Hydrogen refueling station network planning optimization method based on double-layer network effect

Abstract

The invention discloses a hydrogen refueling station network planning optimization method based on a double-layer network effect, and the method comprises the steps: constructing a double-layer network effect model based on a hydrogen refueling station network, a demand point network and the matching degree of the hydrogen refueling station network and the demand point network, the hydrogen refueling station network comprises a plurality of hydrogen refueling stations, and the demand point network comprises a plurality of demand points; the hydrogen refueling station network and the demand point network are mutually connected through a supply-demand relationship; based on the double-layer network effect model, a hydrogen refueling station network planning optimization model is constructed by taking the minimum total construction and operation cost of the hydrogen refueling station network and the maximum double-layer network effect as targets; the hydrogen refueling station network planning optimization model is solved, an optimal hydrogen refueling station network planning scheme is obtained, and the optimal hydrogen refueling station network planning scheme comprises the position of each hydrogen refueling station and a hydrogen supply scheme. According to the invention, the optimized layout of the whole hydrogen refueling station network can be realized, the hydrogen energy supply efficiency of the hydrogen refueling station network is improved, and the operation cost of the hydrogen refueling station network is reduced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a hydrogen refueling station network planning optimization method based on a double-layer network effect. Background Art

[0002] As hydrogen energy infrastructure, hydrogen refueling stations are the core component of the entire hydrogen energy industry chain. my country has proposed to build 1,000 hydrogen refueling stations by 2025; by the end of June 2024, my country has built 507 hydrogen refueling stations, covering 31 provinces, municipalities and autonomous regions. However, the low penetration rate and unreasonable layout of hydrogen refueling stations have seriously restricted the large-scale application and development of the hydrogen energy industry. The existing network layout of hydrogen refueling stations cannot effectively connect the supply and demand of hydrogen, and it is urgent to strengthen the optimization layout of the hydrogen refueling station network. At present, the research on the site selection of hydrogen refueling infrastructure such as hydrogen refueling stations mainly includes three aspects: hydrogen demand based on network nodes, traffic demand based on road networks, and comprehensive mathematical site selection models.

[0003] Among them, starting from the hydrogen demand of network nodes and combining with the classic location problem, the site selection research of hydrogen refueling stations and other facilities mainly includes the P median (or P center) model and the set covering model, as well as the improved models based on this. Models that determine the location of hydrogen refueling stations and other facilities according to the traffic demand of the road network include the flow capture model (FCLM) and the flow refueling location model (FRLM). Among them, the flow capture model (FCLM) aims to maximize the covered demand flow through a limited number of service facilities such as hydrogen refueling stations. The flow refueling location model (FRLM) aims to maximize the number of feasible round trips for vehicles, but this traffic flow can only be counted when the combination of infrastructure on the path can ensure that the vehicle completes the journey without running out of energy. Different from the site selection model that considers point demand and flow demand, some studies have established a comprehensive mathematical site selection model for hydrogen refueling stations by combining many factors of hydrogen production and application.

[0004] Existing research has laid an important theoretical foundation for the layout of hydrogen refueling station networks. However, the existing research still has the following problems: most studies on the planning and layout of hydrogen refueling station networks consider the cost for economic analysis, and few studies consider the overall perspective of the hydrogen refueling station network; in terms of the site selection of charging stations and gas stations, only the site selection of a single charging gas station is considered, and few studies involve the layout of the hydrogen refueling station network.

[0005] Therefore, a hydrogen station network planning optimization method based on double-layer network effect is needed to solve the above technical problems. Summary of the invention

[0006] To this end, the present invention provides a hydrogen station network planning optimization method based on a double-layer network effect to solve or at least alleviate the above problems.

[0007] According to one aspect of the present invention, a method for optimizing hydrogen refueling station network planning based on a double-layer network effect is provided, comprising: constructing a double-layer network effect model based on a hydrogen refueling station network, a demand point network, and the degree of matching between the hydrogen refueling station network and the demand point network, wherein the hydrogen refueling station network includes a plurality of hydrogen refueling stations, the demand point network includes a plurality of demand points, and the hydrogen refueling station network and the demand point network are interconnected through a supply and demand relationship; based on the double-layer network effect model, a hydrogen refueling station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect; and by solving the hydrogen refueling station network planning optimization model, an optimal hydrogen refueling station network planning scheme is obtained, wherein the optimal hydrogen refueling station network planning scheme includes the location of each hydrogen refueling station and a hydrogen supply scheme.

