Gas storage gas injection scheduling optimization method and system based on improved Harris eagle algorithm

Abstract

The invention provides a gas storage gas injection scheduling optimization method and system based on an improved Harris eagle algorithm. The method comprises the following steps: acquiring pipeline data and ground data of an underground gas storage; an underground gas storage gas injection scheduling model is constructed based on pipeline data and ground data of an underground gas storage, the underground gas storage gas injection scheduling model comprises definition of an objective function and setting of constraint conditions, and the objective function comprises compressor unit operation cost and gas storage storage cost. The constraint conditions comprise node constraint, pipeline constraint, compressor unit constraint and gas storage constraint; and solving the gas injection scheduling model of the underground gas storage by improving the Harris eagle algorithm so as to determine the optimal gas injection scheduling strategy of the underground gas storage. The underground gas storage gas injection scheduling model constructed by the invention reduces the operation cost of the gas storage, improves the economic profit and the operation efficiency, can adapt to different requirements and market conditions, and has very high practical value and economic benefit.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas storage facilities, and in particular to a method and system for optimizing gas injection scheduling of a gas storage facility based on an improved Harris Eagle algorithm. Background Art

[0003] At present, the domestic natural gas supply security faces multiple challenges. my country's main natural gas consumption areas are concentrated in the southeast coast, while the main supply areas are located in the central and western regions. Long-distance transportation leads to high costs and difficult pipeline laying. Thirdly, extreme weather in winter leads to a surge in gas demand, which is prone to tight natural gas supply. Finally, the domestic natural gas peak-shaving capacity is insufficient, seasonal supply and demand fluctuations are obvious, gas storage is difficult in summer, and increased supply in winter is limited.

[0004] my country started research on underground gas storage relatively late. In 1998, Professor Ma Xinhua first systematically proposed a definition of underground gas storage that suits my country's characteristics, namely, artificial gas fields or reservoirs formed by re-injecting natural gas into natural or artificial underground structures. Underground gas storage has the advantage of large capacity, which can effectively adjust seasonal supply and demand imbalances and ensure gas supply demand during peak periods. In addition, underground gas storage also serves as a national strategic reserve to provide protection for responding to energy crises.

[0005] In recent years, as my country has paid more and more attention to energy security, the research on underground gas storage has become a hot area. These gas storages are not only important facilities for natural gas storage, but also play a key role in regulating the balance between supply and demand and ensuring energy security. The construction and operation of underground gas storages involve multiple disciplines, including geology, engineering, and materials science. Its research focuses include gas storage site selection, gas storage capacity assessment, gas injection and production technology, and long-term stability monitoring of gas storages.

[0006] In existing research and technical applications, there are two main research directions. One is the configuration and optimization of natural gas pipeline network flow. Research in this area mainly focuses on how to improve the efficiency and safety of natural gas transportation by optimizing pipeline network layout and control strategies. This type of research often uses mathematical modeling and computer simulation technology to provide support for pipeline network planning and operation by predicting and optimizing flow under different scenarios. Another type of research focuses on the optimization and design of equipment and parts inside underground gas storage. This includes structural optimization, material selection and life assessment of key equipment such as compressors, valves, and pipelines. The goal of the research is to improve the operating efficiency of equipment, extend the service life of equipment, and reduce operating and maintenance costs.

[0007] However, despite the significant progress made in the above research directions, there is still relatively little research on the scheduling of underground gas storage. The scheduling of underground gas storage involves systematic planning of issues such as the timing of injection and production, the amount of injection and production, and coordination with other energy storage methods. The shortcoming of this field is the lack of research on dynamic scheduling optimization of gas storage under different operating conditions, especially in the face of sudden changes in demand or pipeline accidents. Existing technologies and research mostly focus on static equipment and pipeline network optimization, and lack systematic solutions for how to perform real-time scheduling in complex and changing market and demand environments.

[0008] Currently, most of the research and optimization of underground gas storage mainly rely on conventional mathematical calculation methods or foreign software for simulation. These methods and tools have the following main disadvantages:

[0009] (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.

