Gas storage gas production optimization method and system based on improved whale optimization algorithm

Abstract

The invention provides a gas storage gas production optimization method and system based on an improved whale optimization algorithm. The method comprises the steps that operation data of an underground gas storage, parameter information of ground equipment, natural gas transportation pipeline data and natural gas receiving station data are obtained; based on the acquired operation data for the underground gas storage, the parameter information of the ground equipment, the natural gas transportation pipeline data and the natural gas receiving station data, an optimization model for gas production of the underground gas storage is constructed, and the optimization model comprises definition of a target function and setting of constraint conditions; and solving the optimization model through an improved whale optimization algorithm to determine an optimal gas production strategy of the underground gas storage. The optimization model constructed by the method has high flexibility and adaptability, can be dynamically adjusted according to different requirements and market conditions, effectively reduces the operation cost, and has remarkable practical value and economic benefit.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas storage reservoirs, and in particular to a gas storage reservoir gas extraction optimization method and system based on an improved whale optimization algorithm. Background Art

[0002] As an important fossil energy, natural gas is widely used in our daily life, including as automobile fuel, power generation, heating, etc.

[0003] According to data provided by China National Petroleum Corporation, with the increase and mobility of population, there is a surge in natural gas demand in some areas, but the supply of natural gas is insufficient. As a natural natural gas storage container, underground gas storage can not only adjust the peak demand on demand, safely supply gas, and reduce the cost of gas transmission, but also serve as a strategic reserve resource to cope with the shortage of urban gas market demand due to large fluctuations in seasons and day and night. It is difficult to solve the contradiction of large fluctuations in gas consumption by only relying on the gas transmission network system to balance the gas transmission and small-scale adjustment of the flow rate. Underground gas storage is used to store the surplus gas in the gas transmission system during low-peak gas consumption, and it is extracted during peak gas consumption to supplement the insufficient pipeline gas supply, solving the problem of gas peak regulation. When the gas source is interrupted and the gas transmission system stops transmission, the underground gas storage can be used as a gas source to ensure continuous gas supply, playing a dual role of peak regulation and safe gas supply.

[0004] Among the research and technical applications that have been published so far, there are two main research directions. One is the configuration and optimization of natural gas pipeline flow. Research in this area mainly focuses on how to improve the efficiency and safety of natural gas transportation by optimizing pipeline layout and control strategies. Another type of research focuses on the optimization and design of equipment and parts inside underground gas storage, including structural optimization of compressors and pipelines, as well as internal gas storage monitoring systems. Currently, these studies on underground gas storage gas production systems mainly rely on conventional mathematical calculation methods or foreign software for simulation. These methods and tools have the following main disadvantages:

[0005] (1) High cost: Using foreign software for simulation and optimization usually requires high licensing fees, which increases the cost of research and application. For many research institutions and enterprises, this high cost has become a major obstacle to its widespread application.

[0006] (2) Large amount of calculation: Conventional mathematical calculation and simulation methods need to process a large amount of data and complex calculations when dealing with complex gas production systems, which not only leads to long calculation time, but also places very high demands on computing resources. This makes real-time scheduling and dynamic optimization difficult, and it is impossible to quickly respond to changes and emergencies in actual operations.

[0007] (3) Insufficient applicability: Existing software and methods are usually based on foreign standards and assumptions, and may not fully adapt to the specific conditions and needs of domestic underground gas storage. This results in the accuracy and practicality of the optimization results being limited in actual applications, and the effectiveness of the gas storage cannot be fully utilized. Summary of the invention

[0008] In view of this, the present invention provides a gas storage gas production optimization method and system based on an improved whale optimization algorithm to at least solve the above-mentioned problems.