[0008] Optionally, in the hydrogen refueling station network planning optimization method based on the double-layer network effect according to the present invention, a double-layer network effect model is constructed based on the hydrogen refueling station network, the demand point network and the matching degree between the hydrogen refueling station network and the demand point network, including: based on a double-layer network framework including the hydrogen refueling station network and the demand point network, the double-layer network effect model is constructed by comprehensively considering the average coverage coefficient of the hydrogen refueling station, the average hydrogenation distance coefficient of the demand point and the matching coefficient between the hydrogen refueling station and the demand point; wherein the average coverage coefficient of the hydrogen refueling station is used to represent the hydrogen supply capacity of the hydrogen refueling station network, which is the normalized mean value of the number of covered demand points of the hydrogen refueling station; The average hydrogenation distance coefficient of the demand point is used to indicate the convenience of hydrogen supply of the hydrogenation station network, and is the normalized mean of the minimum hydrogenation distance of the demand point and the average hydrogenation distance; the matching coefficient between the hydrogenation station and the demand point is used to indicate the degree of matching between the hydrogenation station network and the demand point network, and the matching coefficient between the hydrogenation station and the demand point is the mean of the matching coefficient of the hydrogenation station and the matching coefficient of the demand point, wherein the matching coefficient of the hydrogenation station is the normalized mean of the proportion of the capacity allocated to the demand point to the capacity of the hydrogenation station, and the matching coefficient of the demand point is the normalized mean of the proportion of the demand at the demand point to the capacity allocated to the demand point.

[0009] Optionally, in the hydrogen station network planning optimization method based on the double-layer network effect according to the present invention, the double-layer network effect model is as follows:

[0010]

[0011] Among them, E is the double-layer network effect of the hydrogen refueling station network, E1 is the average coverage coefficient of the hydrogen refueling station, E2 is the average hydrogen refueling distance coefficient of the demand point, and E3 is the matching coefficient between the hydrogen refueling station and the demand point.

[0012] Optionally, in the hydrogen refueling station network planning optimization method based on the double-layer network effect according to the present invention, a hydrogen refueling station network planning optimization model is constructed with the goals of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect, including: constructing a first objective function with the minimum total construction and operation cost of the hydrogen refueling station network as the first goal; constructing a second objective function with the maximum double-layer network effect of the hydrogen refueling station network as the second goal; constructing a hydrogen refueling station network planning optimization model based on the first objective function and the second objective function.

[0013] Optionally, in the hydrogen refueling station network planning optimization method based on the double-layer network effect according to the present invention, the optimal hydrogen refueling station network planning scheme is obtained by solving the hydrogen refueling station network planning optimization model, including: using the subjective and objective integrated weighting method to determine the first comprehensive weight corresponding to the first objective function and the second comprehensive weight corresponding to the second objective function in the hydrogen refueling station network planning optimization model; constructing a comprehensive objective function based on the first objective function and the first comprehensive weight corresponding to the first objective function, as well as the second objective function and the second comprehensive weight corresponding to the second objective function; and obtaining the optimal hydrogen refueling station network planning scheme by solving the comprehensive objective function.

[0014] Optionally, in the hydrogen refueling station network planning optimization method based on the double-layer network effect according to the present invention, the optimal hydrogen refueling station network planning scheme is obtained by solving the hydrogen refueling station network planning optimization model, including: using an optimizer, based on the longitude and latitude of each hydrogen refueling station candidate point and the annual hydrogen supply, and the longitude and latitude of each demand point and the annual hydrogen demand, to solve the hydrogen refueling station network planning optimization model to obtain the optimal hydrogen refueling station network planning scheme.

[0015] Optionally, in the hydrogen refueling station network planning and optimization method based on the double-layer network effect according to the present invention, the total construction and operation cost of the hydrogen refueling station network includes the construction investment cost, operating cost and hydrogen storage cost of each hydrogen refueling station in the hydrogen refueling station network; wherein, the construction investment cost of the hydrogen refueling station includes the investment cost of various equipment required for the construction of the hydrogen refueling station and the cost of the land for the construction of the hydrogen refueling station; the operating cost of the hydrogen refueling station includes the maintenance cost and depreciation cost of the equipment in the station during the operation of the hydrogen refueling station; the hydrogen storage cost of the hydrogen refueling station is related to the unit storage cost of hydrogen and the construction capacity of the hydrogen refueling station.

[0016] Optionally, in the hydrogen station network planning optimization method based on the double-layer network effect according to the present invention, a hydrogen station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect, including: taking the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect as the goal, and considering the hydrogen demand constraints, supply and demand balance constraints, hydrogen station construction capacity constraints, hydrogen station floor space constraints, and hydrogen station quantity constraints to construct a hydrogen station network planning optimization model.

[0017] Optionally, in the hydrogen refueling station network planning optimization method based on the double-layer network effect according to the present invention, the hydrogen refueling demand constraint condition includes: calculating the annual hydrogen demand of each hydrogen demand point according to the predicted number of hydrogen fuel cell vehicles, the average daily hydrogen demand and the annual operating time of each hydrogen demand point; allocating the hydrogen demand of each hydrogen demand point to each hydrogen refueling demand point according to the Huff model to obtain the hydrogen refueling demand of each hydrogen refueling demand point; the supply and demand balance constraint condition includes: the hydrogen demand of the demand point for each hydrogen refueling station does not exceed the annual hydrogen supply of the hydrogen refueling station; when the demand point is within the service range of the hydrogen refueling station, the demand point is allowed to It is served by multiple hydrogen refueling stations, and the hydrogen supply to the demand point by any hydrogen refueling station does not exceed the hydrogen demand of the demand point itself; the construction capacity constraint conditions of the hydrogen refueling station include: the construction capacity of the hydrogen refueling station is greater than or equal to the minimum construction capacity of the hydrogen refueling station, and is less than or equal to the maximum construction capacity of the hydrogen refueling station, wherein the construction capacity of the hydrogen refueling station is determined according to the hydrogen demand of each demand point for the hydrogen refueling station and the safety reserve capacity; the floor area constraint condition of the hydrogen refueling station is: the floor area of ​​the hydrogen refueling station is greater than or equal to the minimum floor area of ​​the hydrogen refueling station, and is less than or equal to the maximum floor area of ​​the hydrogen refueling station; the number constraint condition of the hydrogen refueling station is: the number of hydrogen refueling stations is greater than or equal to the minimum number of hydrogen refueling stations.

