Natural gas industry chain uncertainty analysis and risk mining method and device

Abstract

The invention provides a natural gas industry chain uncertainty analysis and risk mining method and device, and the method comprises the steps: building a topological structure model according to an industry chain; on the basis of the pipe network topological structure model, representing uncertainty in a natural gas industry chain; and a multi-dimensional risk mining index is introduced to carry out analysis and risk mining on the represented uncertainty. According to the method, the topological structure model is established, so that visual understanding of the overall structure of the industrial chain is provided, and a foundation is laid for further data analysis, risk assessment and system optimization; according to the method, a Metropolis-Hastings sampling algorithm based on a Markov Monte Carlo chain is adopted, and the fitting degree of model operation and actual production and life is improved through a probability distribution fitting and random sampling method; and by setting a reasonable constraint condition, the algorithm complexity is reduced, and the problem of easy falling into a local optimal solution during solving is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of natural gas industry chain, and particularly relates to a natural gas industry chain uncertainty analysis and risk mining method and device. BACKGROUND

[0002] With the development of global economy, energy demand continues to grow, and natural gas, as a clean and efficient energy, has become an important part of the global energy structure. However, there are many uncertain factors in the exploration, development, production, storage, transportation and use of natural gas industry chain, such as geological conditions, market fluctuations, policy adjustments, etc., which have a significant impact on the stable operation of natural gas industry chain and the economic benefits of enterprises. Therefore, it is of great significance to analyze the uncertainty of natural gas industry chain and mine the risk for the stable operation of natural gas industry chain and the sustainable development of enterprises.

[0003] In the prior art, researchers at home and abroad have carried out a series of researches on the uncertainty analysis and risk mining of natural gas industry chain. The main methods include statistical analysis based on historical data, optimization method based on mathematical model, and prediction technology based on artificial intelligence.

[0004] Specifically, the existing technology has the following problems: the statistical analysis method depends on a large amount of historical data and cannot consider the sudden situation that may occur in the future, resulting in inaccurate prediction results. The present application needs to solve how to improve the prediction accuracy and reduce the dependence on historical data; the mathematical model optimization method is complex in calculation, difficult to select parameters, and cannot be applied to nonlinear, multivariate and non-stationary natural gas industry chain. The present application needs to solve how to simplify the calculation process and improve the applicability and accuracy of the model; although the artificial intelligence technology can handle nonlinear, multivariate and non-stationary systems, it has high algorithm complexity and large amount of calculation, and is easy to fall into local optimal solution. The present application needs to solve how to reduce the algorithm complexity, reduce the calculation amount and avoid local optimal solution.

[0005] The existing technology lacks comprehensive consideration of the overall, dynamic and uncertain nature of natural gas industry chain, resulting in poor analysis results and risk mining effect; in actual application, the existing technology often needs to be operated by professional personnel, and the operation is complex, which is not conducive to real-time and rapid risk management of enterprises in daily operation. SUMMARY

[0006] The present application provides a natural gas industry chain uncertainty analysis and risk mining method, which comprises,

[0007] establishing a pipe network topology structure model according to the natural gas industry chain;

[0008] Based on the pipeline topology model, the uncertainties in the natural gas industry chain are characterized;

[0009] Multi-dimensional risk mining indicators are introduced to analyze and mine the uncertainty represented.

[0010] Furthermore, the pipeline topology model includes treating the natural gas industry chain as a topology composed of nodes and edges, and establishing a mathematical model using graph theory.

[0011] Furthermore,

[0012] The nodes in the pipeline topology represent unit nodes in the natural gas industry chain.

[0013] The edges in the pipeline network topology represent gas transmission pipelines in the natural gas industry chain.

[0014] Furthermore, the characterization of uncertainties in the natural gas industry chain includes:

[0015] Collect and analyze historical operating data of the natural gas industry chain, and fit the probability distribution of the historical operating data to obtain the probability distribution function;

[0016] By combining the probability distribution function, a sampling algorithm is used to randomly sample historical operating data;

[0017] Set constraints and objective functions for unit nodes and connections in the natural gas industry chain;

[0018] Based on random sampling data, constraints, and objective functions, a solver is used to solve the problem, and the solution results are used to characterize the uncertainties in the natural gas industry chain.

[0019] Furthermore, the unit nodes in the natural gas industry chain include gas source nodes, user demand nodes, distribution station nodes, gas storage nodes, and LNG receiving station nodes.

[0020] Furthermore, the random sampling of historical operating data includes using the Metropolis-Hastington sampling algorithm based on Markov Monte Carlo chains.

[0021] Furthermore, the constraints include: gas source node constraints, user demand node constraints, gas storage node constraints, distribution station node constraints, LNG receiving station node constraints, and gas pipeline constraints.

[0022] The constraints on the gas source nodes include upper and lower limit constraints, material balance constraints, and contract volume target planning constraints.

[0023] The user demand node constraint condition comprises upper and lower limit constraint conditions, material balance conditions and contract quantity target planning constraint conditions of the user demand node;

[0024] The gas storage node constraint condition comprises material balance constraint conditions and target planning constraint conditions of the gas storage node;

[0025] The sub-transmission station node constraint condition comprises material balance constraint conditions of the sub-transmission station node;

[0026] The LNG receiving station node constraint condition comprises material balance constraint conditions and target planning constraint conditions of the LNG receiving station node;

[0027] The gas transmission pipeline constraint condition comprises pipeline positive transmission capacity constraint conditions and pipeline reverse transmission capacity constraint conditions;

[0028] The set target function is to calculate the deviation between the actual demand quantity and the planned gas quantity of each user demand node, and the set formula is,

[0029]

[0030] Wherein, ω i represents the weight of each user, Q si represents the actual gas quantity of user i after solving, Q di represents the planned gas quantity of user i.