[0010] (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 storage systems. This 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.

[0011] (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 optimization results being limited in actual applications, and the effectiveness of gas storage cannot be fully utilized. Summary of the invention

[0012] In view of this, the present invention provides a method and system for optimizing gas storage injection scheduling based on an improved Harris Eagle algorithm to at least solve the above-mentioned problem.

[0013] According to a first aspect of an embodiment of the present invention, a method for optimizing gas injection scheduling of a gas storage reservoir based on an improved Harris Eagle algorithm is provided, comprising: acquiring pipeline data and ground data of an underground gas storage reservoir; constructing a gas injection scheduling model for the underground gas storage reservoir based on the pipeline data and ground data of the underground gas storage reservoir, the gas injection scheduling model for the underground gas storage reservoir comprising the definition of an objective function and the setting of constraints, the objective function comprising the operating cost of a compressor unit and the storage cost of a gas storage reservoir, the constraints comprising node constraints, pipeline constraints, compressor unit constraints and gas storage reservoir constraints; solving the gas injection scheduling model for the underground gas storage reservoir by using the improved Harris Eagle algorithm to determine the optimal gas injection scheduling strategy for the underground gas storage reservoir.

[0014] In one implementation, the gas injection scheduling model for underground gas storage takes minimizing the gas injection process of the underground gas storage as the objective function, and the gas injection scheduling model for underground gas storage is expressed as:

[0015] minC=min(C compressor +C storge )

[0016] Among them, C compressor is the operating cost of the compressor unit, C storge Storage costs for gas storage.

[0017] In another implementation, the compressor unit operating cost is expressed as:

[0018] C compressor =P gas D t W com n com

[0019] Among them, P gas Indicates the unit electricity price, D t Indicates the compressor running time, W com Indicates the power of the compressor, n com Indicates the number of compressors in operation;

[0020] The storage cost of gas storage is expressed as:

[0021] C storge =P storge G storge

[0022] G storge =D t ×W com

[0023] Among them, P storge G represents the unit storage cost coefficient of the gas storage, including the costs of gas storage construction, equipment procurement, and daily maintenance costs. storge Indicates the gas storage capacity.

[0024] In another implementation, the node constraint is expressed as:

[0025] Q source ≥Q inject

[0026] Among them, Q source Indicates the natural gas flow rate output by the gas source, Q inject Indicates the natural gas flow rate injected into the injection-production well;

[0027] The pipeline constraint is expressed as:

[0028]

[0029] Among them, q ij represents the flow rate from section i to j in the pipeline, 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;

[0030] The compressor train constraints are expressed as:

[0031] Q com =n com q com

[0032] 0≤W com ≤maxW com

[0033]

[0034] Among them, q com is the flow rate of a single compressor, μ is the polynomial index, Z1 is the gas compression factor at the compressor inlet, R s is the gas constant, T1 is the compressor inlet temperature, η m is the compressor mechanical efficiency, η pol is the variable efficiency of the compressor, ε is the pressure ratio of the pipe section;

[0035] The gas storage constraint is expressed as:

[0036] G U =G base +G storge

[0037] G U ≤maxG U

[0038] Q inject =n well q well D t

[0039] minq well ≤q well ≤maxq well

[0040] Among them, G base Indicates the cushion gas volume, n well represents the number of injection and production wells, q well Indicates the flow rate of the injection and production well.

[0041] In another implementation, the specific steps of solving the gas injection scheduling model of underground gas storage by improving the Harris Hawk algorithm include: initializing Harris Hawk parameters; introducing Cauchy mutation and adversarial learning strategies to update the initialization strategy of the population; calculating the fitness value, recording the optimal position of the population and the global optimal fitness value; updating the population position, judging whether the number of iterations reaches the termination condition; and outputting the minimum operating cost.