[0018] According to one aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing the hydrogen station network planning optimization method based on the double-layer network effect as described above.

[0019] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the method as described above when executed by a processor.

[0020] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the hydrogen station network planning optimization method based on the double-layer network effect as described above.

[0021] According to the technical solution of the present invention, a hydrogen refueling station network planning optimization method based on a double-layer network effect is provided. First, a double-layer network effect model is constructed based on the hydrogen refueling station network, the demand point network, and the degree of matching between the hydrogen refueling station network and the demand point network. The network effect can be quantified from a system level to achieve optimization of the entire hydrogen refueling station network. Secondly, a hydrogen refueling station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect. The optimal hydrogen refueling station network planning scheme is determined by solving the hydrogen refueling station network planning optimization model, which can achieve an optimized layout of the hydrogen refueling station network, improve the hydrogen energy supply efficiency of the hydrogen refueling station network, and reduce the operating cost of the hydrogen refueling station network.

[0022] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and the accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present disclosure will become more apparent. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0024] Figure 1 A schematic diagram of a double-layer network framework 100 established according to an embodiment of the present invention is shown;

[0025] Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown;

[0026] Figure 3 A schematic flow chart of a hydrogen station network planning optimization method 300 based on a double-layer network effect provided according to an embodiment of the present invention is shown;

[0027] Figure 4 A schematic diagram of the distribution of hydrogen demand points in Beijing is shown;

[0028] Figure 5 A schematic diagram showing the distribution of candidate hydrogen refueling stations in Beijing is shown;

[0029] Figure 6 A schematic diagram of model effectiveness analysis according to some embodiments of the present invention is shown;

[0030] Figure 7A schematic diagram showing the location distribution of hydrogen refueling stations in Beijing is shown;

[0031] Figure 8 A schematic diagram showing the relationship between hydrogen refueling stations providing hydrogen refueling services to demand points;

[0032] Fig. 9 A schematic diagram of sensitivity analysis of the service radius of a hydrogen refueling station is shown;

[0033] Fig.10 A schematic diagram of sensitivity analysis of demand quantity is shown;

[0034] Fig.11 A schematic diagram of the change rate of hydrogen operation subsidies is shown. DETAILED DESCRIPTION

[0035] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] In view of the fact that the existing schemes for hydrogen station network planning and layout only consider economic costs but not the entire hydrogen station network, and only consider the site selection of a single charging gas station but not the layout of the hydrogen station network, an embodiment of the present invention proposes a hydrogen station network planning optimization method based on a double-layer network effect. Firstly, a double-layer network effect model is constructed based on the hydrogen station network, the demand point network, and the degree of matching between the hydrogen station network and the demand point network. The network effect can be quantified from a system level to achieve optimization of the entire hydrogen station network. Secondly, a hydrogen station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect. The optimal hydrogen station network planning scheme is determined by solving the hydrogen station network planning optimization model. This can achieve an optimized layout of the hydrogen station network, improve the hydrogen energy supply efficiency of the hydrogen station network, and reduce the operating cost of the hydrogen station network.

[0037] According to the hydrogen refueling station network planning optimization method based on the double-layer network effect provided by the embodiment of the present invention, a double-layer network framework including the hydrogen refueling station network and the demand point network is pre-established, and a double-layer network effect model is established on the basis of the double-layer network framework. Then, based on the double-layer network effect model, a hydrogen refueling station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect. Below, the double-layer network framework established according to the embodiment of the invention is first introduced.

[0038] Figure 1 A schematic diagram of a double-layer network framework 100 established according to an embodiment of the present invention is shown.

[0039] like Figure 1 As shown, the double-layer network framework 100 includes a hydrogenation station network and a demand point network. The hydrogenation station network includes a plurality of hydrogenation stations, and the demand point network includes a plurality of demand points. The hydrogenation station network and the demand point network are interconnected through a supply and demand relationship.

[0040] Specifically, in the double-layer network framework 100, each hydrogen refueling station is used as a hydrogen refueling station network node, and each demand point is used as a demand point network node. Among them, each hydrogen refueling station in the hydrogen refueling station network is connected to each other through roads, and each demand point in the demand point network is connected to each other through roads. Each hydrogen refueling station in the hydrogen refueling station network is connected to each demand point in the demand point network based on the supply and demand relationship. It can be seen that the double-layer network framework established according to the embodiment of the present invention is a double-layer complex network with hydrogen refueling stations and demand points as network nodes and supply and demand relationships as edges.