[0009] According to a first aspect of an embodiment of the present invention, a method for optimizing gas production from a gas storage facility based on an improved whale optimization algorithm is provided, comprising: obtaining operating data for an underground gas storage facility, parameter information of ground equipment, and natural gas transportation pipeline data and natural gas receiving station data, wherein the operating data of the underground gas storage facility includes the capacity, pressure change, gas injection and gas production rate of the gas storage facility, the parameter information of the ground equipment includes the specifications, operating status and maintenance records of the compressor, valve and pipeline, the natural gas transportation pipeline data includes the diameter, length, pressure loss, flow rate and information of the transmission stations along the way, and the natural gas receiving station data includes the processing capacity and storage capacity of the receiving station. , demand, input flow and output flow; based on the acquired operation data of underground gas storage, parameter information of ground equipment, natural gas transportation pipeline data and natural gas receiving station data, an optimization model for gas production in underground gas storage is constructed. The optimization model includes the definition of objective function and the setting of constraints. The objective function includes pipeline transportation maintenance cost, gas storage cost, ground equipment operation cost and natural gas shortage penalty cost. The constraints include node flow conservation constraints, pipeline pressure constraints, pressure flow boundary constraints and gas storage operation constraints; the optimization model is solved by the improved whale optimization algorithm to determine the optimal gas production strategy for underground gas storage.

[0010] In one implementation, the optimization model takes minimizing the operating cost of the underground gas storage as the objective function, and the optimization model is expressed as:

[0011] minf=f transform +f storge +f ground +f penal

[0012] Among them, f transform is the pipeline transportation maintenance cost, f storge is the storage cost of gas storage, f ground is the ground equipment operating cost, f penal Penalize costs for gas shortages.

[0013] In another implementation, the pipeline transportation maintenance cost is expressed as:

[0014]

[0015] Among them, P transform represents the unit pipeline transportation cost, Q ij represents the pipeline flow rate, A represents the pipeline collection, t represents the number of single operating days, and T represents the total number of operating days;

[0016] The storage cost of gas storage is expressed as:

[0017]

[0018] Among them, P storge Represents the unit operating cost of the gas storage, G extract,t Indicates the gas production per unit operating time;

[0019] The operating cost of ground equipment is expressed as:

[0020]

[0021] Among them, P main represents the pipeline maintenance cost, D t Indicates the equipment running time;

[0022] The gas shortage penalty cost is expressed as:

[0023]

[0024] Among them, C penal represents the penalty cost coefficient, Q need,t represents the demand at time t, Q ij,t Represents the actual supply of natural gas pipeline at time t.

[0025] In another implementation, the node flow conservation constraint is expressed as:

[0026]

[0027] Among them, Q retention represents the node retention amount;

[0028] The pipeline pressure constraint is expressed as:

[0029]

[0030] Among them, q ij represents the flow rate from section i to j in the pipeline (i, j∈A), C is the pressure drop coefficient from section i to j, and D ij is the pipe diameter from i to j, Z is the gas compression factor, T is the pipe temperature, Δ * is the relative density of natural gas, λ is the hydraulic friction coefficient, L ij is the length of the pipeline from segment i to j;

[0031] The pressure-flow boundary constraint is expressed as:

[0032] minP ij ≤P ij ≤maxP ij

[0033] Q ij ≤Q ij ≤maxQ ij

[0034] The gas storage operation constraints are expressed as:

[0035] G U =G base +G extract

[0036] The total gas production is calculated as follows:

[0037]

[0038] Among them, G base Indicates the gas volume of the cushion layer; n well represents the number of injection and production wells, Indicates the flow rate of the injection and production well at time t.

[0039] In another implementation, the specific steps of solving the optimization model through the improved whale optimization algorithm include: first, initializing the population parameters; then optimizing the individual positions of the population through Chebyshev chaos mapping; then entering the iterative process: according to random probability, choosing to adopt the surrounding prey strategy or the spiral predation strategy with Levy flight to update the position; recording the optimal position in the population and the global optimal fitness value; judging whether the maximum number of iterations has been reached or the termination condition has been met; finally, outputting the optimization result.

[0040] In another implementation, the individual positions of the population are optimized by Chebyshev chaotic mapping to obtain the updated population position. The population position update formula is:

[0041] X t+1 =cos(n·arccos(x t ))

[0042]

[0043] Among them, x t is the chaotic variable in generation t, n is the order of the chaotic sequence, which is 2 here, x i is the chaotic variable generated by Chebyshev mapping, X i (0) is iThe initial position of the whale.