[0042] In another implementation, the diversity of the population is increased by introducing Cauchy mutation to generate the initial solution of the population. The updated population initialization formula is:

[0043] Q=(Cauchy(β)·β)

[0044] X new =(lb+(ub-lb)·rand(0,1))+Q

[0045] Among them, Cauchy(β) is the Cauchy distribution, β is a scaling factor, usually used to adjust the step size, lb is the lower bound of the variable, ub is the upper bound of the variable, both of which can be customized, and rand(0,1) is a uniformly distributed random number generator that generates random numbers between 0 and 1, which is used to randomly explore different values ​​of the variable;

[0046] By introducing the adversarial learning strategy, adversarial learning is performed on the current population individuals, the adversarial individuals of the individuals in the search space are calculated, and the adversarial individuals are evaluated as candidate solutions. The update formula is:

[0047] OPPO_X=lb+ub-X new

[0048] Among them, OPPO_X is the opposite solution generated based on X_new, and X_new is the new solution obtained by the previous calculation.

[0049] In another implementation, the method further includes updating the escape energy factor of the algorithm through an adaptive operator, and the updated escape energy formula is:

[0050]

[0051] E0=2·rand(0,1)-1

[0052] E=E0·E1

[0053] Among them, E1 is the escape energy factor, E0 is the random energy, t represents the current number of iterations, T is the maximum number of iterations, f(X i ) is the fitness value of an individual, is the average fitness of the population, and n is the population size.

[0054] According to a second aspect of an embodiment of the present invention, a gas storage reservoir gas injection scheduling optimization system based on an improved Harris Eagle algorithm is provided, comprising: a data acquisition module, used to acquire pipeline data and ground data of an underground gas storage reservoir; a model construction module, used to construct an underground gas storage reservoir gas injection scheduling model based on the pipeline data and ground data of the underground gas storage reservoir, the underground gas storage reservoir gas injection scheduling model including the definition of an objective function and the setting of constraints, the objective function including the compressor unit operating cost and the gas storage reservoir storage cost, the constraints including node constraints, pipeline constraints, compressor unit constraints and gas storage reservoir constraints; a solution module, used to solve the underground gas storage reservoir gas injection scheduling model by using the improved Harris Eagle algorithm to determine the optimal gas injection scheduling strategy for the underground gas storage reservoir.

[0055] 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.

[0056] 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.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (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.

[0059] (2) Improve efficiency: The present invention utilizes advanced algorithms and optimization models to improve computing efficiency and reduce computing time and resource consumption.

[0060] (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 operating efficiency and safety of the gas storage facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a flowchart of the steps of the gas storage gas injection scheduling optimization method based on the improved Harris Eagle algorithm.

[0062] Figure 2 For Figure 1 Corresponding to the specific flow chart of the gas storage injection scheduling optimization method based on the improved Harris Hawk algorithm of the present invention.

[0063] Figure 3 A comparison chart between different algorithms and the improved Harris Eagle optimization algorithm. DETAILED DESCRIPTION

[0064] 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.

[0065] See also Figure 1-Figure 3 , the steps of the embodiment of the present invention include:

[0066] Step S1, obtaining pipeline data and ground data of the underground gas storage;

[0067] Step S2, based on the pipeline data and ground data of the underground gas storage, constructing a gas injection scheduling model for the underground gas storage, the gas injection scheduling model for the underground gas storage including the definition of an objective function and the setting of constraints, the objective function including the compressor unit operating cost and the gas storage cost, the constraints including node constraints, pipeline constraints, compressor unit constraints and gas storage constraints;

[0068] Step S3, solving the gas injection scheduling model of the underground gas storage reservoir by improving the Harris Eagle algorithm to determine the optimal gas injection scheduling strategy of the underground gas storage reservoir.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] (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.

[0071] (2) Improve efficiency: The present invention utilizes advanced algorithms and optimization models to improve computing efficiency and reduce computing time and resource consumption.

[0072] (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 operating efficiency and safety of the gas storage facilities.

[0073] Specifically, the scheme of the present invention is further described according to the following examples:

[0074] Objective function:

[0075] First, the present invention takes the gas storage in document 23 as the research object and optimizes the gas storage gas injection scheduling model. The gas injection scheduling model of the underground gas storage aims to minimize the gas storage gas injection process cost, including the compressor unit operating cost C compressor and gas storage cost C storge, its mathematical formula is as follows:

[0076] min C=min(C compressor +C storge ) (1)

[0077] Among them, C compressor is the operating cost of the compressor unit, C storge Storage costs for gas storage.