[0031] Further,

[0032] The upper and lower limit constraint conditions of the gas source node limit the operation range of the gas source node, which is represented as,

[0033] Q i min ≤Q i supply ≤Q i max

[0034] Wherein, Q i min represents the minimum supply quantity, i.e. the lower limit of the supply quantity, Q i supply represents the supply quantity, Q i max represents the maximum supply quantity, i.e. the upper limit of the supply quantity;

[0035] The material balance constraint condition of the gas source node requires that the natural gas quantity of the gas source supply quantity and the gas source outflow quantity be equal, which is represented as,

[0036] Q i in =Q iout

[0037] where Q i in represents the natural gas supply amount of the ith gas source node, Q i out represents the natural gas amount of the jth outflow gas source;

[0038] The contract amount target planning constraint condition of the gas source node ensures the relationship between the actual supply amount and the planned supply amount, the positive deviation amount and the negative deviation amount, which is represented as,

[0039] Q i plan +Q i pos -Q i neg =Q i optimized

[0040] where Q i plan represents the planned supply amount of natural gas, Q i pos represents the positive deviation amount (e.g. additional supply), Q i neg represents the negative deviation amount (e.g. insufficient supply), Q i optimized represents the optimized actual supply amount;

[0041] The upper and lower limit constraint condition of the user demand node limits the use range of the user node, which is represented as,

[0042] Q i min ≤Q i demand ≤Q i max

[0043] where Q i min represents the minimum demand amount, i.e. the lower limit of the demand amount, Q i demand represents the planned demand amount, Q i max represents the maximum demand amount, i.e. the upper limit of the demand amount;

[0044] The material balance constraint condition of the user demand node requires that the user demand amount and the natural gas amount flowing into the user be equal, which is represented as,

[0045] Q i demand =Q i infiow

[0046] where Q i demand represents the natural gas demand of the user, Q i inflow represents the natural gas flow into the user;

[0047] The contract quantity target planning constraint condition of the user demand node ensures the relationship between the actual supply quantity and the user planned supply quantity, the positive deviation quantity and the negative deviation quantity, which is expressed as,

[0048] Q i plan_supplu +Q i pos_deviation -Q i neg_deviation =Q i optimized

[0049] where Q i plan_supply represents the natural gas quantity planned to be obtained by the user, Q i pos_deviation represents the positive deviation quantity (for example, additional supply), Q i neg_deviation represents the negative deviation quantity (for example, insufficient supply), Q i optimized represents the optimized actual supply quantity;

[0050] The material balance constraint condition of the gas storage node is that the actual gas injection quantity of the gas storage minus the actual gas production quantity is equal to the positive pipeline transportation quantity of the gas storage minus the reverse pipeline transportation quantity of the gas storage, which is expressed as,

[0051] Q i storage_inj -Q i storage_with =Q i storage_forward -Q i storage_reverse

[0052] where Q i inj represents the actual gas injection quantity of the gas storage, Q i with represents the actual gas production quantity of the gas storage, Q i forward represents the positive pipeline transportation quantity of the gas storage, Q i reverse represents the reverse pipeline transportation quantity of the gas storage;

[0053] The target planning constraint condition of the gas storage node ensures the relationship between the actual gas quantity of the gas storage and the planned quantity of the gas storage, the positive deviation quantity and the negative deviation quantity, which is expressed as,

[0054] Q i storage_plan +ΔQ i storage_pos -ΔQ i storage_neg =Q i storage_actual

[0055] wherein Q i plan represents the planned amount of the gas storage, ΔQ i pos represents the positive deviation amount, ΔQ i neg represents the negative deviation amount, Q i actual represents the actual gas amount of the gas storage;

[0056] The material balance constraint condition of the LNG receiving station node is that the unloading amount of the LNG receiving station is equal to the amount of natural gas delivered into the system, which is expressed as,

[0057] Q i LNG =Q i gas

[0058] wherein Q i LNG represents the unloading amount of the LNG receiving station, Q i gas represents the amount of natural gas delivered into the system;

[0059] The target planning constraint condition of the LNG receiving station node ensures the relationship between the actual gas amount of the LNG receiving station and the planned amount, the positive deviation amount and the negative deviation amount of the LNG receiving station, which is expressed as,

[0060] Q i tank_plan +ΔQ i tank_pos -ΔQ i tank_neg =Q i tank_actual

[0061] wherein Q i tank_plan represents the planned amount of the LNG receiving station, ΔQ i tank_pos represents the positive deviation amount, ΔQ i tank_neg represents the negative deviation amount, Q i tank_actual represents the actual gas amount of the LNG receiving station;

[0062] The material balance constraint condition of the distribution station node is the gas amount flowing into the distribution station = the gas amount flowing out of the distribution station, which is expressed as,

[0063] (Q up_fwd -Q up_rev )+Q up_source +Q storage_with +Q LNG_to_station =

[0064] (Q down_fwd -Q down_rev )+Q down_demand +Q storage_inj +Q station_to_LNG

[0065] wherein Q up_fwd represents the positive flow of the upstream real pipe section, Q up_rev represents the negative flow of the upstream real pipe section, Q up_source represents the pipe flow of the upstream gas source pipe section, Q storage_with represents the actual gas production of the gas storage, Q LNG_to_station represents the gas amount transported from the field station to the LNG receiving station, Q down_fwd represents the positive flow of the downstream real pipe section, Q down_rev represents the negative flow of the downstream real pipe section, Q down_demand represents the pipe flow of the downstream demand pipe section, Q storage_inj represents the actual gas injection of the gas storage, Q station_to_LNG represents the gas amount transported from the field station to the LNG receiving station;