[0041] It can be understood that the double-layer network framework 100 established according to the embodiment of the present invention includes multiple hydrogen refueling stations, multiple demand points, and multiple roads. It should be noted that the hydrogen refueling station is the hydrogen refueling infrastructure for hydrogen fuel cell vehicles, which is responsible for storing hydrogen in high pressure or low temperature and delivering it to the vehicle when needed. The demand point is a geographically concentrated area of ​​hydrogen energy demand, representing the demand aggregation of hydrogen refueling for hydrogen fuel cell vehicles in a specific area, and is usually located in logistics bases, wholesale markets, railway freight yards, and major passenger and freight channels. The road is an important infrastructure in the network and has the following four functions: used to connect various hydrogen refueling stations to form a hydrogen refueling station network to ensure connectivity between various hydrogen refueling sites; used to connect various demand points to form a demand point network to connect various demand points; used to connect the hydrogen refueling station network with the demand point network to achieve cross-network connection and directly connect the supply and demand of hydrogen energy; as a channel for hydrogen energy delivery, hydrogen is delivered from the hydrogen refueling station to the demand point to ensure that hydrogen energy reaches the demand end from the supply end.

[0042] The double-layer network framework 100 established according to the embodiment of the present invention has the characteristics of double-layer structure, supply and demand coupling, and multi-scale characteristics. Specifically, the double-layer structure is mainly reflected in that the double-layer network framework is composed of two different network layers, namely the hydrogen refueling station network and the demand point network. The nodes within each network layer are interconnected by roads, forming two independent networks. Supply and demand coupling is mainly reflected in that the hydrogen refueling station network and the demand point network of the double-layer network framework are interconnected through the supply and demand relationship. This coupling relationship is the core feature of the double-layer network and reflects the dynamic balance between hydrogen energy supply and demand. The multi-scale characteristics are mainly reflected in that the double-layer network framework can capture the multi-scale characteristics from a single node to the overall network.

[0043] In an embodiment of the present invention, the computing device can be configured to execute the hydrogen refueling station network planning optimization method 300 based on the double-layer network effect to achieve the optimized layout of the entire hydrogen refueling station network, improve the hydrogen energy supply efficiency of the hydrogen refueling station network, and reduce the operating cost of the hydrogen refueling station network. The hydrogen refueling station network planning optimization method 300 based on the double-layer network effect of the present invention will be described below.

[0044] A computing device 200 provided by an embodiment of the present invention is introduced below.

[0045] Figure 2 FIG. 2 is a schematic diagram of a computing device 200 provided according to an embodiment of the present invention. Figure 2 As shown, in a basic configuration, the computing device 200 includes at least one processing unit 202 and a system memory 204. According to one aspect, depending on the configuration and type of the computing device, the processing unit 202 can be implemented as a processor. The system memory 204 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 204 includes an operating system 205.

[0046] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 2 The basic configuration is shown in FIG. 2 by those components within the dashed lines. According to one aspect, the computing device 200 has additional features or functionality. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 2 2 is illustrated by a removable storage device 209 and a non-removable storage device 210.

[0047] As stated above, according to one aspect, a program module 203 is stored in the system memory 204. According to one aspect, the program module 203 may include one or more application programs, and the present invention does not limit the type of application program, for example, the application program may include: email and contact application programs, word processing application programs, spreadsheet application programs, database application programs, slide show application programs, drawing or computer-aided application programs, web browser application programs, etc.

[0048] In an embodiment according to the present invention, the program module 203 includes a plurality of program instructions for executing the hydrogen station network planning optimization method 300 based on the double-layer network effect of the present invention.

[0049] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 2 Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in the present invention can be operated via a dedicated logic integrated with other components of the computing device 200 on a single integrated circuit (chip). Other technologies capable of performing logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present invention, including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0050] According to one aspect, the computing device 200 may also have one or more input devices 212, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 214 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitters, receivers and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0051] The term computer-readable medium as used in the present invention includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage device 209, and non-removable storage device 210 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage medium may be part of computing device 200. Computer storage media do not include carrier waves or other propagated data signals.

[0052] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0053] In an embodiment according to the present invention, the computing device 200 is configured to execute the hydrogen refueling station network planning optimization method 300 based on the double-layer network effect. The computing device 200 includes one or more processors and one or more readable storage media storing program instructions. When the program instructions are configured to be executed by one or more processors, the computing device executes the hydrogen refueling station network planning optimization method 300 based on the double-layer network effect in the embodiment of the present invention.

[0054] In some embodiments, the computing device 200 executing the hydrogen station network planning optimization method 300 based on double-layer network effect in the embodiment of the present invention may be a terminal or a server.

[0055] The following is a detailed description of the hydrogen station network planning optimization method 300 based on the double-layer network effect in an embodiment of the present invention.

