas pipeline section are:

[0040] ;

[0041] in, is the actual flow rate of gas pipeline section p, is the maximum output of the gas pipeline section p;

[0042] The gas storage mass conservation constraint is:

[0043] ;

[0044] in, is the actual injection of gas storage r, is the gas production of gas storage r, is the positive flow rate of all gas pipeline sections directly connected to the gas storage r, The reverse flow of all gas pipeline sections directly connected to the gas storage r;

[0045] The actual production constraints of the gas storage are:

[0046] ;

[0047] ;

[0048] in, , is the actual gas injection and production volume of gas storage r, , is the maximum gas injection and production volume of gas storage r;

[0049] The carbon emission constraints of the gas storage are:

[0050] ;

[0051] in, is the actual carbon emission of gas storage r under the system deterministic risk scenario, is the maximum carbon emission of gas storage r under the system deterministic risk scenario;

[0052] The mass conservation constraint condition of the liquefied natural gas receiving station is:

[0053] ;

[0054] in, is the actual amount of natural gas unloaded by LNG receiving station t, is the sum of the throughput of all pipeline sections directly connected to the LNG receiving station t, It is the connection and unloading pipe section with the liquefied natural gas receiving station t as the terminal;

[0055] The actual production constraints of the liquefied natural gas receiving station are:

[0056] ;

[0057] in, is the maximum gas loading and unloading volume of LNG receiving station t;

[0058] The carbon emission constraints of the liquefied natural gas receiving station are:

[0059] ;

[0060] in, is the actual carbon emissions of LNG receiving station t under the system deterministic risk scenario, is the maximum carbon emission of LNG receiving station t under the system deterministic risk scenario.

[0061] Optionally, the inputting of the deterministic risk factors into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors includes:

[0062] Filtering a target solver from preset solvers; the solvers include a CPLEX solver and a Gurobi solver;

[0063] The deterministic risk factors are input into the natural gas pipeline system optimization model, and solved in conjunction with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

[0064] In a second aspect, the present application discloses a deterministic risk identification device for a natural gas pipeline network system, comprising:

[0065] A risk factor identification module is used to identify risk factors of the natural gas pipeline network system to be identified by using a fishbone diagram method and a risk logic tree method to obtain corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors, and human damage risk factors;

[0066] An optimization model construction module is used to construct a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints;

[0067] The plan and suggestion determination module is used to input the deterministic risk factors into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

[0068] Optionally, the risk factor identification module includes:

[0069] The risk identification and risk tracing module is used to combine the fishbone diagram method and the risk logic tree method, and to identify and trace the risks of the natural gas pipeline network system to be identified, so as to obtain the components with risk sources;

[0070] The sorting module is used to sort the components with risk sources to obtain corresponding deterministic risk factors.

[0071] Optionally, the plan and suggestion determination module includes:

[0072] A solver screening module, used to screen out a target solver from preset solvers; the solvers include CPLEX solver and Gurobi solver;

[0073] A solution module is used to input the deterministic risk factors into the natural gas pipeline system optimization model, and solve it in combination with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

[0074] In a third aspect, the present application discloses an electronic device, comprising:

[0075] Memory, used to store computer programs;

[0076] A processor is used to execute the computer program to implement the aforementioned natural gas pipeline network system deterministic risk identification method.

[0077] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned method for deterministic risk identification of a natural gas pipeline network system are implemented.

[0078] It can be seen that the present application provides a method for identifying deterministic risks of a natural gas pipeline network system, including using a fishbone diagram method and a risk logic tree method to identify risk factors of a natural gas pipeline network system to be identified, so as to obtain corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors, and human damage risk factors; constructing a natural gas pipeline network system optimization model including an objective function and constraint conditions; the constraint conditions include a gas source quality conservation constraint condition, a gas source actual production constraint condition, a gas source carbon emission constraint condition, a user quality conservation constraint condition, and a user actual Production constraints, user carbon emission constraints, distribution station quality conservation constraints, distribution station carbon emission constraints, actual production constraints of gas pipeline sections, gas storage quality conservation constraints, actual production constraints of gas storage, carbon emission constraints of gas storage, liquefied natural gas receiving station quality conservation constraints, actual production constraints of liquefied natural gas receiving station and carbon emission constraints of liquefied natural gas receiving station; the deterministic risk factors are input into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under deterministic risk factors. The present application combines the fishbone diagram method and the risk logic tree method to identify the risk factors of the natural gas pipeline network system to be identified. It can identify the risk factors of the historical risk situations of the pipeline network system, trace the causes of the risks, and obtain the corresponding deterministic risk factors. It is applicable to natural gas pipeline networks of various sizes and complexity levels, and constructs a natural gas pipeline network system optimization model including objective functions and constraints. The constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, and gas pipeline section actual production constraints. Production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emissions constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emissions constraints, solve the problem of lack of specific quantification of carbon emissions of the natural gas pipeline system under deterministic risks, and consider multiple risk aspects and multiple key factors, input the deterministic risk factors into the natural gas pipeline system optimization model, and obtain risk identification emergency plans and guidance suggestions under deterministic risk factors, thereby realizing risk identification of the natural gas pipeline network and optimization of the system topology, which has strong universality, feasibility and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0080] Figure 1 A flow chart of a deterministic risk identification method for a natural gas pipeline network system disclosed in this application;