[0066] The pipeline positive flow capacity constraint condition of the gas transmission pipeline ensures that the pipe section positive flow capacity is within the upper and lower limits of the pipeline positive flow capacity, and considers the positive excess deviation value, which is expressed as,

[0067]

[0068] wherein Q i pipeline_forward_capacity_min represents the lower limit of the pipeline positive flow capacity, Q i segment_forward_capacity represents the pipe section positive flow capacity, Q i pipeline_forward_capacity_max represents the upper limit of the pipeline positive flow capacity, AQ i forward_excess represents the positive excess deviation value, i.e. the difference between the upper limit of the pipeline positive flow capacity and the actual pipe section positive flow capacity;

[0069] The pipeline negative flow capacity constraint condition of the gas transmission pipeline ensures that the pipe section negative flow capacity is within the upper and lower limits of the pipeline negative flow capacity, and considers the negative excess deviation value, which is expressed as,

[0070]

[0071] wherein Q i pipeline_reverse_capacity_min represents the lower limit of the reverse delivery capacity of the pipeline, Q i segment_reverse_capacity represents the reverse delivery capacity of the pipe section, Q i pipeline_reverse_capacity_max represents the upper limit of the reverse delivery capacity of the pipeline, AQ i reverse_excess represents the reverse excess deviation value, i.e. the difference between the upper limit of the reverse delivery capacity of the pipeline and the actual reverse delivery capacity of the pipe section.

[0072] Further, the dimensions include a time dimension, a gas volume dimension, and a reliability dimension;

[0073] When the time dimension is introduced, the risk mining index of gas supply satisfaction is used, i.e. the ratio of the time of planned gas supply or gas obtaining of the unit node or the natural gas industry chain to the running time;

[0074] When the gas volume dimension is introduced, the risk mining index of gas supply guarantee degree is used, i.e. the ratio of the actual gas supply volume or gas obtaining volume in the running time to the planned gas supply volume or gas obtaining volume of the unit node or the natural gas industry chain;

[0075] When the reliability dimension is introduced, the risk mining indexes of system average gas shortage time and system average gas supply guarantee frequency are used to analyze the influence of the gas supply shortage caused by the uncertainty factors on the natural gas industry chain.

[0076] A natural gas industry chain uncertainty analysis and risk mining device, characterized in that the device comprises a topological structure representation module, an uncertainty representation module, and an analysis and mining module;

[0077] The topological structure representation module is used to establish a pipe network topological structure model of the natural gas industry chain;

[0078] The uncertainty representation module is used to represent the uncertainty in the natural gas industry chain;

[0079] The analysis and mining module is used to analyze the uncertainty of the natural gas industry chain and mine potential risks, helping to identify the key nodes, bottlenecks, and potential risk points in the natural gas industry chain.

[0080] Compared with the prior art, the present application has the following advantages:

[0081] 1. This invention employs a topology model, which not only provides an intuitive understanding of the overall structure of the natural gas industry chain but also lays the foundation for further data analysis, risk assessment, and system optimization. Through in-depth research of the topology model, potential bottlenecks and failure points in the system can be identified, allowing for timely solutions and thereby improving the stability, security, and efficiency of the natural gas industry chain.

[0082] 2. The Metropolis-Hastings sampling algorithm based on Markov Monte Carlo chains was adopted. By using probability distribution fitting and random sampling, the model calculations were made more consistent with actual production and life, and the reliance on a large amount of historical data was reduced.

[0083] 3. This invention also reduces the complexity of the algorithm by setting reasonable constraints, thus avoiding the problem of easily getting trapped in local optima during the solution process.

[0084] 4. This invention comprehensively considers the integrity, dynamism, and uncertainty of the natural gas industry chain. By setting an objective function, it can accurately identify key nodes and risk points in the system. It is easy to operate, facilitating enterprises to conduct risk management in real time and quickly. Through continuous monitoring and analysis, it can promptly discover and respond to potential risks, ensuring the stable operation and efficient functioning of the system.

[0085] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 A schematic diagram of the process for uncertainty analysis and risk mining in the natural gas industry chain according to the present invention is shown.

[0088] Figure 2 An example diagram of the pipeline topology of the natural gas industry chain established in the embodiment is shown.

[0089] Figure 3 A schematic diagram of the Metropolis-Hastings sampling algorithm used in this invention is shown.

[0090] Figure 4 A schematic diagram of a natural gas industry chain uncertainty analysis and risk mining device according to the present invention is shown. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] like Figure 1 As shown, a method for uncertainty analysis and risk mining in the natural gas industry chain includes: S1, establishing a pipeline topology model based on the natural gas industry chain.

[0093] By analyzing the pipeline topology of the natural gas industry chain, key nodes and critical paths can be identified, thereby assessing the system's stability and resilience. Topology characterization can also help discover potential leaks, bottlenecks, and failure points in the pipeline network, providing important scientific evidence for uncertainty analysis and risk assessment.

[0094] like Figure 2 As shown, the natural gas industry chain is first regarded as a mathematical model, and then abstracted into a topological structure composed of nodes and edges using graph theory.

[0095] In this topology, each node represents a unit node in the natural gas industry chain, and each connecting edge represents a gas transmission pipeline in the natural gas industry chain.

[0096] Alternatively, the unit nodes in the natural gas industry chain can be divided into gas source nodes, user demand nodes, distribution station nodes, gas storage nodes, and LNG receiving station nodes.

[0097] The gas source node is the starting point of the natural gas industry chain, usually located at the natural gas production site or import point; it represents the starting point of the natural gas industry chain from the extraction or import point of underground reservoirs or other resource areas.