[0078] (1) Compressor unit operating cost

[0079] When the gas storage ground system receives the natural gas delivered from upstream, it will be separated, cooled, dehydrated, and then injected. The model of the present invention chooses to use a motor-driven compressor as the core equipment of the ground equipment group. This choice not only helps to avoid the costs associated with carbon emissions, but also reduces the operating and management costs required for using a gas-driven compressor group.

[0080] In addition, motor-driven compressors do not require a dedicated gas-driven equipment maintenance team, further reducing the complexity and cost of maintenance. The mathematical model of the compressor unit operating cost is as follows:

[0081] C compressor =P gas D t W com n com (2)

[0082] Among them, P gas Indicates the unit electricity price, D t Indicates the compressor running time, W com Indicates the power of the compressor, n com Indicates the number of compressors in operation.

[0083] (2) Gas storage cost

[0084] The storage cost of a gas storage facility reflects the costs incurred to ensure smooth injection or extraction of natural gas under different filling conditions of the gas storage facility. These costs may involve equipment maintenance, energy consumption, and other related operating expenses. In particular, as the storage capacity increases or decreases, the operating efficiency of the system and the load conditions of the equipment will change, thus affecting the overall operating cost. Its mathematical expression is as follows:

[0085] C storge =P storge G storge (3)

[0086] G storge =D t ×W com (4)

[0087] Among them, P storge G represents the unit storage cost coefficient of the gas storage, including the costs of gas storage construction, equipment procurement, and daily maintenance costs. storge Indicates the gas storage capacity.

[0088] Constraints:

[0089] (1) Node constraints:

[0090] According to the law of conservation of flow, for a gas storage system, the sum of the inflow of the gas source and the boost flow of the compressor station must be equal to the injection flow of the gas storage. In other words, the total amount of natural gas entering the gas storage is equal to the amount of natural gas supplied by the gas source plus the amount of natural gas boosted by the compressor. This constraint ensures the material balance within the system. The mathematical formula of the node constraint is as follows:

[0091] Q source ≥Q inject (5)

[0092] Among them, Q source Indicates the natural gas flow rate output by the gas source, Q inject Indicates the natural gas flow rate injected into the injection-production well.

[0093] (2) Pipeline 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.

[0095] Specifically, changes in pipeline pressure not only affect the transmission 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 for the constraints of pipeline pressure. The pipeline constraint is expressed as follows:

[0096]

[0097] Among them, q ij represents the flow rate from section i to j in the pipeline, 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.

[0098] (3)Compressor unit constraints:

[0099] Compressor stations are usually composed of multiple compressors that work together to regulate the flow and pressure of natural gas. In order to ensure the safe operation and optimal efficiency of the compressor station, the flow of each compressor needs to be strictly constrained and managed. These constraints include minimum and maximum flow limits for each compressor to prevent overload or no-load operation.

[0100] In addition, the total flow limit of the compressor unit must also be considered to ensure the smooth operation of the overall system and prevent it from exceeding the design capacity. The setting of these flow constraints not only affects the operational stability of the compressor station, but is also directly related to the safety and reliability of the natural gas transmission network. The specific flow constraints are as follows:

[0101] Q com =n com q com (8)

[0102] 0≤W com ≤maxW com (9)

[0103]

[0104] Among them, q com Refers to the flow rate of a single compressor; μ is a variable index, generally taken as 1.28; Z1 is the gas compression factor at the compressor inlet, generally taken as 1; R s is the gas constant, which is generally 475; T1 is the compressor inlet temperature; η m is the mechanical efficiency of the compressor, which is generally taken as 0.7; η pol is the variable efficiency of the compressor, which is generally taken as 0.9. ε is the pressure ratio of the pipe section, which generally ranges from 1.1 to 1.6. Here it is taken as 1.4.