[0044] In another implementation, the local search capability of the improved whale optimization algorithm is improved by introducing a spiral predation strategy with Levy flight. The mathematical formula of the spiral predation strategy with Levy flight is as follows:

[0045]

[0046] Where LF(X) is the Lévy flight formula, μ is the location parameter, σ is the scale parameter, β is the exponential parameter of the Lévy flight, ν is a random variable sampled from a standard normal distribution, and e bl is the exponential term associated with the whale's spiral path, b is a constant used to control the contraction of the spiral motion, and l is a random number. is the current optimal solution, and D' is the distance from the current whale to the optimal solution.

[0047] According to a second aspect of an embodiment of the present invention, a gas storage gas production optimization system based on an improved whale optimization algorithm is provided, comprising: a data acquisition module for acquiring operating data of an underground gas storage, parameter information of ground equipment, and natural gas transportation pipeline data and natural gas receiving station data, wherein the operating data of the underground gas storage includes the capacity, pressure change, gas injection and gas production rate of the gas storage, the parameter information of the ground equipment includes the specifications, operating status and maintenance records of the compressor, valve and pipeline, the natural gas transportation pipeline data includes the diameter, length, pressure loss, flow rate and information of the transmission stations along the way, and the natural gas receiving station data includes the processing capacity, storage capacity and demand of the receiving station , input flow and output flow; a model building module, which is used to build an optimization model for gas production in underground gas storage based on the acquired operation data of underground gas storage, parameter information of ground equipment, natural gas transportation pipeline data and natural gas receiving station data. The optimization model includes the definition of objective function and the setting of constraint conditions. The objective function includes pipeline transportation maintenance cost, gas storage cost, ground equipment operation cost and natural gas shortage penalty cost. The constraint conditions include node flow conservation constraint, pipeline pressure constraint, pressure flow boundary constraint and gas storage operation constraint; a solution module, which is used to solve the optimization model through the improved whale optimization algorithm to determine the optimal gas production strategy for underground gas storage.

[0048] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising a processor and a memory storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the steps executed by the method of the first aspect.

[0049] According to a fourth aspect of an embodiment of the present invention, there is provided a computer storage medium on which a computer program is stored. When the program is executed by a processor, the method of the first aspect described above is implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) Reduce costs: The present invention reduces dependence on foreign software and reduces licensing and usage fees by developing a scheduling optimization system with independent intellectual property rights, thereby reducing overall costs.

[0052] (2) Improve efficiency: The present invention utilizes advanced algorithms and optimization models to improve computing efficiency and reduce computing time and resource consumption.

[0053] (3) Strong adaptability: The present invention will be designed according to the actual situation of domestic underground gas storage facilities to ensure the applicability and accuracy of the optimization model and algorithm, thereby improving the gas production efficiency of the gas storage facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention is a flow chart of the steps of the gas storage gas extraction optimization method based on the improved whale optimization algorithm.

[0055] Figure 2 For Figure 1 Corresponding to the specific flow chart of the gas storage gas extraction optimization method based on the improved whale optimization algorithm of the present invention.

[0056] Figure 3 This is a comparison chart of the results after optimizing the model using different algorithms. DETAILED DESCRIPTION

[0057] 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.

[0058] See also Figure 1-Figure 3 , the steps of the embodiment of the present invention include:

[0059] Step S1, obtaining the operation data of the underground gas storage, the parameter information of the ground equipment, the natural gas transportation pipeline data and the natural gas receiving station data, wherein the operation data of the underground gas storage includes the capacity, pressure change, gas injection and gas production rate of the gas storage, the parameter information of the ground equipment includes the specifications, operation status and maintenance records of the compressor, valve and pipeline, the natural gas transportation pipeline data includes the diameter, length, pressure loss, flow rate and the information of the transmission stations along the way, and the natural gas receiving station data includes the processing capacity, storage capacity, demand, input flow rate and output flow rate of the receiving station.

[0060] Step S2, based on the acquired operation data of the underground gas storage, parameter information of the ground equipment, and the natural gas transportation pipeline data and the natural gas receiving station data, an optimization model for gas production in the underground gas storage is constructed, wherein the optimization model includes the definition of the objective function and the setting of the constraint conditions, wherein the objective function includes the pipeline transportation maintenance cost, the gas storage cost, the ground equipment operation cost and the natural gas shortage penalty cost, and the constraint conditions include the node flow conservation constraint, the pipeline pressure constraint, the pressure flow boundary constraint and the gas storage operation constraint;

[0061] Step S3, solving the optimization model by using the improved whale optimization algorithm to determine the optimal gas production strategy for the underground gas storage.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] (1) Reduce costs: The present invention reduces dependence on foreign software and reduces licensing and usage fees by developing a scheduling optimization system with independent intellectual property rights, thereby reducing overall costs.