[0098] Optionally, the gas source node includes natural gas wells, pipeline connections, or liquefied natural gas (LNG) import terminals. In addition, the gas source node also includes natural gas processing facilities to remove impurities such as hydrogen sulfide, carbon dioxide, and water, and to pressurize or cool the natural gas to conditions suitable for transportation.

[0099] The user demand node is the end point of the natural gas industry chain, representing the terminal or final consumption location of natural gas supply, usually located in cities, industrial areas or other energy demand centers.

[0100] The distribution station nodes are located in key positions in the natural gas industry chain and are used to transport natural gas from gas source nodes to user demand nodes. They are usually located at the intersection of gas pipelines or major transfer centers, and can realize the diversion, scheduling and control of natural gas by adjusting pressure and flow.

[0101] Optionally, the distribution station nodes include facilities such as pressure regulating stations, flow regulating stations, and valve stations to ensure the safe operation and stable supply of the natural gas industry chain.

[0102] The gas storage node is a gas storage facility in the natural gas industry chain, used to store and regulate the supply of natural gas. It is usually located underground or in groundwater layers, using underground gas storage tanks or underground gas storage domes to store natural gas in order to cope with the seasonal and diurnal fluctuations between natural gas supply and demand.

[0103] Alternatively, gas storage nodes play an important buffering and regulating role in the natural gas industry chain, enabling the release of stored natural gas during peak demand periods and its storage during off-peak demand periods.

[0104] The LNG receiving terminal node is a facility used to receive, store, and redistribute liquefied natural gas (LNG). It is typically located near the coastline or in port areas to receive LNG from LNG carriers at sea, convert it into gaseous natural gas, and then transport it through pipelines to other nodes in the natural gas industry chain.

[0105] Alternatively, LNG receiving terminal nodes play an important role in natural gas supply, especially in areas far from onshore natural gas resources, providing flexibility and diversity in natural gas supply.

[0106] Alternatively, in the pipeline network topology of the natural gas industry chain, in order to simplify the complex network relationships, gas transmission pipelines are usually abstracted as edges between nodes, which helps to reveal the overall structure and operation mechanism of the natural gas industry chain.

[0107] S2. After establishing the pipeline topology of the natural gas industry chain, the uncertainties in the natural gas industry chain are characterized.

[0108] S2.1 Collect gas source node supply data and user node gas demand and usage during historical operating periods, and perform probability distribution fitting on the historical operating data of gas source nodes and user nodes to help understand and describe the changes and fluctuations between gas source node supply and user node demand, thereby better grasping the uncertainties of the natural gas industry chain.

[0109] Alternatively, after fitting the probability distribution, various probability distributions can be obtained, such as the normal distribution, Poisson distribution, or exponential distribution.

[0110] S2.2, such as Figure 3 As shown, based on the function obtained after fitting the probability distribution, the Metropolis-Hastings sampling algorithm based on Markov Monte Carlo chains is used to randomly sample historical running data.

[0111] S2.2.1. For the probability density function p(θ) of the gas source node and user node obtained after fitting, a sequence is generated based on the Markov chain using the Metropolis-Hastings sampling algorithm. The sequence is represented as follows:

[0112] θ (0) →θ (1) →…θ (t) →…

[0113] Where, θ (t) This represents the state of the Markov chain at time t.

[0114] S2.2.2 The Metropolis-Hastings sampling algorithm first initializes the state value θ. (0) Then, by proposing the distribution q(θ|θ) (t-1) Generate a new candidate state θ (*) Based on a certain probability α, the sample chooses to accept or reject the new value until the sampling process converges. After convergence, the sample θ... (t) This refers to the sample of the demand-side probability density function p(θ), whose α is expressed by the formula:

[0115]

[0116] S2.3 Set constraints for each unit node and connection.

[0117] S2.3.1 The constraints of the gas source node include upper and lower limit constraints, material balance constraints, and contract volume target planning constraints.

[0118] The upper and lower limit constraints of the gas source node limit its operating range, ensuring its safe and economical operation. They are expressed as follows:

[0119] Q i min ≤Q l supply ≤Q i max

[0120] Among them, Qi min Q represents the minimum supply, i.e., the lower limit of the supply. i supply Q represents the supply quantity. i max This indicates the maximum supply, i.e., the upper limit of the supply.

[0121] The material balance constraint condition of the gas source node requires that the amount of natural gas supplied and the amount of natural gas flowing out of the gas source be equal to avoid resource waste or supply-demand imbalance. This is expressed as follows:

[0122] Q i in =Q i out

[0123] Among them, Q i in Q represents the natural gas supply of the i-th gas source node. i out This represents the amount of natural gas flowing out of the j-th gas source.

[0124] The contract volume target planning constraints for the gas source nodes ensure the relationship between actual supply and planned supply, positive deviation, and negative deviation, achieving the goals of supply and demand balance and resource optimization. This is expressed as follows:

[0125] Q i plan +Q i pos -Q i neg =Q i optimized

[0126] Among them, Q i plan Q represents the planned supply of natural gas. i pos Q represents the amount of positive deviation (e.g., additional supply). i neg Q represents a negative deviation (e.g., insufficient supply). i optimized This represents the optimized actual supply.

[0127] S2.3.2 The constraints of the user demand node include upper and lower limit constraints, material balance conditions, and contract quantity target planning constraints.

[0128] The upper and lower bound constraints of the user demand node limit the scope of its use, ensuring its safe and economical operation. They are expressed as follows:

[0129] Qi min ≤Q i demand ≤Q i max

[0130] Among them, Q i min Q represents the minimum demand, i.e., the lower limit of demand. i demand Q represents the planned demand. i max This indicates the maximum demand, i.e., the upper limit of demand.