[0105] (4) Gas storage constraints:

[0106] 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.

[0107] The first is the storage capacity constraint. The actual storage capacity of the gas storage facility must be within its designed storage capacity. It cannot exceed the maximum storage capacity, nor be lower than the minimum safety stock.

[0108] The second is the constraints on gas injection and production rates, including that the daily amount of natural gas injected or produced should be within the bearing capacity of the equipment and pipeline system to avoid equipment damage or safety hazards caused by excessively high rates.

[0109] In addition, pressure constraint is also one of the key factors. The internal pressure of the gas storage must be maintained between the preset upper and lower limits to ensure structural safety and operational stability. These constraints jointly ensure the safe operation and effective management of the gas storage. The specific mathematical expression is as follows:

[0110] G U =G base +G storge (11)

[0111] G U ≤max G U (12)

[0112] Q inject =n well q well D t (13)

[0113] min q well ≤q well ≤max q well (14)

[0114] Among them, G base Indicates the gas volume of the cushion layer; n well represents the number of injection and production wells, q well Indicates the flow rate of the injection and production well.

[0115] In order to obtain better optimization results and improve the speed of model solving, the present invention chooses to use the swarm intelligence optimization algorithm as the solving tool, and after many experiments, chooses to use the improved Harris Eagle optimization algorithm as the final solving tool.

[0116] The specific steps of solving the gas injection scheduling model of underground gas storage by improving the Harris Hawk algorithm include: initializing Harris Hawk parameters; introducing Cauchy mutation and adversarial learning strategies to update the initialization strategy of the population; calculating the fitness value, recording the optimal position of the population and the global optimal fitness value; updating the population position, judging whether the number of iterations reaches the termination condition; and outputting the minimum operating cost.

[0117] Initialize Harris Hawk parameters. According to the underground gas storage information and constraints, set the initial Harris Hawk parameters. The initial Harris Hawk parameters include population size N, individual latitude D, maximum number of iterations T, individual variable upper limit ub, and individual variable lower limit lb.

[0118] The Cauchy mutation and adversarial learning strategies update the population initialization strategy, specifically including: using Cauchy mutation to mutate the individuals in the population, and then using the adversarial learning strategy to generate adversarial individuals.

[0119] It should be understood that the Cauchy mutation and adversarial learning strategies can increase the diversity of the initial population of the algorithm and avoid premature convergence of the algorithm.

[0120] Use the updated population for calculations, and record the optimal position of the population and the global optimal fitness value. Specifically, according to the objective function in the underground gas storage gas injection scheduling model, calculate the current fitness value of each individual, measure the quality of the individual position according to the size, take the minimum fitness value that appears in the iteration process as the optimal fitness value, and take the position corresponding to the optimal fitness value in the iteration process as the optimal position of the individual.

[0121] Then, the adaptive operator is used to adjust the algorithm escape energy factor, which improves the convergence speed of the algorithm and the diversity of the search, thus improving the quality of the solution.

[0122] Finally, determine whether the number of iterations reaches the condition and whether the number of iterations reaches the maximum number of iterations T. If the number of iterations reaches the maximum number of iterations, the iteration is completed and the minimum running cost is output, otherwise it returns to receive population initialization.

[0123] Exemplarily, the present invention uses Cauchy mutation to increase the diversity of the population and generate an initial solution for the population. The updated population initialization formula is:

[0124] Q=(Cauchy(β)·β) (15)

[0125] X new =(lb+(ub-lb)·rand(0,1))+Q (16)

[0126] Among them, Cauchy(β) is the Cauchy distribution, β is a scaling factor, usually used to adjust the step size, lb is the lower bound of the variable, ub is the upper bound of the variable, both of which can be customized, and rand(0,1) is a uniformly distributed random number generator that generates random numbers between 0 and 1, which is used to randomly explore different values ​​of the variable;

[0127] Secondly, an adversarial learning strategy is added to conduct adversarial learning on the current population individuals, calculate the adversarial individuals of the individuals in the search space, and evaluate these adversarial individuals as candidate solutions to enhance the global search capability of the algorithm and improve the quality of the solution. The update formula is as follows:

[0128] OPPO_X=lb+ub-X new (17)

[0129] Among them, OPPO_X is the opposite solution generated based on X_new, and X_new is the new solution obtained by the previous calculation.