[0081] Figure 2 A topological structure diagram of a natural gas pipeline network system disclosed in this application;

[0082] Figure 3 A classic fishbone diagram structure diagram disclosed in this application;

[0083] Figure 4 A fishbone diagram suitable for a natural gas pipeline network system disclosed in the present application;

[0084] Figure 5 A risk logic tree diagram suitable for a natural gas pipeline network system disclosed in this application;

[0085] Figure 6 This is a schematic diagram of the structure of a deterministic risk identification device for a natural gas pipeline network system disclosed in this application;

[0086] Figure 7 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] In the operation and management of the natural gas pipeline network system, deterministic risks include but are not limited to aging and corrosion of equipment, the impact of geological disasters, and damage caused by human factors. The existence of deterministic risks not only threatens the safe and stable operation of the pipeline network system, but may also lead to serious environmental accidents and socio-economic losses. At present, existing research on the deterministic risks of the pipeline network system is limited to the analysis of a single system component, resulting in a certain difference between the research conclusions and the actual situation. In addition, when discussing the deterministic risks of the natural gas pipeline network system, it mainly focuses on the discussion of methodology, but fails to pay enough attention to the key factors in actual production. This phenomenon has caused a certain adaptability gap between the research results and the deterministic risk problems faced in the actual operation of the pipeline network. As can be seen from the above, how to solve the problem of the lack of specific quantification of carbon emissions of the natural gas pipeline network system under deterministic risks, consider multiple risk aspects and multiple key factors, and realize the risk identification of the natural gas pipeline network and the optimization of the system topology are problems to be solved in this field.

[0089] See also Figure 1 As shown, the embodiment of the present invention discloses a method for identifying deterministic risks of a natural gas pipeline network system, which may specifically include:

[0090] Step S11: using the fishbone diagram method and the risk logic tree method to identify the risk factors of the natural gas pipeline network system to be identified, so as to obtain corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors and human damage risk factors.

[0091] In this embodiment, the fishbone diagram method and the risk logic tree method are combined, and the natural gas pipeline system to be identified is subjected to risk identification and risk tracing to obtain the components with risk sources; the components with risk sources are sorted to obtain corresponding deterministic risk factors.

[0092] In order to achieve risk identification and resilience optimization of the natural gas pipeline system under deterministic risk situations, the first step is to use complex network theory to simplify the actual overall structure of the natural gas pipeline system, aiming to reduce the complexity of calculations in the subsequent optimization process. After simplification by complex network theory, the natural gas pipeline network can be abstracted as a topological structure consisting of nodes and edges connecting nodes. In this topological structure, each node represents a specific functional unit in the pipeline system, such as a gas source node, a user node, a distribution station node, etc. Different nodes have their own unique attributes according to their functional differences in the system; and the edges between nodes symbolize the gas pipeline sections between the various functional units in the system, and their function is to ensure the circulation and transportation of natural gas between the system nodes. The simplified topological structure of the natural gas pipeline system based on complex network theory is shown in the figure. Figure 2 shown.

[0093] After simplifying the overall structure of the pipeline network system, the connection relationship and characteristics of each system component are described as follows: (1) Gas source node: In the natural gas pipeline network system, the gas source node is located in the upstream part of the system. The sum of its values ​​represents the total input of natural gas in the system, that is, the total gas supply of the system. There are many types of gas sources, including domestically produced natural gas resources, imported natural gas transported through cross-border pipelines, LNG (Liquefied Natural Gas) imported by sea, and natural gas from unconventional resources;

[0094] (2) Distribution station nodes: Intermediate station nodes are key components of the upstream part of the natural gas pipeline network system. Together with the gas pipelines, they bear the function of connecting other nodes to each other. Distribution station nodes play a vital role in the system, ensuring that natural gas is safely and efficiently transported from the production site to downstream users;

[0095] (3) Gas pipeline section: The gas pipeline section is the midstream component of the natural gas pipeline network system and plays the role of a carrier for transporting natural gas resources. A gas pipeline section refers to a section of gas pipeline without an upstream or downstream distribution point. These gas pipeline sections are interconnected to form a continuous long-distance transmission pipeline. When multiple long-distance gas pipelines are interconnected and interoperable, they will together form a complete gas pipeline network;

[0096] (4) User nodes: User nodes are located in the downstream part of the natural gas pipeline network system. The sum of their values ​​represents the total demand for natural gas in the entire system. User types are diverse, covering multiple fields such as urban gas, industrial production gas, and transportation gas.

[0097] (5) Gas storage node: Gas storage is a midstream link in the natural gas pipeline network system and is a facility for storing natural gas. Its total capacity represents the total storage capacity of natural gas in the industrial chain system. When the natural gas pipeline network system faces short-term gas shortage or seasonal peak load, gas storage can balance the supply and demand of the system to the greatest extent through peak load operation;

[0098] (6) LNG receiving station node: The LNG receiving station is a midstream link in the natural gas pipeline network system and is a loading and unloading facility responsible for receiving LNG. The total loading and unloading volume of the LNG receiving station represents the total amount of LNG received by the natural gas pipeline network system.