[0131] The material balance constraint at the user demand node requires that the user demand and the amount of natural gas flowing into the user be equal, thus maintaining the stable operation of the natural gas supply chain. This constraint is expressed as follows:

[0132] Q i demand =Q i inflow

[0133] Among them, Q i demand Q represents the user's natural gas demand. i inflow This indicates the amount of natural gas flowing into the user's account.

[0134] The contract quantity target planning constraints of the user demand nodes ensure the relationship between the actual supply quantity and the user's planned supply quantity, positive deviation, and negative deviation, achieving supply and demand balance and resource optimization. This is expressed as follows:

[0135] Q i plan_supply +Q i pos_deviation -Q i neg_deviation =Q i optimized

[0136] Among them, Q i plan_supply Q represents the amount of natural gas the user plans to receive. i pos_deviation Q represents the amount of positive deviation (e.g., additional supply). i neg_deviation Q represents a negative deviation (e.g., insufficient supply). i optimized This represents the optimized actual supply.

[0137] S2.3.3 The gas storage node constraints include material balance constraints and target planning constraints.

[0138] The material balance constraint condition for the gas storage node is that the actual gas injection volume minus the actual gas extraction volume equals the forward pipeline flow rate minus the reverse pipeline flow rate. This describes the balance relationship of gas flow rate within the gas storage facility, and is expressed as follows:

[0139] Q i storage_inj -Q i storage_with =Q i storage_forward -Q i storage_reverse

[0140] Among them, Q i inj Q represents the actual amount of gas injected into the gas storage facility. i with Q represents the actual gas extraction volume of the gas storage facility. i forward Q represents the forward pipeline flow rate of the gas storage facility. i reverse This indicates the reverse pipeline transport volume of the gas storage facility.

[0141] The target planning constraints of the gas storage nodes ensure the relationship between the actual gas volume of the gas storage and the planned volume, positive deviation, and negative deviation, achieving supply and demand balance and resource optimization. This is expressed as follows:

[0142] Q i storage_plan +ΔQ i storage_pos -ΔQ i storage_neg =Q i storage_actual

[0143] Among them, Q i plan ΔQ represents the planned volume of the gas storage facility. i pos ΔQ represents the positive deviation. i neg Q represents the negative deviation. i actual This indicates the actual gas volume in the gas storage facility.

[0144] S2.3.4 The LNG receiving terminal node constraints include material balance constraints and target planning constraints.

[0145] The material balance constraint for the LNG receiving terminal node is that the amount of LNG unloaded at the receiving terminal equals the amount of natural gas supplied into the system, which is expressed as follows:

[0146] Q iLNG =Q i gas

[0147] Among them, Q i LNG Q represents the LNG receiving terminal's unloading volume. i gas This represents the amount of natural gas supplied to the system.

[0148] The target planning constraints of the LNG receiving terminal nodes ensure the relationship between the actual gas volume of the LNG receiving terminal and the planned volume, positive deviation, and negative deviation, achieving supply and demand balance and resource optimization. This is expressed as follows:

[0149] Q i tank_plan +ΔQ i tank_pos -ΔQ i tank_neg =Q i tank_actual

[0150] Among them, Q i tank_plan ΔQ represents the planned volume of the LNG receiving terminal. i tank_pos ΔQ represents the positive deviation. i tank_neg Q represents the negative deviation. i tank_actual This indicates the actual gas volume at the LNG receiving terminal.

[0151] S2.3.5, The constraints of the distribution station nodes include material balance constraints.

[0152] The material balance constraint condition for the distribution station node is: gas inflow into the distribution station = gas outflow from the distribution station, i.e., upstream actual pipeline forward flow - upstream actual pipeline reverse flow + upstream gas source pipeline flow + gas storage facility gas intake + gas flow from LNG receiving terminal to the terminal = downstream actual pipeline forward flow - downstream actual pipeline reverse flow + downstream demand pipeline flow + gas storage facility gas injection + gas flow from terminal to LNG receiving terminal, expressed as follows:

[0153] (Q up_fwd -Q up_rev )+Q up_source +Q storage_with +Q LNG_to_station =

[0154] (Q down_fwd -Q down_rev )+Q down_demand +Q storage_inj +Q station_to_LNG

[0155] Among them, Q up_fwd Q represents the forward flow rate of the actual upstream pipeline segment. up_rev Q represents the reverse flow rate of the actual upstream pipe segment. up_source Q represents the pipeline throughput of the upstream gas source section. storage_with Q represents the actual gas extraction volume of the gas storage facility. LNG_to_station Q represents the amount of gas delivered from the LNG receiving terminal to the LNG yard. down_fwd Q represents the forward flow rate of the actual downstream pipeline segment. down_rev Q represents the reverse flow rate of the actual downstream pipe section. down_demand Q represents the pipeline transport volume of the downstream demand section. storage_inj Q represents the actual amount of gas injected into the gas storage facility. station_to_LNG This indicates the amount of gas transported from the terminal to the LNG receiving terminal.

[0156] S2.3.6 The constraints on the gas pipeline include the forward transmission capacity constraints and the reverse transmission capacity constraints.

[0157] The pipeline's positive transport capacity constraint ensures that the pipeline segment's positive transport capacity remains within the upper and lower limits of the pipeline's positive transport capacity, while also considering positive excess deviation values, which are expressed as follows:

[0158]

[0159] Among them, Q i pipeline_forward_capacity_min Q represents the lower limit of the pipeline's positive transport capacity. i segment_forward_capacity Q represents the positive transport capacity of the pipeline segment. i pipeline_forward_capacity_max ΔQ represents the upper limit of the pipeline's positive transport capacity. i forward_excess This represents the positive excess deviation value, which is the difference between the upper limit of the pipeline's positive transport capacity and the actual positive transport capacity of the pipeline section.