[0130] Finally, the adaptive operator is used to adjust the escape energy factor update of the algorithm, so that the algorithm pays more attention to the exploration of the solution space, strengthens the global search ability of the algorithm, and avoids the algorithm from converging prematurely. The updated escape energy formula is as follows:

[0131]

[0132] E0=2·rand(0,1)-1 (19)

[0133] E=E0·E1 (20)

[0134] Among them, E1 is the escape energy factor, E0 is the random energy, t represents the current number of iterations, T is the maximum number of iterations, f(X i ) is the fitness value of an individual, is the average fitness of the population, and n is the population size.

[0135] The present invention reduces the operation cost of gas storage, improves economic profit and operational efficiency by establishing a gas storage injection scheduling model, and provides a powerful tool for energy management. Through advanced data-driven decision-making and intelligent control, the model provides flexibility and adaptability, can adapt to different needs and market conditions, and has high practical value and economic benefits.

[0136] In addition, in addition to using the improved Harris Eagle optimization algorithm as a solution tool, other alternatives can also be used to achieve the same purpose of the invention for the technical solution of the gas storage gas production optimization model.

[0137] Specifically, other meta-heuristic algorithms can be considered, such as particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA), which perform well in global optimization and solving complex problems. In addition to meta-heuristic algorithms, other optimization models, such as linear programming, mixed integer linear programming (MILP), dynamic programming (DP), etc. can also be used for gas storage scheduling optimization. These models 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.

[0138] 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:

[0139] Table 1

[0140]

[0141] In Table 1, CS, BAT, WOA, and IHHO are simulated annealing algorithm, bat optimization algorithm, whale optimization algorithm, and improved Harris Hawk optimization algorithm, respectively.

[0142] According to another aspect of an embodiment of the present invention, a gas storage gas injection scheduling optimization system based on an improved Harris Eagle algorithm is provided, comprising:

[0143] A data acquisition module, used to acquire pipeline data and ground data of underground gas storage;

[0144] A model building module is used to build an underground gas storage gas injection scheduling model based on the pipeline data and ground data of the underground gas storage. The underground gas storage gas injection scheduling model includes the definition of the objective function and the setting of constraint conditions. The objective function includes the compressor unit operation cost and the gas storage cost. The constraint conditions include node constraints, pipeline constraints, compressor unit constraints and gas storage constraints.

[0145] The solution module is used to solve the gas injection scheduling model of the underground gas storage reservoir by improving the Harris Eagle algorithm to determine the optimal gas injection scheduling strategy of the underground gas storage reservoir.

[0146] The gas storage gas injection scheduling optimization system based on the improved Harris Eagle 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 method for optimizing gas injection scheduling of a gas storage facility based on an improved Harris Eagle algorithm, characterized in that: include: Obtain pipeline data and surface data for underground gas storage; Based on the pipeline data and ground data of the underground gas storage, a gas injection scheduling model for the underground gas storage is constructed. The gas injection scheduling model for the underground gas storage includes the definition of the objective function and the setting of constraint conditions. The objective function includes the operation cost of the compressor unit and the storage cost of the gas storage. The constraint conditions include node constraints, pipeline constraints, compressor unit constraints and gas storage constraints. The gas injection scheduling model of underground gas storage is solved by improving Harris Eagle algorithm to determine the optimal gas injection scheduling strategy of underground gas storage.

2. The method according to claim 1, characterized in that The gas injection scheduling model of underground gas storage takes minimizing the gas injection process of underground gas storage as the objective function. The gas injection scheduling model of underground gas storage is expressed as: minC=min(C compressor +C storge ) Among them, C compressor is the operating cost of the compressor unit, C storge Storage costs for gas storage.