[0099] The detailed parameters involved in each system component are shown in Table 1:

[0100] Table 1

[0101]

[0102] After setting the detailed parameters of each component of the natural gas pipeline network system, the overall static structure of the pipeline network system has been built. Next, the deterministic risk scenarios will be identified and sorted out in combination with the actual production and operation of the pipeline network system. Risk identification refers to the use of various risk identification methods in risk management to comprehensively and continuously understand the various risks faced and analyze the various causes of risk accidents. The deterministic risk scenario identification proposed in this method includes two parts: one is to identify the source of the risk of the pipeline network system, that is, to identify the cause or source of the risk result based on observable or inferable results; the second is to determine the conditions for the occurrence of the risk, that is, on the premise of determining the source of the risk, further explain that when the conditions change, this type of risk source will likely cause the risk. The risk identification of the natural gas pipeline network system is based on actual cases and historical data. Through the planning and management of the overall risk of the system, risk identification is carried out according to the type of risk, combined with actual cases and past historical data. Commonly used risk identification methods include fishbone diagram method, risk logic tree method, Delphi method, case statistics method, etc., but not every method is suitable for the risk identification of the pipeline network system. It is necessary to combine the actual production situation to ensure that the identification results are accurate and effective.

[0103] The present invention selects a combination of fishbone diagram method and risk logic tree method to identify deterministic risks of pipe network system. The principle of fishbone diagram method is to find the cause according to the result, analyze the cause of each layer layer by layer for the classified cause, and finally draw the result at the fish head, and draw each cause into each branch. The pattern is similar to fishbone. The structure of classic fishbone diagram is as follows: Figure 3 As shown in the figure, the characteristics of the fishbone diagram are as follows: (1) Good classifiability: It can classify the causes of various results, and the same causes can be grouped into a large category, which is convenient for sorting out problems; (2) Strong intuitiveness: After sorting out various problems one by one, they are drawn into a shape similar to fish bones, which intuitively reflects which causes lead to the results.

[0104] When applying the fishbone diagram to the natural gas pipeline system, it should be made clear that the use of the fishbone diagram method to identify the deterministic risks of the pipeline system constitutes an adaptive adjustment to the traditional fishbone diagram method. The causes of pipeline system risks can be mainly summarized into two categories: one is the impact of risk factors on the integrity of the pipeline system links, and the other is the imbalance of the natural gas industry and its related industries. In the application of the fishbone diagram method, the focus is on the identification of risk factors on the risks of the pipeline system; in addition, although the fishbone diagram method requires that each sub-factor should remain independent, there may be causal relationships between different factors in the process of identifying risks in the pipeline system. For example, changes in the policy environment may affect the price ratio between natural gas and alternative energy, thereby affecting downstream demand and forming market factors. At the same time, the sub-factors are not completely independent, such as changes in the climate environment may have an impact on the policy environment; further, the fishbone diagram method starts from specific system deterministic risk events, traces the causes of the events, and checks the causes of each link of the pipeline system layer by layer to reveal the risks of the pipeline system. This process is from the outside to the inside. The fishbone diagram method needs to be used in combination with the risk logic tree method to accurately identify the deterministic risks of the pipeline network system. Figure 4 shown.

[0105] The risk logic tree method still starts from the results. According to the logical relationship between events or related links, it analyzes the causes of deterministic risk events layer by layer and link by link, and finally establishes a tree-structured risk logic analysis technology. The difference between the risk logic tree method and the fishbone diagram method is that the risk identification is based on the two levels of meaning of pipeline system risks. The first thing is to identify whether the deterministic risk of the pipeline system is caused by the mismatch between links or the integrity of the entire pipeline system is affected by risk factors. This is the trunk of the risk logic tree, which is expanded to other branches on two branches. Therefore, the risk logic tree method starts from the general connotation of pipeline system risks and identifies specific risk factors. Finally, it can be applied to a certain field of natural gas utilization to complete the process of deterministic risk identification from general to specific. The risk logic tree suitable for the natural gas pipeline system is as follows: Figure 5 By combining the fishbone diagram and the risk logic tree, the historical risk situations of the pipeline system can be sorted out and identified, the causes of the risks can be traced back, and the system components of the pipeline system where the risk sources are located can be found, thereby obtaining the corresponding deterministic risk factors.

[0106] Step S12: Construct a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-station quality conservation constraints, sub-station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints.

[0107] In this embodiment, the objective function is:

[0108] ;

[0109] in, is the objective function, is the user supply priority weight coefficient, the value range is [0,1], D is the total number of users in the system, is the gas supply priority coefficient of user d, is the actual gas volume obtained by user d under the system deterministic risk, is the planned gas volume of user d under system deterministic risk, is the actual carbon emissions of user d under system deterministic risk, is the maximum carbon emission of user d under system deterministic risk, S is the total number of gas sources in the pipeline network system, is the actual carbon emission of gas source s under system deterministic risk, is the maximum carbon emission of gas source s under system deterministic risk, Z is the total number of distribution stations in the system, is the actual carbon emissions of substation z under system deterministic risk, is the maximum carbon emission of distribution station z under system deterministic risk, R is the total number of gas storages in the system, is the actual carbon emissions of gas storage r under system deterministic risk, is the maximum carbon emission of gas storage r under system deterministic risk, T is the total number of liquefied natural gas receiving stations in the system, is the actual carbon emissions of LNG receiving station t under system deterministic risk, is the maximum carbon emission of LNG receiving terminal t under system deterministic risk;

[0110] The gas source mass conservation constraint condition is:

[0111] ;

[0112] in, is the actual gas supply of gas source s, is the sum of the gas flow rates of all gas pipeline sections connected to the gas source s, is the gas pipeline section starting from the gas source s;