[0160] The pipeline reverse transmission capacity constraint ensures that the reverse transmission capacity of the pipeline segment is within the upper and lower limits of the pipeline's reverse transmission capacity, while also considering the reverse excess deviation value, which is expressed as follows:

[0161]

[0162] Among them, Q i pipeline_reverse_capacity_min Q represents the lower limit of the pipeline's reverse transport capacity. i segment_reverse_capacity Q represents the reverse transmission capacity of the pipeline segment. i pipeline_reverse_capacity_max ΔQ represents the upper limit of the pipeline's reverse transport capacity.i reverse_excess This represents the reverse excess deviation value, which is the difference between the upper limit of the pipeline's reverse transport capacity and the actual reverse transport capacity of the pipeline section.

[0163] S2.4 After creating the constraints, set the objective function and use a solver to solve it, so as to observe the deviation between the actual gas yield and the planned gas yield for each user in the output results. Based on the magnitude of the deviation value for each user, the potential risk points in the system can be indirectly identified. The formula for setting the objective function is as follows:

[0164]

[0165] Where, ω i Q represents the weight of each user; the higher the value, the more important the user is in the system. si Q represents the actual gas yield of user i after the solution is performed. di This represents the planned gas yield for user i.

[0166] S2.5 Once the random sampling data, constraints, and objective function have been set, a commercial solver can be used to solve the problem, thereby characterizing the uncertainties in the natural gas industry chain.

[0167] S3. By introducing risk mining indicators under various dimensions such as time dimension, gas volume dimension and reliability dimension, uncertainty is analyzed and risks are mined.

[0168] S3.1 When the introduced dimension is the time dimension, the gas supply satisfaction S is used. a The risk mining indicator is defined as the time T during which a unit node (or natural gas industry chain) supplies (or obtains) gas according to the planned volume. actual With running time T operation The ratio of .

[0169] S3.1.1 From a unit perspective, for the i-th gas source / user node, the gas supply satisfaction rate is... Let be the ratio of the planned gas supply / receive time of the i-th node to the actual operating time of the i-th node, expressed as .

[0170]

[0171] in, This represents the time when the i-th node supplies / receives gas according to the planned amount when the system is in state k for the j-th time.

[0172] S3.1.2 From the perspective of the natural gas industry chain, the satisfaction level of gas supply. This is the ratio of the planned supply / receipt time of the natural gas industry chain to the actual operating time of the system, expressed as:

[0173]

[0174] in, This represents the time during which the system is in state k for the planned amount of gas supply / receive.

[0175] S3.2 When the introduced dimension is the gas volume dimension, the gas supply guarantee degree S is adopted. u The risk mining indicator is defined as the actual gas supply (or gas gain) V of a unit node (or natural gas industry chain) during the operating time. actual Compared with the planned gas supply (or gas intake) V plan The ratio of .

[0176] S3.2.1 From the perspective of a unit node, for the i-th gas source / user node, the gas supply guarantee degree Let be the ratio of the actual gas supply / receipt of the i-th node during its operating time to the planned gas supply / receipt of the i-th node during its operating time, expressed as .

[0177]

[0178] in, This represents the actual gas supply / receipt of the i-th node when the system is in state k for the j-th time.

[0179] S3.2.2, When considering the natural gas industry chain, the degree of gas supply security The ratio of the actual gas supply / receipt during the operating time of the natural gas industry chain to the planned gas supply / receipt during the operating time of the system is expressed as:

[0180]

[0181] in, This represents the actual gas supply / receipt when the system is in state k for the jth time.

[0182] S3.3 When the introduced dimension is the reliability dimension, the risk mining indicators of the system mean gas shortage time (SASGT) and the system mean supply frequency (SASGF) are used to analyze the impact of gas supply shortages caused by uncertainties on the natural gas industry chain.

[0183] S3.3.1 The average gas shortage time of the system is the ratio of the sum of the time during which all users in the system are in a state of incomplete gas supply to the total number of users in the system during the operating time, which is expressed as follows:

[0184]

[0185] Wherein, SASGT is the system mean gas shortage time. S3.3.2 The average gas supply guarantee frequency is the ratio of the sum of the proportion of users affected by insufficient gas supply at each time point t during the operating time to the operating time, expressed as:

[0186]

[0187] Wherein, SASGF is the system average power supply frequency. T is the sum of the proportion of users affected by insufficient gas supply at each time point t. operation For runtime,

[0188] This represents the affected users at time point t.

[0189] S3.4 By using the risk mining indicators in steps S3.1-S3.3, the uncertainty of the natural gas industry chain can be analyzed and potential risks can be mined.

[0190] Optionally, the risk mining indicators can help identify key nodes, bottlenecks, and potential risk points in the system, thereby enabling corresponding measures to reduce risks and improve the robustness and reliability of the system.

[0191] like Figure 4 As shown, an apparatus for uncertainty analysis and risk mining in the natural gas industry chain includes a topology characterization module, an uncertainty characterization module, and an analysis and mining module.

[0192] The topology characterization module is used to establish a pipeline topology model for the natural gas industry chain.

[0193] The uncertainty characterization module is used to characterize the uncertainties in the natural gas industry chain.

[0194] The analysis and mining module is used to analyze the uncertainties in the natural gas industry chain and mine potential risks, helping to identify key nodes, bottlenecks and potential risk points in the natural gas industry chain, so as to take corresponding measures to reduce risks and improve the robustness and reliability of the system.