3. The method according to claim 2, characterized in that The operating cost of the compressor unit is expressed as: C compressor =P gas D t W com n com Among them, P gas Indicates the unit electricity price, D t Indicates the compressor running time, W com Indicates the power of a single compressor when it is working, n com Indicates the number of compressors in operation; The storage cost of gas storage is expressed as: C storge =P storge G storge G storge =D t ×W com Among them, P storge G represents the unit storage cost coefficient of the gas storage, including the costs of gas storage construction, equipment procurement, and daily maintenance costs. storge Indicates the gas storage capacity.

4. The method according to claim 3, characterized in that The node constraints are expressed as: Q source ≥Q inject Among them, Q source Indicates the natural gas flow rate output by the gas source, Q inject Indicates the natural gas flow rate injected into the injection-production well; The pipeline constraint is expressed as: Among them, q ij represents the flow rate from section i to j in the pipeline, 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 compressor train constraints are expressed as: Q com =n com q com 0≤W com ≤maxW com Among them, q com is the flow rate of a single compressor, μ is the polynomial index, Z1 is the gas compression factor at the compressor inlet, R s is the gas constant, T1 is the compressor inlet temperature, η m is the compressor mechanical efficiency, η pol is the variable efficiency of the compressor, ε is the pressure ratio of the pipe section; The gas storage constraint is expressed as: G U =G base +G storge G U ≤maxG U Q inject =n well q well D t minq well ≤q well ≤maxq well Among them, G base Indicates the cushion gas volume, n well represents the number of injection and production wells, q well Indicates the flow rate of the injection and production well.

5. The method according to claim 4, characterized in that The specific steps of solving the gas injection scheduling model of underground gas storage by improving the Harris Eagle algorithm include: Initialize Harris Eagle parameters; Introduce Cauchy mutation and adversarial learning strategies to update the population initialization strategy; Calculate the fitness value, record the optimal position of the population and the global optimal fitness value; Update the population position and determine whether the number of iterations reaches the termination condition; Output the minimum operating cost.

6. The method according to claim 5, characterized in that By introducing Cauchy mutation to increase the diversity of the population and generate the initial solution of the population, the updated population initialization formula is: Q = (Cauchy (β) β) X new =(lb+(ub-lb)·rand(0,1))+Q Among them, Cauchy(β) is the Cauchy distribution, β is a scaling factor, usually used to adjust the step size, lb is the lower bound of the variable, ub is the upper bound of the variable, both of which can be customized, and rand(0,1) is a uniformly distributed random number generator that generates random numbers between 0 and 1, which is used to randomly explore different values ​​of the variable; By introducing the adversarial learning strategy, adversarial learning is performed on the current population individuals, the adversarial individuals of the individuals in the search space are calculated, and the adversarial individuals are evaluated as candidate solutions. The update formula is: OPPO_X=lb+ub-X new Among them, OPPO_X is the opposite solution generated based on X_new, and X_new is the new solution obtained by the previous calculation.

7. The method according to claim 6, characterized in that The method further includes updating the escape energy factor of the algorithm through an adaptive operator, and the updated escape energy formula is: E0=2·rand(0,1)-1 E=E0·E1 Among them, E1 is the escape energy factor, E0 is the random energy, t represents the current number of iterations, T is the maximum number of iterations, f(X i ) is the fitness value of an individual, is the average fitness of the population, and n is the population size.

8. A gas storage gas injection scheduling optimization system based on the improved Harris Eagle algorithm, characterized in that: include: A data acquisition module, used to acquire pipeline data and ground data of underground gas storage; A model building module is used to build an underground gas storage gas injection scheduling model based on the pipeline data and ground data of the underground gas storage. The underground gas storage gas injection scheduling model includes the definition of the objective function and the setting of constraint conditions. The objective function includes the compressor unit operation cost and the gas storage cost. The constraint conditions include node constraints, pipeline constraints, compressor unit constraints and gas storage constraints. The solution module is used to solve the gas injection scheduling model of the underground gas storage reservoir by improving the Harris Eagle algorithm to determine the optimal gas injection scheduling strategy of the underground gas storage reservoir.

9. An electronic device, characterized in that: include: processor; 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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