[0113] The actual production constraints of the gas source are:

[0114] ;

[0115] in, and are the upper and lower limits of the gas supply capacity of the gas source s respectively;

[0116] The gas source carbon emission constraint condition is:

[0117] ;

[0118] in, is the actual carbon emission of gas source s, is the maximum daily carbon emission of gas source s;

[0119] The user quality conservation constraint is:

[0120] ;

[0121] in, is the actual gas volume obtained by user d, is the sum of the gas flow rates of all gas pipeline sections directly connected to user d, is the gas pipeline section with user d as the terminal;

[0122] The actual production constraints of the user are:

[0123] ;

[0124] in, is the daily planned gas demand of user d;

[0125] The user carbon emission constraint condition is:

[0126] ;

[0127] in, is the maximum carbon emission per day of user d;

[0128] The mass conservation constraint condition of the sub-transmission station is:

[0129] ;

[0130] in, is the actual gas volume entering station z from upstream, is the gas pipeline section with station z as the terminal, is the actual gas volume exported from station z to the downstream, is the gas pipeline section starting from station z;

[0131] The carbon emission constraint conditions of the sub-transmission station are:

[0132] ;

[0133] in, is the actual carbon emission of substation z under the system deterministic risk scenario, is the maximum carbon emission of substation z under the system deterministic risk scenario;

[0134] The actual production constraints of the gas pipeline section are:

[0135] ;

[0136] in, is the actual flow rate of gas pipeline section p, is the maximum output of the gas pipeline section p;

[0137] The gas storage mass conservation constraint is:

[0138] ;

[0139] in, is the actual injection of gas storage r, is the gas production of gas storage r, is the positive flow rate of all gas pipeline sections directly connected to the gas storage r, The reverse flow of all gas pipeline sections directly connected to the gas storage r;

[0140] The actual production constraints of the gas storage are:

[0141] ;

[0142] ;

[0143] in, , is the actual gas injection and production volume of gas storage r, , is the maximum gas injection and production volume of gas storage r;

[0144] The carbon emission constraints of the gas storage are:

[0145] ;

[0146] in, is the actual carbon emission of gas storage r under the system deterministic risk scenario, is the maximum carbon emission of gas storage r under the system deterministic risk scenario;

[0147] The mass conservation constraint condition of the liquefied natural gas receiving station is:

[0148] ;

[0149] in, is the actual amount of natural gas unloaded by LNG receiving station t, is the sum of the throughput of all pipeline sections directly connected to the LNG receiving station t, It is the connection and unloading pipe section with the liquefied natural gas receiving station t as the terminal;

[0150] The actual production constraints of the liquefied natural gas receiving station are:

[0151] ;

[0152] in, is the maximum gas loading and unloading volume of LNG receiving station t;

[0153] The carbon emission constraints of the liquefied natural gas receiving station are:

[0154] ;

[0155] in, is the actual carbon emissions of LNG receiving station t under the system deterministic risk scenario, is the maximum carbon emission of LNG receiving station t under the system deterministic risk scenario.

[0156] In this embodiment, the establishment of the natural gas pipeline network system optimization model can be mainly divided into two parts: objective function and constraint conditions.

[0157] In the first part, the objective function of the model should be set according to actual production. Under the deterministic risk situation, it is necessary to consider both satisfying the gas demand of users in the pipeline system and minimizing the carbon emissions of the system. Under the premise of considering the different gas supply priorities of each user in the pipeline system, the objective function comprehensively considers the user's gas supply satisfaction and the carbon emissions of system components, aiming to minimize the carbon emissions of the pipeline system while ensuring that the actual gas volume of each user reaches the planned gas demand as much as possible.

[0158] In the second part, the meaning of the gas source mass conservation constraint is: the actual gas supply of the gas source s is equal to the sum of the gas supply of all gas pipeline sections directly connected to the gas source s;

[0159] The actual production constraint of the gas source means that the actual gas supply of the gas source s will be automatically obtained after calculation by the optimization model according to the system deterministic risk situation, and the value will fluctuate between the upper and lower limits of its gas supply capacity;

[0160] The meaning of the constraint condition of gas source carbon emissions is that the actual carbon emissions of gas source s fluctuate between 0 and the maximum carbon emissions of a single day. The actual carbon emissions will be automatically calculated by the optimization model according to the system deterministic risk situation;

[0161] The meaning of the user quality conservation constraint is: the actual gas volume obtained by user d is equal to the sum of the gas volume of all gas pipeline sections directly connected to user d;

[0162] The meaning of the user's actual production constraint condition is that the actual gas volume of user d will fluctuate between 0 and its planned gas demand for the day. The specific value of the actual gas volume will be automatically obtained after calculation by the optimization model based on the system's deterministic risk situation.

[0163] The meaning of the user carbon emission constraint condition is that the actual carbon emission of user d will fluctuate between 0 and its maximum carbon emission per day. The specific value of the actual carbon emission will be automatically obtained after calculation by the optimization model based on the system deterministic risk situation.

[0164] The meaning of the mass conservation constraint condition of the distribution station is: the actual gas volume entering the station field z from the upstream is equal to the actual gas volume exported from the station field z to the downstream;

[0165] The meaning of the carbon emission constraint condition of the sub-transmission station is that the actual carbon emission of the sub-transmission station z will fluctuate between the value of 0 and its maximum carbon emission per day. The specific value of the actual carbon emission will be automatically obtained after calculation by the optimization model based on the system deterministic risk situation.