[0064] (2) Improve efficiency: The present invention utilizes advanced algorithms and optimization models to improve computing efficiency and reduce computing time and resource consumption.

[0065] (3) Strong adaptability: The present invention will be designed according to the actual situation of domestic underground gas storage facilities to ensure the applicability and accuracy of the optimization model and algorithm, thereby improving the gas production efficiency of the gas storage facilities.

[0066] Specifically, the scheme of the present invention is further described according to the following examples:

[0067] 1. Objective Function

[0068] The technology of the present invention takes Wen 96 gas storage as the research object, and models and optimizes the gas production situation of the gas storage. The optimization model of the present invention aims to minimize the operating cost of the gas storage, including the pipeline transportation cost f transform , gas storage cost f storge , ground equipment operating cost f ground , gas shortage penalty cost f penal The mathematical formula is as follows:

[0069] minf=f transform +f storge +f ground +f penal (1)

[0070] (1) Pipeline transportation maintenance costs:

[0071]

[0072] Among them, P transformrepresents the unit pipeline transportation cost, Q ij represents the pipeline flow rate, A represents the pipeline collection, t represents the number of single operating days, and T represents the total number of operating days.

[0073] (2) Gas storage cost:

[0074] The storage cost of a gas storage reflects the storage capacity and operation mode of the gas storage when extracting gas. Its mathematical expression is as follows:

[0075]

[0076] Among them, P storge Represents the unit operating cost of the gas storage, G extract,t Indicates the gas production per unit operating time.

[0077] (3) Ground equipment operating costs:

[0078] The operation of ground equipment includes the operation and maintenance of desulfurization, dehydration and other equipment, as well as the maintenance of other supporting equipment. The mathematical formula is as follows:

[0079]

[0080] Among them, P main represents the pipeline maintenance cost, D t Indicates the equipment running time;

[0081] (4) Natural gas shortage penalty costs:

[0082] The natural gas shortage penalty cost refers to the cost of meeting the natural gas supply demand at the demand point and avoiding the shortage due to insufficient natural gas supply. In addition, considering the difference between the actual situation and the model design, the design of the natural gas shortage penalty cost also changes according to the needs of the actual data. Its mathematical expression is as follows:

[0083]

[0084] Among them, C penal represents the penalty cost coefficient, Q need,t represents the demand at time t, Q ij,t Represents the actual supply of natural gas pipeline at time t.

[0085] See Table 1 for the data results obtained after the experiment according to the above design, and compare them with the data results of other algorithms:

[0086] Table 1

[0087]

[0088] 2. Constraints

[0089] (1) Node flow conservation constraints:

[0090] According to the law of conservation of flow, for the gas storage system, each section of the pipeline is connected by nodes, and the nodes are divided into supply nodes and ordinary nodes. According to the law of conservation of flow, for the supply node, the outflow of natural gas minus the inflow is equal to the node planned supply minus the node retention; for the ordinary node, the inflow of natural gas is equal to the outflow. Its mathematical formula is as follows:

[0091]

[0092] Among them, Q retention Indicates the node retention amount.

[0093] (2) Pipeline pressure constraints:

[0094] In the process of natural gas transportation, the pressure change of the pipeline is a key factor that needs to be strictly considered. This pressure change is mainly affected by factors such as natural gas flow rate, pipeline diameter, pipe wall roughness, temperature and friction loss. In order to ensure the safety and efficiency of the transportation process, the pressure gradient in the pipeline must be accurately calculated and controlled. Specifically, the change in pipeline pressure not only affects the transportation efficiency, but may also affect the quality and safety of gas supply to downstream users. Therefore, it is crucial to establish a relevant mathematical model based on the constraints of pipeline pressure, and its expression is as follows:

[0095]

[0096] Among them, q ij represents the flow rate from section i to j in the pipeline (i, j∈A), C is the pressure drop coefficient from section i to j, and D ij is the pipe diameter from i to j, Z is the gas compression factor, T is the pipe temperature, Δ * is the relative density of natural gas, λ is the hydraulic friction coefficient, L ij is the length of the pipeline from segment i to j.