[0056] Figure 3FIG. 3 is a flow chart of a hydrogen station network planning optimization method 300 based on a double-layer network effect according to an embodiment of the present invention. Figure 3 As shown, the hydrogen station network planning optimization method 300 based on the double-layer network effect includes the following steps 310-330.

[0057] Step 310: The computing device 200 may construct a two-layer network effect model based on the hydrogen refueling station network, the demand point network, and the degree of matching between the hydrogen refueling station network and the demand point network.

[0058] It should be noted that the hydrogen refueling station network includes multiple hydrogen refueling stations, and the demand point network includes multiple demand points. The hydrogen refueling station network and the demand point network are interconnected through a supply and demand relationship.

[0059] Step 320, the computing device 200 can construct a hydrogen station network planning optimization model (dual-objective optimization model) based on the double-layer network effect model, with the goals of minimizing the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect of the hydrogen station network.

[0060] Step 330: The computing device 200 may obtain an optimal hydrogen refueling station network planning scheme by solving the hydrogen refueling station network planning optimization model, wherein the optimal hydrogen refueling station network planning scheme includes the location of each hydrogen refueling station and the hydrogen supply scheme.

[0061] In the embodiment of the present invention, a double-layer network framework 100 including a hydrogen refueling station network and a demand point network may be pre-established. In step 310, the computing device 200 may construct a double-layer network effect model based on the double-layer network framework including a hydrogen refueling station network and a demand point network, taking into account the average coverage coefficient of hydrogen refueling stations, the average hydrogen refueling distance coefficient of demand points, and the matching coefficient between hydrogen refueling stations and demand points.

[0062] As described above, in the two-layer network framework 100, the hydrogen refueling station network and the demand point network are interconnected through the supply and demand relationship. Each hydrogen refueling station serves as a hydrogen refueling station network node, and each demand point serves as a demand point network node. Among them, each hydrogen refueling station in the hydrogen refueling station network is interconnected through roads, and each demand point in the demand point network is connected to each other through roads. Each hydrogen refueling station in the hydrogen refueling station network is connected to each demand point in the demand point network based on the supply and demand relationship.

[0063] It should be noted that the network effect of a complex network refers to the phenomenon that the performance of products or services in the network improves and becomes more efficient as the number of network nodes increases. The coverage and service efficiency of the hydrogen refueling station network can be measured by the average coverage coefficient of hydrogen refueling stations, and the accessibility and convenience of hydrogen refueling services can be evaluated by the average hydrogen refueling distance coefficient of demand points. In addition, the supply and demand relationship between the hydrogen refueling station network and the demand point network can be quantified by the matching coefficient to ensure that the layout of hydrogen refueling stations matches the hydrogen energy demand of the demand points.

[0064] In the embodiment of the present invention, the average coverage coefficient of the hydrogen refueling station is used to represent the hydrogen supply capacity of the hydrogen refueling station network, and is the normalized average value of the number of coverage demand points of the hydrogen refueling station.

[0065] In some embodiments, the average coverage factor of the hydrogen refueling station can be calculated by the following formulas (1)-(5). The average coverage factor of the hydrogen refueling station is specifically shown in formula (1):

[0066]

[0067] Where E1 is the average coverage factor of hydrogen refueling stations (the average coverage demand point index of the hydrogen refueling station network), P j ' is the normalized result of the number of demand points covered by hydrogen refueling station j; X j is a 0-1 variable, where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. j A value of 1 indicates that the hydrogen refueling station candidate point j is selected as the hydrogen refueling station.

[0068]

[0069] P J = {P1,P2,P3,…,P n},j∈J (3)

[0070] As shown in formula (2) and formula (3), P max P J The maximum value, P min P J The minimum value, P j is the number of demand points covered by hydrogen refueling station j, P J The set of the number of demand points covered by the hydrogen refueling station.

[0071]

[0072] As shown in formula (4) and formula (5), d ij is the distance from demand point i to hydrogen refueling station j, c ij is the coverage factor of hydrogen refueling station j to demand point i, R jThe coverage coefficient is the maximum distance allowed from the demand point to the hydrogen refueling station, that is, the service radius of the hydrogen refueling station. When the distance between the demand point and the hydrogen refueling station is within the service radius of the hydrogen refueling station, the coverage coefficient is 1, otherwise it is 0.

[0073] In the embodiment of the present invention, the average hydrogenation distance coefficient of the demand point is used to indicate the convenience of hydrogen supply in the hydrogenation station network, and is the normalized mean value of the minimum hydrogenation distance of the demand point compared with the average hydrogenation distance.

[0074] In some embodiments, the average hydrogenation distance coefficient of the demand point can be calculated by the following equations (6)-(11).

[0075] The average hydrogenation distance coefficient of the demand point is specifically shown in formula (6):

[0076]

[0077] Where, E2 is the average hydrogenation distance coefficient of the demand point, D i ’ is the normalized result of the hydrogenation distance coefficient of demand point i, and m is the number of demand points.