[0166] The actual production constraint of the gas pipeline section means that the actual flow rate of the gas pipeline section p will fluctuate between the value of 0 and the upper limit of the flow rate. The actual flow rate will be automatically calculated by the optimization model according to the system deterministic risk situation.

[0167] The meaning of the gas storage mass conservation constraint is: the difference between the actual gas injection and production volume of gas storage r is equal to the difference between the forward and reverse transmission volume of all gas transmission pipeline sections directly connected to gas storage r; the forward and reverse transmission directions of the gas transmission pipeline sections are set manually, and the pipeline transmission direction of gas injection into the gas storage is defined as the forward direction, and the pipeline transmission direction of gas production from the gas storage is defined as the reverse direction;

[0168] The actual production constraints of the gas storage reservoir mean that the actual gas injection and production volume of the gas storage reservoir r will fluctuate between the value of 0 and the upper limit of the output volume. The actual gas injection and production volume will be automatically obtained after calculation by the optimization model according to the system deterministic risk situation.

[0169] The meaning of the gas storage carbon emission constraint is that the actual carbon emission of gas storage r will fluctuate between 0 and its maximum carbon emission per day. The specific value of the actual carbon emission will be automatically obtained after calculation by the optimization model based on the system deterministic risk situation.

[0170] The meaning of the mass conservation constraint of the LNG receiving station is: the actual natural gas volume unloaded by the LNG receiving station t is equal to the sum of the throughput of all the unloading pipeline sections directly connected to the LNG receiving station t;

[0171] The actual production constraints of the LNG receiving station mean that the actual gas volume received and unloaded at the LNG receiving station t will fluctuate between the value of 0 and the upper limit of the volume, and the actual gas volume received and unloaded will be automatically calculated by the optimization model according to the system deterministic risk scenario;

[0172] The meaning of the carbon emission constraint condition of the LNG receiving station is: the actual carbon emission of the LNG receiving station t will fluctuate between the value of 0 and its maximum carbon emission per day. The specific value of the actual carbon emission will be automatically obtained after calculation by the optimization model based on the system deterministic risk scenario;

[0173] Step S13: inputting the deterministic risk factors into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

[0174] In this embodiment, a target solver is selected from preset solvers; the solvers include a CPLEX solver and a Gurobi solver; the deterministic risk factors are input into the natural gas pipeline system optimization model, and solved in combination with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

[0175] In this embodiment, after constructing a natural gas pipeline system optimization model including objective functions and constraints, the deterministic risk situation of the pipeline system can be solved by commercial solvers, such as CPLEX solver, Gurobi solver, etc., and finally the optimal solution under the deterministic risk situation can be obtained. At the same time, it takes into account the purpose of satisfying the gas consumption of system users and reducing the carbon emissions of the system, and provides scientific and reasonable guidance and suggestions for the formulation and disposal of emergency plans for deterministic risk situations of the pipeline system. This method starts from the overall perspective of the natural gas pipeline system, comprehensively covers the research fields of upstream, midstream and downstream, and has the ability to analyze and optimize pipelines at all levels at different scales. It is expected to become a practical tool for pipeline system operation and maintenance personnel, providing scientific, reasonable and effective guidance to ensure the stable operation of the pipeline system, improve reliability and enhance resilience.

[0176] This application has the following advantages: significant universality. In view of the common similarities in the random risk scenarios faced by the natural gas pipeline network system, the random risk resilience optimization method considering carbon emission factors proposed by this method is applicable to natural gas pipeline networks of various scales and complexity levels, and has significant universality; high foresight. This method comprehensively considers the carbon emission problem in the operation process of the natural gas pipeline network system, which is consistent with the current trend of "energy saving and carbon reduction" and "carbon neutrality", conforms to the development trend of the times, and shows outstanding advancement and foresight; strong feasibility and practicality. The deterministic risk resilience optimization method of the natural gas pipeline network considering carbon emissions proposed in this invention deeply analyzes the historical deterministic risk situation of the pipeline network system. On this basis, by constructing and solving the optimization model, the actual inlet and outlet gas volume and carbon emissions of each component unit of the pipeline network system can be accurately calculated. This method has a high degree of functional subdivision and is closely integrated with the operation practice of the pipeline network system, so it has high feasibility and practicality.

[0177] This application combines complex network theory, risk identification and analysis theory, and optimization theory to sort out the historical deterministic risk situations of the pipeline network system, trace the root of the risk and perform optimization calculations to solve it, and provide scientific, reasonable, and effective suggestions for the formulation and disposal of system deterministic risk emergency plans. In addition, it can also provide a reference for the design and improvement of the natural gas pipeline network system, optimize the topological structure of the pipeline network system, and improve its operating efficiency and safety.

[0178] In this embodiment, the fishbone diagram method and the risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified to obtain the corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors and human damage risk factors; a natural gas pipeline network system optimization model including objective functions and constraints is constructed; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints; the deterministic risk factors are input into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under deterministic risk factors. The present application combines the fishbone diagram method and the risk logic tree method to identify the risk factors of the natural gas pipeline network system to be identified. It can identify the risk factors of the historical risk situations of the pipeline network system, trace the causes of the risks, and obtain the corresponding deterministic risk factors. It is applicable to natural gas pipeline networks of various sizes and complexity levels, and constructs a natural gas pipeline network system optimization model including objective functions and constraints. The constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, and gas pipeline section actual production constraints. Production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emissions constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emissions constraints, solve the problem of lack of specific quantification of carbon emissions of the natural gas pipeline system under deterministic risks, and consider multiple risk aspects and multiple key factors, input the deterministic risk factors into the natural gas pipeline system optimization model, and obtain risk identification emergency plans and guidance suggestions under deterministic risk factors, thereby realizing risk identification of the natural gas pipeline network and optimization of the system topology, which has strong universality, feasibility and practicality.