[0097] (3) Pressure and flow boundary constraints:

[0098] In addition to considering flow and pressure constraints, it is also necessary to set certain boundary conditions for the flow and pressure within the model so that the model meets the actual situation:

[0099] minP ij ≤P ij ≤maxP ij (10)

[0100] Q ij ≤Q ij ≤maxQ ij (11)

[0101] (4) Gas storage operation constraints:

[0102] The operation of gas storage facilities is subject to multiple constraints that are designed to ensure their safety, efficiency, and ability to meet system demands.

[0103] G U =G base +G extract (12)

[0104] The total gas production is calculated as follows:

[0105]

[0106] Among them, G base Indicates the gas volume of the cushion layer; n well represents the number of injection and production wells, Indicates the flow rate of the injection and production well at time t.

[0107] The specific steps of solving the optimization model through the improved whale optimization algorithm include: first, initialize the population parameters; then optimize the individual positions of the population through Chebyshev chaos mapping; then enter the iterative process: according to random probability, choose to adopt the surrounding prey strategy or the spiral predation strategy with Levy flight to update the position; record the optimal position in the population and the global optimal fitness value; determine whether the maximum number of iterations is reached or the termination condition is met; finally, output the optimization result.

[0108] It should be understood that in order to solve the optimization results faster and more accurately, we choose to use the improved whale optimization algorithm as a solution tool. And use the Chebyshev chaos mapping formula to optimize the individual positions of the population and increase the diversity of solution space exploration. The updated population position update formula is:

[0109] X t+1 =cos(n·arccos(x t )) (16)

[0110]

[0111] Among them, x t is the chaotic variable in generation t, n is the order of the chaotic sequence, usually a positive integer, and here it is 2. i is the chaotic variable generated by Chebyshev mapping, X i (0) is i The initial position of the whale.

[0112] Subsequently, the spiral predation strategy with Levy flight, namely the Levy flight strategy, is introduced to improve the local search ability of the algorithm. The mathematical formula is as follows:

[0113]

[0114]

[0115] Where LF(X) is the Lévy flight formula, μ is the location parameter, σ is the scale parameter, β is the exponential parameter of the Lévy flight, ν is a random variable sampled from a standard normal distribution, and e bl is the exponential term associated with the whale's spiral path, b is a constant used to control the contraction of the spiral motion, and l is a random number. is the current optimal solution, and D' is the distance from the current whale to the optimal solution.

[0116] It should be understood that the value range of μ and ν is (0,1), the value range of β is [0,2], l varies in the range of [-1,1], and D' is the distance from the current whale to the optimal solution, which can be regarded as the vector of the whale's distance to the prey (solution).

[0117] The present invention effectively reduces operating costs, improves economic benefits and operational efficiency, and provides strong support for energy management by establishing a gas storage gas extraction optimization model. Through advanced data-driven decision-making and intelligent control, the model has high flexibility and adaptability, can be dynamically adjusted according to different needs and market conditions, and shows significant practical value and economic benefits.

[0118] In addition, for the technical solution of the gas storage gas production optimization model, in addition to using the improved whale optimization algorithm as a solution tool, other alternative solutions can also be used to achieve the same invention purpose.

[0119] Specifically, other metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA) can be considered, which perform well in global optimization and solving complex problems. In addition, other optimization models such as linear programming, mixed integer linear programming (MILP) and dynamic programming (DP) can also be used. These optimization methods are usually used to deal with structured problems, have determinism and high solution accuracy, and are particularly suitable for scenarios with clear constraints and objective functions. Through these alternatives, the gas production process of gas storage can also be effectively optimized to achieve efficient resource scheduling and cost control.