[0078]

[0079] D I ={D1,D2,D3,…,D m},i∈I (9)

[0080] As shown in formula (7), formula (8) and formula (9), D i is the hydrogenation distance coefficient of demand point i, D I is the set of hydrogenation distance coefficients of demand points, D max D I The maximum value, D min D I The minimum value of .

[0081]

[0082]

[0083] As shown in formula (10) and formula (11), d ij is the distance from demand point i to hydrogen refueling station j, D 1 i is the minimum hydrogenation distance of demand point i, D 2 i is the average hydrogenation distance of demand point i; X j is a 0-1 variable (decision variable), where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. jA value of 1 indicates that the hydrogen refueling station candidate point j is selected as the hydrogen refueling station.

[0084] In an embodiment of the present invention, the matching coefficient between the hydrogen refueling station and the demand point is used to indicate the degree of matching between the hydrogen refueling station network and the demand point network. Specifically, the matching coefficient between the hydrogen refueling station and the demand point is related to two parts: one is the matching coefficient of the hydrogen refueling station, which is the normalized average of the proportion of the capacity allocated to the demand point to the capacity of the hydrogen refueling station; the other is the matching coefficient of the demand point, which is the normalized average of the proportion of the demand at the demand point to the capacity allocated to the demand point. By averaging the matching coefficients of the hydrogen refueling station and the matching coefficients of the demand point, the matching coefficient between the hydrogen refueling station and the demand point can be obtained, that is, the matching coefficient between the hydrogen refueling station network and the demand point network (double-layer network). In other words, the matching coefficient between the hydrogen refueling station and the demand point is the average of the matching coefficient of the hydrogen refueling station and the matching coefficient of the demand point, wherein the matching coefficient of the hydrogen refueling station is the normalized average of the proportion of the capacity allocated to the demand point to the capacity of the hydrogen refueling station, and the matching coefficient of the demand point is the normalized average of the proportion of the demand at the demand point to the capacity allocated to the demand point.

[0085] In some embodiments, the matching coefficient between the hydrogen refueling station and the demand point can be calculated by the following formulas (12)-(22). The matching coefficient between the hydrogen refueling station and the demand point is specifically shown in formula (12):

[0086] E3=mean(E 31 ,E 32 ) (12)

[0087] Where, E3 is the matching coefficient between the hydrogen refueling station and the demand point, E 31 is the matching coefficient of the hydrogen refueling station, E 32 is the matching coefficient of the demand point.

[0088] The matching coefficient of the hydrogen refueling station is specifically shown in formula (13):

[0089]

[0090] In formula (13), e j ' is the normalized result of the ratio of the capacity of hydrogen station j allocated to the demand point to the capacity of hydrogen station j; X j is a 0-1 variable, where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. j A value of 1 indicates that the hydrogen refueling station candidate point j is selected as the hydrogen refueling station.

[0091]

[0092] e J ={e1,e2,e3,…,e n},j∈J,e j≠0 (15)

[0093] As shown in formula (14) and formula (15), e j is the ratio of the capacity of hydrogen refueling station j allocated to the demand point to the capacity of hydrogen refueling station j, e J is the ratio of the capacity of the hydrogen refueling station allocated to the demand point to the capacity of the hydrogen refueling station, e Jmax for e J The maximum value, e Jmin for e J The minimum value of .

[0094]

[0095] As shown in formula (16) and formula (17), x j is the capacity of hydrogen refueling station j allocated to the demand point, S j is the capacity of hydrogen refueling station j, X j Indicates whether the candidate point j of the hydrogen refueling station is selected as a hydrogen refueling station, x ij is the distance between hydrogen refueling station j and demand point i.

[0096] The matching coefficient of the demand point is specifically shown in formula (18):

[0097]

[0098] In formula (18), e i ' is the normalized result of the ratio of the demand of the demand point to the capacity allocated to the demand point, and m is the number of demand points.

[0099]

[0100] e I ={e1,e2,e3,…,e m},i∈I (20)

[0101] As shown in formula (19) and formula (20), e i is the ratio of the demand at demand point i to the capacity allocated to demand point i, e I is the ratio of the demand of the demand point to the capacity allocated to the demand point, e Imax for e I The maximum value, e Imin for e I The minimum value of .

[0102]

[0103] As shown in formula (21) and formula (22), G i is the demand quantity of demand point i, x i is the capacity allocated to demand point i, Xj Indicates whether the candidate point j of the hydrogen refueling station is selected as a hydrogen refueling station, x ij is the distance between hydrogen refueling station j and demand point i.

[0104] According to the above formulas (1)-(22), the average coverage coefficient of hydrogen refueling stations, the average hydrogen refueling distance coefficient of demand points, and the matching coefficient between hydrogen refueling stations and demand points can be determined. Based on this, the present invention can start from the three perspectives of hydrogen refueling station network, demand point network, and double-layer network architecture, and comprehensively consider the three indicators of the average coverage coefficient of hydrogen refueling stations, the average hydrogen refueling distance coefficient of demand points, and the matching coefficient between hydrogen refueling stations and demand points to construct a double-layer network effect model. The double-layer network effect model constructed according to the present invention can be expressed as the following formula (23).