[0179] See also Figure 6 As shown, the embodiment of the present invention discloses a deterministic risk identification device for a natural gas pipeline network system, which may specifically include:

[0180] The risk factor identification module 11 is used to identify the risk factors of the natural gas pipeline network system to be identified by using the fishbone diagram method and the risk logic tree method to obtain the corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors and human damage risk factors;

[0181] The optimization model construction module 12 is used to construct a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints;

[0182] The plan and suggestion determination module 13 is used to input the deterministic risk factors into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

[0183] In this embodiment, the fishbone diagram method and the risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified to obtain the corresponding deterministic risk factors; the deterministic risk factors include equipment risk factors, geological disaster risk factors and human damage risk factors; a natural gas pipeline network system optimization model including objective functions and constraints is constructed; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints; the deterministic risk factors are input into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under deterministic risk factors. The present application combines the fishbone diagram method and the risk logic tree method to identify the risk factors of the natural gas pipeline network system to be identified. It can identify the risk factors of the historical risk situations of the pipeline network system, trace the causes of the risks, and obtain the corresponding deterministic risk factors. It is applicable to natural gas pipeline networks of various sizes and complexity levels, and constructs a natural gas pipeline network system optimization model including objective functions and constraints. The constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, and gas pipeline section actual production constraints. Production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emissions constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emissions constraints, solve the problem of lack of specific quantification of carbon emissions of the natural gas pipeline system under deterministic risks, and consider multiple risk aspects and multiple key factors, input the deterministic risk factors into the natural gas pipeline system optimization model, and obtain risk identification emergency plans and guidance suggestions under deterministic risk factors, thereby realizing risk identification of the natural gas pipeline network and optimization of the system topology, which has strong universality, feasibility and practicality.

[0184] In some specific embodiments, the risk factor identification module 11 may specifically include:

[0185] The risk identification and risk tracing module is used to combine the fishbone diagram method and the risk logic tree method, and to identify and trace the risks of the natural gas pipeline network system to be identified, so as to obtain the components with risk sources;

[0186] The sorting module is used to sort the components with risk sources to obtain corresponding deterministic risk factors.

[0187] In some specific embodiments, the objective function is:

[0188] ;

[0189] in, is the objective function, is the user supply priority weight coefficient, the value range is [0,1], D is the total number of users in the system, is the gas supply priority coefficient of user d, is the actual gas volume obtained by user d under the system deterministic risk, is the planned gas volume of user d under system deterministic risk, is the actual carbon emissions of user d under system deterministic risk, is the maximum carbon emission of user d under system deterministic risk, S is the total number of gas sources in the pipeline network system, is the actual carbon emission of gas source s under system deterministic risk, is the maximum carbon emission of gas source s under system deterministic risk, Z is the total number of distribution stations in the system, is the actual carbon emissions of substation z under system deterministic risk, is the maximum carbon emission of distribution station z under system deterministic risk, R is the total number of gas storages in the system, is the actual carbon emissions of gas storage r under system deterministic risk, is the maximum carbon emission of gas storage r under system deterministic risk, T is the total number of liquefied natural gas receiving stations in the system, is the actual carbon emissions of LNG receiving station t under system deterministic risk, is the maximum carbon emission of LNG receiving station t under system deterministic risk.

[0190] In some specific embodiments, the gas source mass conservation constraint condition is:

[0191] ;

[0192] in, is the actual gas supply of gas source s, is the sum of the gas flow rates of all gas pipeline sections connected to the gas source s, is the gas pipeline section starting from the gas source s;

[0193] The actual production constraints of the gas source are:

[0194] ;

[0195] in, and are the upper and lower limits of the gas supply capacity of the gas source s respectively;

[0196] The gas source carbon emission constraint condition is:

[0197] ;

[0198] in, is the actual carbon emission of gas source s, is the maximum daily carbon emission of gas source s;

[0199] The user quality conservation constraint is:

[0200] ;

[0201] in, is the actual gas volume obtained by user d, is the sum of the gas flow rates of all gas pipeline sections directly connected to user d, is the gas pipeline section with user d as the terminal;

[0202] The actual production constraints of the user are:

[0203] ;

[0204] in, is the daily planned gas demand of user d;

[0205] The user carbon emission constraint condition is:

[0206] ;

[0207] in, is the maximum carbon emission per day of user d;

[0208] The mass conservation constraint condition of the sub-transmission station is:

[0209] ;

[0210] in, is the actual gas volume entering station z from upstream, is the gas pipeline section with station z as the terminal, is the actual gas volume exported from station z to the downstream, is the gas pipeline section starting from station z;

[0211] The carbon emission constraint conditions of the sub-transmission station are:

[0212] ;

[0213] in, is the actual carbon emission of substation z under the system deterministic risk scenario, is the maximum carbon emission of substation z under the system deterministic risk scenario;