[0120] According to another aspect of an embodiment of the present invention, a gas storage gas production optimization system based on an improved whale optimization algorithm is provided, comprising:

[0121] A data acquisition module is used to acquire the operation data of underground gas storage, parameter information of ground equipment, and data of natural gas transportation pipelines and natural gas receiving stations. The operation data of underground gas storage includes the capacity, pressure change, gas injection and gas production rate of the gas storage. The parameter information of ground equipment includes the specifications, operation status and maintenance records of compressors, valves and pipelines. The data of natural gas transportation pipelines includes the diameter, length, pressure loss, flow rate and information of transmission stations along the way. The data of natural gas receiving stations includes the processing capacity, storage capacity, demand, input flow rate and output flow rate of the receiving stations.

[0122] A model building module is used to build an optimization model for underground gas storage gas production based on the acquired operation data of the underground gas storage, parameter information of the ground equipment, and the natural gas transportation pipeline data and the natural gas receiving station data. The optimization model includes the definition of the objective function and the setting of the constraint conditions. The objective function includes the pipeline transportation maintenance cost, the gas storage cost, the ground equipment operation cost and the natural gas shortage penalty cost. The constraint conditions include the node flow conservation constraint, the pipeline pressure constraint, the pressure flow boundary constraint and the gas storage operation constraint.

[0123] The solution module is used to solve the optimization model through the improved whale optimization algorithm to determine the optimal gas production strategy of the underground gas storage.

[0124] The gas storage gas extraction optimization system based on the improved whale optimization algorithm of this embodiment is used to implement the corresponding methods in the aforementioned multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here. In addition, the functional implementation of each module in the system of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be repeated here.

[0125] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the method according to the first aspect.

[0126] According to another aspect of an embodiment of the present invention, there is provided a computer storage medium on which a computer program is stored. When the program is executed by a processor, the method of the first aspect is implemented.

[0127] The present invention may be a method, a system, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0128] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0129] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0130] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, Python, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0131] Various aspects of the present invention are described herein with reference to the flow chart and / or block diagram of the method, system (device) and computer program product according to the embodiment of the present invention. It should be understood that each square frame of the flow chart and / or block diagram and the combination of each square frame in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0132] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing system, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing system, a system that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing system, and / or other device to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0133] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing system, or other device so that a series of operational steps are performed on the computer, other programmable data processing system, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing system, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0134] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0135] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A gas storage gas extraction optimization method based on an improved whale optimization algorithm, characterized in that: include: Obtaining the operation data of underground gas storage, parameter information of surface equipment, and data of natural gas transportation pipelines and natural gas receiving stations, wherein the operation data of underground gas storage includes the capacity, pressure change, gas injection and gas production rate of the gas storage, the parameter information of surface equipment includes the specifications, operation status and maintenance records of compressors, valves and pipelines, the data of natural gas transportation pipelines includes the diameter, length, pressure loss, flow rate and information of transmission stations along the way, and the data of natural gas receiving stations includes the processing capacity, storage capacity, demand, input flow rate and output flow rate of the receiving stations; Based on the acquired operation data of the underground gas storage, parameter information of the ground equipment, and data of the natural gas transportation pipeline and the natural gas receiving station, an optimization model for gas production in the underground gas storage is constructed. The optimization model includes the definition of the objective function and the setting of the constraint conditions. The objective function includes the pipeline transportation maintenance cost, the gas storage cost, the ground equipment operation cost and the natural gas shortage penalty cost. The constraint conditions include the node flow conservation constraint, the pipeline pressure constraint, the pressure flow boundary constraint and the gas storage operation constraint. The optimization model is solved by using the improved whale optimization algorithm to determine the optimal gas production strategy for the underground gas storage.

2. The method according to claim 1, characterized in that The optimization model takes minimizing the operating cost of underground gas storage as the objective function, and the optimization model is expressed as: minf=f transform +f storge +f ground +f penal Among them, f transform is the pipeline transportation maintenance cost, f storge is the storage cost of gas storage, f ground is the ground equipment operating cost, f penal Penalize costs for gas shortages.

3. The method according to claim 2, characterized in that The pipeline transportation maintenance cost is expressed as: Among them, P transform represents the unit pipeline transportation cost, Q ij represents the pipeline flow rate, A represents the pipeline collection, t represents the number of single operating days, and T represents the total number of operating days; The storage cost of gas storage is expressed as: Among them, P storge Represents the unit operating cost of the gas storage, G extract,t Indicates the gas production per unit operating time; The operating cost of ground equipment is expressed as: Among them, P main represents the pipeline maintenance cost, D t Indicates the equipment running time; The gas shortage penalty cost is expressed as: Among them, C penal represents the penalty cost coefficient, Q need,t represents the demand at time t, Q ij,t Represents the actual supply of natural gas pipeline at time t.