[0105]

[0106] In formula (23), E represents the double-layer network effect of the hydrogen refueling station network, E1 is the average coverage coefficient of the hydrogen refueling station, E2 is the average hydrogen refueling distance coefficient of the demand point, and E3 is the matching coefficient between the hydrogen refueling station and the demand point.

[0107] That is to say, the double-layer network effect of the hydrogen refueling station network can be determined by the above formula (23).

[0108] Furthermore, in step 320, the computing device 200 may construct a hydrogen station network planning optimization model (i.e., a hydrogen station network planning dual-objective model) based on the double-layer network effect model, with the goals of minimizing the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect of the hydrogen station network.

[0109] It should be noted that the assumptions of the present invention when constructing the hydrogen refueling station network planning optimization model are as follows: Assumption 1. The construction and operation costs of each hydrogen refueling station are the same, and the built hydrogen refueling stations have no construction and operation costs; Assumption 2. The geographical locations of hydrogen refueling stations and demand points are known; Assumption 3. The supply of hydrogen sources to hydrogen refueling stations is sufficient and stable; Assumption 4. The distance between the demand point and the candidate hydrogen refueling station point is the Manhattan distance between the points.

[0110] In some embodiments, x can be preset ij , X j , S j The three decision variables are as follows:

[0111] x ij : The amount of hydrogen supplied from candidate hydrogen refueling station j to demand point i.

[0112] X j : 0-1 variable, where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. j1 indicates that the candidate point j is selected as the hydrogen station. j 1, the node X where the new hydrogen refueling station is located j is 1, as shown in formula (24) and formula (25).

[0113] X j ∈{0,1}(24)

[0114]

[0115] S j :The construction capacity of the candidate hydrogen station j and X j There is an inevitable relationship. Where M is a very large number, as shown in formula (26).

[0116]

[0117] Specifically, in step 220, the first objective function can be constructed with the minimum total construction and operation cost of the hydrogen refueling station network as the first objective; and the second objective function can be constructed with the maximum double-layer network effect of the hydrogen refueling station network as the second objective. Furthermore, a hydrogen refueling station network planning optimization model can be constructed based on the first objective function and the second objective function. Based on this, the hydrogen refueling station network planning optimization model constructed according to the present invention is a dual-objective optimization model, which includes the first objective function and the second objective function.

[0118] It should be noted that the total construction and operation cost of the hydrogen refueling station network includes the construction investment cost, operation cost and hydrogen storage cost of each hydrogen refueling station in the hydrogen refueling station network. That is, the total construction and operation cost of the hydrogen refueling station network is the sum of the construction investment cost, operation cost and hydrogen storage cost of each hydrogen refueling station in the hydrogen refueling station network. Based on this, the first objective function constructed in the embodiment of the present invention is shown in the following formula (27).

[0119] minC J =SC+SO+HS (27)

[0120] Where, J represents the set of candidate hydrogen refueling stations, J = {1, 2, 3, ..., n}. J is the total construction and operation cost of the hydrogen refueling station network, SC is the sum of the construction investment cost of each hydrogen refueling station in the hydrogen refueling station network, SO is the sum of the operating cost of each hydrogen refueling station in the hydrogen refueling station network, and HS is the sum of the hydrogen storage cost of each hydrogen refueling station in the hydrogen refueling station network.

[0121] Among them, the construction investment cost of a single hydrogen refueling station mainly includes the investment cost of various equipment required for the construction of the hydrogen refueling station (for example, compressors, hydrogen storage tanks, hydrogen refueling machines and other equipment) and the construction land cost of the hydrogen refueling station. By summing up the investment costs of various equipment required for the construction of each hydrogen refueling station, the sum of the equipment investment costs of all hydrogen refueling stations can be obtained. The construction land cost of a single hydrogen refueling station is related to the land price and floor area of ​​its location. By summing up the construction land costs of each hydrogen refueling station, the sum of the construction land costs of all hydrogen refueling stations can be obtained. The floor area of ​​a hydrogen refueling station is related to the construction capacity. According to the total capacity of the hydrogen storage container and the capacity of a single tank, the hydrogen refueling station can be divided into three levels. Specifically, the hydrogen refueling station can be divided into three levels according to the construction capacity of the hydrogen refueling station, and the floor area of ​​the hydrogen refueling station to be built can be determined based on the average floor area of ​​each level of hydrogen refueling station.

[0122] Specifically, the sum of the construction investment costs of each hydrogen refueling station in the hydrogen refueling station network can be determined by the following equations (28)-(33).

[0123]

[0124] In the formula, C Land is the land investment cost of the hydrogen refueling station, c j land is the land price at the location of hydrogen refueling station j, s j land is the area occupied by hydrogen refueling station j, S j is the construction capacity of hydrogen refueling station j; s1, s2, s3 are the average floor space of the first-level hydrogen refueling station, the second-level hydrogen refueling station, and the third-level hydrogen refueling station respectively; S1, S2, S3 are the capacity division boundaries of the first-level hydrogen refueling station, the second-level hydrogen refueling station, and the third-level hydrogen refueling station respectively, which can be 8000kg, 5000kg, and 3000kg respectively.