[0195] Based on the above disclosure, the present invention also provides an electronic device. The electronic device of this embodiment includes at least one processor and at least one storage medium electrically connected to the processor. The storage medium is electrically connected to the processor, wherein the storage medium stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.

[0196] Based on the same inventive concept, the present invention also provides a storage medium storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0197] It should be noted that the electrical connections between the various units mentioned above do not necessarily represent the connections between lines. Indirect connections are acceptable as long as they achieve the purpose of this invention and can be applied to the embodiments of this invention.

[0198] The foregoing description and accompanying drawings fully illustrate embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may include structural and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Some portions and features of some embodiments may be included or substituted for portions and features of other embodiments. Embodiments of the invention are not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from their scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for uncertainty analysis and risk mining in the natural gas industry chain, characterized in that, The method includes, Establish a pipeline topology model based on the natural gas industry chain; Based on the pipeline topology model, the uncertainties in the natural gas industry chain are characterized; Multi-dimensional risk mining indicators are introduced to analyze and mine the uncertainty represented.

2. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 1, characterized in that, The pipeline topology model includes treating the natural gas industry chain as a topology composed of nodes and edges, and establishing a mathematical model using graph theory.

3. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 2, characterized in that, The nodes in the pipeline topology represent unit nodes in the natural gas industry chain. The edges in the pipeline network topology represent gas transmission pipelines in the natural gas industry chain.

4. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 1, characterized in that, The characterization of uncertainties in the natural gas industry chain includes: Collect and analyze historical operating data of the natural gas industry chain, and fit the probability distribution of the historical operating data to obtain the probability distribution function; By combining the probability distribution function, a sampling algorithm is used to randomly sample historical operating data; Set constraints and objective functions for unit nodes and connections in the natural gas industry chain; Based on random sampling data, constraints, and objective functions, a solver is used to solve the problem, and the solution results are used to characterize the uncertainties in the natural gas industry chain.

5. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 3 or 4, characterized in that, The unit nodes in the natural gas industry chain include gas source nodes, user demand nodes, distribution station nodes, gas storage nodes, and LNG receiving station nodes.

6. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 5, characterized in that, The random sampling of historical operational data includes using the Metropolis-Hastington sampling algorithm based on Markov Monte Carlo chains.

7. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 3, characterized in that, The constraints include: gas source node constraints, user demand node constraints, gas storage node constraints, distribution station node constraints, LNG receiving station node constraints, and gas pipeline constraints. The constraints on the gas source nodes include upper and lower limit constraints, material balance constraints, and contract volume target planning constraints. The user demand node constraints include upper and lower limit constraints, material balance conditions, and contract volume target planning constraints. The constraints of the gas storage node include the material balance constraints and the target planning constraints of the gas storage node. The constraints on the distribution station nodes include the material balance constraints on the distribution station nodes; The constraints of the LNG receiving terminal node include the material balance constraints and the target planning constraints of the LNG receiving terminal node. The constraints on the gas pipeline include constraints on the pipeline's forward transmission capacity and constraints on the pipeline's reverse transmission capacity. The objective function is defined as the deviation between the actual demand and the planned gas supply at each user demand node, and the formula is as follows: Where, ω i Q represents the weight of each user. si Q represents the actual gas yield of user i after the solution is performed. di This represents the planned gas yield for user i.

8. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 7, characterized in that, The upper and lower limit constraints of the gas source node limit the operating range of the gas source node, and are expressed as follows: Q i min ≤Q i supply ≤Q i max Among them, Q i min Q represents the minimum supply, i.e., the lower limit of the supply. i supply Q represents the supply quantity. i max This indicates the maximum supply, i.e., the upper limit of the supply. The material balance constraint condition of the gas source node requires that the amount of natural gas supplied and the amount of natural gas flowing out of the gas source be equal, which is expressed as follows: Q i in =Q i out Among them, Q i in Q represents the natural gas supply of the i-th gas source node. i out This represents the amount of natural gas flowing out of the j-th gas source; The contractual quantity target planning constraints for the gas source nodes ensure the relationship between the actual supply quantity and the planned supply quantity, the positive deviation, and the negative deviation, which are expressed as follows: Q i plan +Q i pos -Q i neg =Q i optimized Among them, Q i plan Q represents the planned supply of natural gas. i pos Q represents the amount of positive deviation (e.g., additional supply). i neg Q represents a negative deviation (e.g., insufficient supply). i optimized This represents the optimized actual supply. The upper and lower bound constraints of the user demand node limit the scope of its use, and are expressed as follows: Q i min ≤Q i demand ≤Q i max Among them, Q i min Q represents the minimum demand, i.e., the lower limit of demand. i demand Q represents the planned demand. i max This indicates the maximum demand, i.e., the upper limit of demand. The material balance constraint of the user demand node requires that the user demand and the amount of natural gas flowing into the user be equal, which is expressed as follows: Q l demand =Q i inflow Among them, Q i demand Q represents the user's natural gas demand. i inflow This indicates the amount of natural gas flowing into the user's account; The contract quantity target planning constraints of the user demand node ensure the relationship between the actual supply quantity and the user's planned supply quantity, positive deviation, and negative deviation, which is expressed as follows: Q i plan_supply +Q i pos_deviation -Q i neg_deviation =Q i optimized Among them, Q i plan_supply Q represents the amount of natural gas the user plans to receive. i pos_deviation Q represents the amount of positive deviation (e.g., additional supply). i neg_deviation Q represents a negative deviation (e.g., insufficient supply). i optimized This represents the optimized actual supply. The material balance constraint condition for the gas storage node is that the actual gas injection volume minus the actual gas extraction volume equals the forward pipeline flow volume minus the reverse pipeline flow volume, which is expressed as follows: Q i storage_inj -Q i storage_with =Q i storage_forward -Q i storage_reverse Among them, Q i inj Q represents the actual amount of gas injected into the gas storage facility. i with Q represents the actual gas extraction volume of the gas storage facility. i forward Q represents the forward pipeline flow rate of the gas storage facility. i reverse Indicates the reverse pipeline transport volume of the gas storage facility; The target planning constraints of the gas storage node ensure the relationship between the actual gas volume of the gas storage and the planned volume, positive deviation, and negative deviation, which are expressed as follows: Q i storage_plan +ΔQ i storage_pos -ΔQ i storage_neg =Q i storage_actual Among them, Q i plan ΔQ represents the planned volume of the gas storage facility. i pos ΔQ represents the positive deviation. i neg Q represents the negative deviation. i actual This indicates the actual gas volume in the gas storage facility; The material balance constraint for the LNG receiving terminal node is that the amount of LNG unloaded at the receiving terminal equals the amount of natural gas supplied into the system, which is expressed as follows: Q i LNG =Q i gas Among them, Q i LNG Q represents the LNG receiving terminal's unloading volume. i gas This represents the amount of natural gas supplied into the system. The target planning constraints of the LNG receiving terminal nodes ensure the relationship between the actual gas volume of the LNG receiving terminal and the planned volume, positive deviation, and negative deviation, which are expressed as follows: Q i tamk_plan +ΔQ i tank_pos -ΔQ i tank_neg =Q i tank_actual Among them, Q i tank_plan ΔQ represents the planned volume of the LNG receiving terminal. i tank_pos ΔQ represents the positive deviation. i tank_neg Q represents the negative deviation. i tank_actual This indicates the actual gas volume at the LNG receiving terminal; The material balance constraint condition for the distribution station node is that the gas flow into the distribution station equals the gas flow out of the distribution station, which is expressed as follows: (Q up_fwd -Q up_rev )+Q up_source +Q storage_with +Q LNG_to_station =(Q down_fwd -Q down_rev )+Q down_demand +Q storage_inj +Q station_to_LNG Among them, Q up_fwd Q represents the forward flow rate of the actual upstream pipeline segment. up_rev Q represents the reverse flow rate of the actual upstream pipe segment. up_source Q represents the pipeline throughput of the upstream gas source section. storage_with Q represents the actual gas extraction volume of the gas storage facility. LNG_to_station Q represents the amount of gas delivered from the LNG receiving terminal to the LNG yard. down_fwd Q represents the forward flow rate of the actual downstream pipeline segment. down_rev Q represents the reverse flow rate of the actual downstream pipe section. down_demand Q represents the pipeline transport volume of the downstream demand section. storage_inj Q represents the actual amount of gas injected into the gas storage facility. station_to_LNG This indicates the volume of gas transported from the terminal to the LNG receiving terminal; The pipeline's positive transport capacity constraint ensures that the pipeline segment's positive transport capacity remains within the upper and lower limits of the pipeline's positive transport capacity, and also considers positive excess deviation values, which are expressed as follows: Among them, Q i pipeline_forward_capacity_min Q represents the lower limit of the pipeline's positive transport capacity. i segment_forward_capacity Q represents the positive transport capacity of the pipeline segment. i pipeline_forward_capacity_max ΔQ represents the upper limit of the pipeline's positive transport capacity. i forward_excess This represents the positive excess deviation value, which is the difference between the upper limit of the pipeline's positive transport capacity and the actual positive transport capacity of the pipeline section; The pipeline reverse transmission capacity constraint condition ensures that the reverse transmission capacity of the pipeline segment is within the upper and lower limits of the pipeline's reverse transmission capacity, and considers the reverse excess deviation value, which is expressed as follows: Among them, Q i pipeline_reverse_capacity_min Q represents the lower limit of the pipeline's reverse transport capacity. i segment_reverse_capacity Q represents the reverse transmission capacity of the pipeline segment. i pipeline_reverse_capacity_max ΔQ represents the upper limit of the pipeline's reverse transport capacity. i reverse_excess This represents the reverse excess deviation value, which is the difference between the upper limit of the pipeline's reverse transport capacity and the actual reverse transport capacity of the pipeline section.

9. The method for uncertainty analysis and risk mining in the natural gas industry chain according to claim 1, characterized in that, The multiple dimensions include time dimension, gas volume dimension, and reliability dimension; When the time dimension is introduced, the risk mining index of gas supply satisfaction is adopted, which is the ratio of the time for a unit node or natural gas industry chain to supply or obtain gas according to the planned amount to the operating time. When introducing the gas volume dimension, the risk mining index of gas supply security is adopted, which is the ratio of the actual gas supply or gas gain of a unit node or natural gas industry chain system to the planned gas supply or gas gain during the operating time. When introducing the reliability dimension, risk mining indicators such as the system's average gas shortage time and the system's average supply guarantee frequency are used to analyze the impact of gas supply shortages caused by uncertainties on the natural gas industry chain system.

10. A device for uncertainty analysis and risk assessment in the natural gas industry chain, characterized in that, The device includes: a topology characterization module, an uncertainty characterization module, and an analysis and mining module; The topology characterization module is used to establish a pipeline topology model for the natural gas industry chain. The uncertainty characterization module is used to characterize the uncertainties in the natural gas industry chain; The analysis and mining module is used to analyze the uncertainties in the natural gas industry chain and mine potential risks, helping to identify key nodes, bottlenecks and potential risk points in the natural gas industry chain.

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