4. The method according to claim 3, characterized in that The node flow conservation constraint is expressed as: Among them, Q retention represents the node retention amount; The pipeline pressure constraint is expressed as: Among them, q ij represents the flow rate from section i to j in the pipeline (i, j∈A), C is the pressure drop coefficient from section i to j, and D ij is the pipe diameter from i to j, Z is the gas compression factor, T is the pipe temperature, Δ * is the relative density of natural gas, λ is the hydraulic friction coefficient, L ij is the length of the pipeline from segment i to j; The pressure-flow boundary constraint is expressed as: minP ij ≤P ij ≤maxP ij minQ ij ≤Q ij ≤maxQ ij The gas storage operation constraints are expressed as: G U =G base +G extract The total gas production is calculated as follows: Among them, G base Indicates the gas volume of the cushion layer; n well represents the number of injection and production wells, Indicates the flow rate of the injection and production well at time t.

5. The method according to claim 4, characterized in that The specific steps of solving the optimization model by using the improved whale optimization algorithm include: First, initialize the population parameters; Then, the individual positions of the population are optimized through Chebyshev chaotic mapping; Then it enters the iterative process: according to random probability, it chooses to adopt the encirclement strategy or the spiral predation strategy with Levy flight to update the position; Record the optimal position in the population and the global optimal fitness value; Determine whether the maximum number of iterations has been reached or the termination condition has been met; Finally, the optimization results are output.

6. The method according to claim 5, characterized in that The individual positions of the population are optimized by Chebyshev chaotic mapping to obtain the updated population position. The population position update formula is: X t+1 =cos(n·arccos(x t )) Among them, x t is the chaotic variable in generation t, n is the order of the chaotic sequence, which is 2 here, x i is the chaotic variable generated by Chebyshev mapping, X i (0) is i The initial position of the whale.

7. The method according to claim 6, characterized in that By introducing the spiral predation strategy with Levy flight, the local search ability of the improved whale optimization algorithm is improved. The mathematical formula of the spiral predation strategy with Levy flight is as follows: Where LF(X) is the Lévy flight formula, μ is the location parameter, σ is the scale parameter, β is the exponential parameter of the Lévy flight, ν is a random variable sampled from a standard normal distribution, and e bl is the exponential term associated with the whale's spiral path, b is a constant used to control the contraction of the spiral motion, and l is a random number. is the current optimal solution, and D' is the distance from the current whale to the optimal solution.

8. A gas storage gas extraction optimization system based on an improved whale optimization algorithm, characterized in that: include: A data acquisition module is used to acquire the operation data of underground gas storage, parameter information of ground equipment, and data of natural gas transportation pipelines and natural gas receiving stations. The operation data of underground gas storage includes the capacity, pressure change, gas injection and gas production rate of the gas storage. The parameter information of ground equipment includes the specifications, operation status and maintenance records of compressors, valves and pipelines. The data of natural gas transportation pipelines includes the diameter, length, pressure loss, flow rate and information of transmission stations along the way. The data of natural gas receiving stations includes the processing capacity, storage capacity, demand, input flow rate and output flow rate of the receiving stations. A model building module is used to build an optimization model for underground gas storage gas production based on the acquired operation data of the underground gas storage, parameter information of the ground equipment, and the natural gas transportation pipeline data and the natural gas receiving station data. The optimization model includes the definition of the objective function and the setting of the constraint conditions. The objective function includes the pipeline transportation maintenance cost, the gas storage cost, the ground equipment operation cost and the natural gas shortage penalty cost. The constraint conditions include the node flow conservation constraint, the pipeline pressure constraint, the pressure flow boundary constraint and the gas storage operation constraint. The solution module is used to solve the optimization model through the improved whale optimization algorithm to determine the optimal gas production strategy of the underground gas storage.

9. An electronic device, characterized in that: include: processor; A memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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