[0214] The actual production constraints of the gas pipeline section are:

[0215] ;

[0216] in, is the actual flow rate of gas pipeline section p, is the maximum output of the gas pipeline section p;

[0217] The gas storage mass conservation constraint is:

[0218] ;

[0219] in, is the actual injection of gas storage r, is the gas production of gas storage r, is the positive flow rate of all gas pipeline sections directly connected to the gas storage r, The reverse flow of all gas pipeline sections directly connected to the gas storage r;

[0220] The actual production constraints of the gas storage are:

[0221] ;

[0222] ;

[0223] in, , is the actual gas injection and production volume of gas storage r, , is the maximum gas injection and production volume of gas storage r;

[0224] The carbon emission constraints of the gas storage are:

[0225] ;

[0226] in, is the actual carbon emission of gas storage r under the system deterministic risk scenario, is the maximum carbon emission of gas storage r under the system deterministic risk scenario;

[0227] The mass conservation constraint condition of the liquefied natural gas receiving station is:

[0228] ;

[0229] in, is the actual amount of natural gas unloaded by LNG receiving station t, is the sum of the throughput of all pipeline sections directly connected to the LNG receiving station t, It is the connection and unloading pipe section with the liquefied natural gas receiving station t as the terminal;

[0230] The actual production constraints of the liquefied natural gas receiving station are:

[0231] ;

[0232] in, is the maximum gas loading and unloading volume of LNG receiving station t;

[0233] The carbon emission constraints of the liquefied natural gas receiving station are:

[0234] ;

[0235] in, is the actual carbon emissions of LNG receiving station t under the system deterministic risk scenario, is the maximum carbon emission of LNG receiving station t under the system deterministic risk scenario.

[0236] In some specific embodiments, the plan and suggestion determination module 13 may specifically include:

[0237] A screening module, used for screening out a target solver from preset solvers; the solvers include CPLEX solver and Gurobi solver;

[0238] A solution module is used to input the deterministic risk factors into the natural gas pipeline system optimization model, and solve it in combination with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

[0239] In some specific embodiments, the risk factor identification module 11 includes:

[0240] The risk identification and risk tracing module is used to combine the fishbone diagram method and the risk logic tree method, and to identify and trace the risks of the natural gas pipeline network system to be identified, so as to obtain the components with risk sources;

[0241] The sorting module is used to sort the components with risk sources to obtain corresponding deterministic risk factors.

[0242] In some specific embodiments, the plan and suggestion determination module 13 includes:

[0243] A solver screening module, used to screen out a target solver from preset solvers; the solvers include CPLEX solver and Gurobi solver;

[0244] A solution module is used to input the deterministic risk factors into the natural gas pipeline system optimization model, and solve it in combination with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

[0245] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the deterministic risk identification method for a natural gas pipeline network system performed by an electronic device disclosed in any of the aforementioned embodiments.

[0246] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0247] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0248] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the natural gas pipeline system deterministic risk identification method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from an external device received by the natural gas pipeline system deterministic risk identification device, the data 223 can also include data collected by its own input and output interface 25, etc.

[0249] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0250] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the deterministic risk identification method for the natural gas pipeline system disclosed in any of the aforementioned embodiments are implemented.

[0251] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0252] The above is a detailed introduction to the deterministic risk identification method, device, equipment and storage medium for a natural gas pipeline system provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for identifying deterministic risks of a natural gas pipeline network system, characterized in that: include: The fishbone diagram method and risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified, so as to obtain the corresponding deterministic risk factors; The deterministic risk factors include equipment risk factors, geological disaster risk factors and man-made damage risk factors; Constructing a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints; The deterministic risk factors are input into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

2. The method for identifying deterministic risks of a natural gas pipeline network system according to claim 1, characterized in that: The fishbone diagram method and the risk logic tree method are used to identify the risk factors of the natural gas pipeline network system to be identified, so as to obtain the corresponding deterministic risk factors, including: Combine the fishbone diagram method with the risk logic tree method, and perform risk identification and risk tracing on the natural gas pipeline network system to be identified, so as to obtain the components with risk sources; The components having risk sources are sorted out to obtain corresponding deterministic risk factors.

3. The method for identifying deterministic risks of a natural gas pipeline network system according to claim 1, characterized in that: The objective function is: ； in, is the objective function, is the user supply priority weight coefficient, the value range is [0,1], D is the total number of users in the system, is the gas supply priority coefficient of user d, is the actual gas volume obtained by user d under the system deterministic risk, is the planned gas volume of user d under system deterministic risk, is the actual carbon emissions of user d under system deterministic risk, is the maximum carbon emission of user d under system deterministic risk, S is the total number of gas sources in the pipeline network system, is the actual carbon emission of gas source s under system deterministic risk, is the maximum carbon emission of gas source s under system deterministic risk, Z is the total number of distribution stations in the system, is the actual carbon emissions of substation z under system deterministic risk, is the maximum carbon emission of distribution station z under system deterministic risk, R is the total number of gas storages in the system, is the actual carbon emissions of gas storage r under system deterministic risk, is the maximum carbon emission of gas storage r under system deterministic risk, T is the total number of liquefied natural gas receiving stations in the system, is the actual carbon emissions of LNG receiving station t under system deterministic risk, is the maximum carbon emission of LNG receiving station t under system deterministic risk.

4. The method for identifying deterministic risks of a natural gas pipeline network system according to claim 1, characterized in that: The gas source mass conservation constraint condition is: ； in, is the actual gas supply of gas source s, is the sum of the gas flow rates of all gas pipeline sections connected to the gas source s, is the gas pipeline section starting from the gas source s; The actual production constraints of the gas source are: ； in, and are the upper and lower limits of the gas supply capacity of the gas source s respectively; The gas source carbon emission constraint condition is: ； in, is the actual carbon emission of gas source s, is the maximum daily carbon emission of gas source s; The user quality conservation constraint is: ； in, is the actual gas volume obtained by user d, is the sum of the gas flow rates of all gas pipeline sections directly connected to user d, is the gas pipeline section with user d as the terminal; The actual production constraints of the user are: ； in, is the daily planned gas demand of user d; The user carbon emission constraint condition is: ； in, is the maximum carbon emission per day of user d; The mass conservation constraint condition of the sub-transmission station is: ； in, is the actual gas volume entering station z from upstream, is the gas pipeline section with station z as the terminal, is the actual gas volume exported from station z to the downstream, is the gas pipeline section starting from station z; The carbon emission constraint conditions of the sub-transmission station are: ； in, is the actual carbon emission of substation z under the system deterministic risk scenario, is the maximum carbon emission of substation z under the system deterministic risk scenario; The actual production constraints of the gas pipeline section are: ； in, is the actual flow rate of gas pipeline section p, is the maximum output of the gas pipeline section p; The gas storage mass conservation constraint is: ； in, is the actual injection of gas storage r, is the gas production of gas storage r, is the positive flow rate of all gas pipeline sections directly connected to the gas storage r, The reverse flow of all gas pipeline sections directly connected to the gas storage r; The actual production constraints of the gas storage are: ； ； in, , is the actual gas injection and production volume of gas storage r, , is the maximum gas injection and production volume of gas storage r; The carbon emission constraints of the gas storage are: ； in, is the actual carbon emission of gas storage r under the system deterministic risk scenario, is the maximum carbon emission of gas storage r under the system deterministic risk scenario; The mass conservation constraint condition of the liquefied natural gas receiving station is: ； in, is the actual amount of natural gas unloaded by LNG receiving station t, is the sum of the throughput of all pipeline sections directly connected to the LNG receiving station t, It is the connection and unloading pipe section with the liquefied natural gas receiving station t as the terminal; The actual production constraints of the liquefied natural gas receiving station are: ； in, is the maximum gas loading and unloading volume of LNG receiving station t; The carbon emission constraints of the liquefied natural gas receiving station are: ； in, is the actual carbon emissions of LNG receiving station t under the system deterministic risk scenario, is the maximum carbon emission of LNG receiving station t under the system deterministic risk scenario.

5. The method for identifying deterministic risks of a natural gas pipeline network system according to any one of claims 1 to 4, characterized in that: The inputting of the deterministic risk factors into the natural gas pipeline network system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors includes: Filtering a target solver from preset solvers; the solvers include a CPLEX solver and a Gurobi solver; The deterministic risk factors are input into the natural gas pipeline system optimization model, and solved in conjunction with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

6. A deterministic risk identification device for a natural gas pipeline network system, characterized in that: include: The risk factor identification module is used to identify the risk factors of the natural gas pipeline network system to be identified by using the fishbone diagram method and the risk logic tree method to obtain the corresponding deterministic risk factors; The deterministic risk factors include equipment risk factors, geological disaster risk factors and man-made damage risk factors; An optimization model construction module is used to construct a natural gas pipeline system optimization model including an objective function and constraints; the constraints include gas source quality conservation constraints, gas source actual production constraints, gas source carbon emission constraints, user quality conservation constraints, user actual production constraints, user carbon emission constraints, sub-transmission station quality conservation constraints, sub-transmission station carbon emission constraints, gas pipeline section actual production constraints, gas storage quality conservation constraints, gas storage actual production constraints, gas storage carbon emission constraints, liquefied natural gas receiving station quality conservation constraints, liquefied natural gas receiving station actual production constraints and liquefied natural gas receiving station carbon emission constraints; The plan and suggestion determination module is used to input the deterministic risk factors into the natural gas pipeline system optimization model to obtain risk identification emergency plans and guidance suggestions under the deterministic risk factors.

7. The natural gas pipeline network system deterministic risk identification device according to claim 6, characterized in that: The risk factor identification module includes: The risk identification and risk tracing module is used to combine the fishbone diagram method and the risk logic tree method, and to identify and trace the risks of the natural gas pipeline network system to be identified, so as to obtain the components with risk sources; The sorting module is used to sort out the components with risk sources to obtain corresponding deterministic risk factors.

8. The natural gas pipeline network system deterministic risk identification device according to claim 6, characterized in that: The plan and suggestion determination module includes: A solver screening module, used to screen out a target solver from preset solvers; the solvers include CPLEX solver and Gurobi solver; A solution module is used to input the deterministic risk factors into the natural gas pipeline system optimization model, and solve it in combination with the target solver to obtain the optimal risk identification emergency plan and guidance suggestions under the deterministic risk factors.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, used to execute the computer program to implement the deterministic risk identification method for a natural gas pipeline network system as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: Used to store computer programs; wherein, when the computer program is executed by a processor, the method for identifying deterministic risks in a natural gas pipeline network system as described in any one of claims 1 to 5 is implemented.

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