[0125] The construction of a hydrogen refueling station requires equipment such as compressors, hydrogen storage tanks, and hydrogen refueling machines. The investment cost of the various equipment required for the construction of a single hydrogen refueling station is composed of the cost of purchasing compressors, hydrogen storage tanks, hydrogen refueling machines, and other equipment, which is related to the unit price of the equipment, the rated capacity of the equipment, and the construction capacity of the hydrogen refueling station. The equipment investment cost required for the construction of a hydrogen refueling station is shown in formula (30), formula (31), and formula (32).

[0126]

[0127] In the formula, C press is the compressor investment cost of the hydrogen refueling station, C H2sto is the investment cost of hydrogen storage tanks at hydrogen refueling stations, C dispenser The investment cost of the hydrogen refueling machine at the hydrogen refueling station; cpress , c dispenserare the unit prices of compressor, hydrogen storage tank and hydrogen filling machine respectively; max(M press ), max(M dispenser ) are the rated daily hydrogen processing capacity of the compressor, hydrogen storage tank and hydrogen refueling machine respectively; S j is the construction capacity (daily service capacity) of hydrogen refueling station j.

[0128] The sum of the construction investment costs SC of each hydrogen refueling station in the hydrogen refueling station network is shown in formula (33):

[0129]

[0130] The operating cost of a single hydrogen refueling station mainly includes the maintenance cost and depreciation cost of the equipment in the station during operation. The sum of the operating costs of each hydrogen refueling station in the hydrogen refueling station network is shown in formula (34):

[0131]

[0132] In the formula, C o is the operating cost (annual operating cost) of a single newly built hydrogen refueling station. The hydrogen storage cost of a hydrogen refueling station is related to the unit storage cost of hydrogen and the construction capacity of the hydrogen refueling station. The sum of the hydrogen storage costs HS of each hydrogen refueling station in the hydrogen refueling station network is shown in formula (35):

[0133]

[0134] In the formula, C s is the unit storage cost of hydrogen, S j is the construction capacity of hydrogen refueling station j.

[0135] In the embodiment of the present invention, in order to maximize the double-layer network effect while minimizing the total construction and operation cost of the hydrogen refueling station network, a second objective function is constructed with the maximum double-layer network effect of the hydrogen refueling station network as the second objective. The second objective function is shown in formula (36):

[0136] E max =max(E J ) (36)

[0137] In the formula, E J The two-layer network effect of all possible hydrogen station network planning schemes, E max For E J The maximum value of . Among them, the double-layer network effect can be determined by the following formula (37) or the above formula (23).

[0138] E=mean(E1,E2,E3) (37)

[0139] Where E is the double-layer network effect of the hydrogen refueling station network, E1 is the average coverage coefficient of the hydrogen refueling station, E2 is the average hydrogen refueling distance coefficient of the demand point, and E3 is the matching coefficient between the hydrogen refueling station and the demand point.

[0140] In some embodiments, when constructing a hydrogen refueling station network planning optimization model, the present invention also takes into account hydrogen refueling demand constraints, supply and demand balance constraints, hydrogen refueling station construction capacity constraints, hydrogen refueling station floor space constraints, and hydrogen refueling station quantity constraints.

[0141] Specifically, in step 320, the computing device can take the minimum total construction and operation cost of the hydrogen station network and the maximum double-layer network effect as the goal, consider the hydrogenation demand constraint, the supply and demand balance constraint, the hydrogenation station construction capacity constraint, the hydrogenation station floor area constraint, and the hydrogenation station number constraint, and construct a hydrogenation station network planning optimization model. Further, by taking the minimum total construction and operation cost of the hydrogenation station network as the first goal to construct the first objective function, and taking the maximum double-layer network effect of the hydrogenation station network as the second goal to construct the second objective function, then, based on the first objective function and the second objective function, and considering the hydrogenation demand constraint, the supply and demand balance constraint, the hydrogenation station construction capacity constraint, the hydrogenation station floor area constraint, and the hydrogenation station number constraint, the hydrogenation station network planning optimization model can be constructed. Based on this, the hydrogenation station network planning optimization model constructed according to the present invention includes the first objective function, the second objective function, and multiple constraints (hydrogenation demand constraint, supply and demand balance constraint, hydrogenation station construction capacity constraint, hydrogenation station floor area constraint, hydrogenation station number constraint).

[0142] The hydrogenation demand constraint conditions involve hydrogenation demand prediction and hydrogenation demand allocation. The hydrogenation demand constraint conditions are specifically shown in the following equations (38)-(42).

[0143] It should be noted that the prediction of hydrogen demand can refer to the method of determining gas station demand. The commonly used methods for determining the total demand of gas stations include service radius planning method, daily fuel demand method, and engineering analogy method.

[0144] In some embodiments, the hydrogen daily demand method can be used to predict hydrogen demand. First, the different application areas of hydrogen fuel cell vehicles (FCEVs) can be defined to identify the location of hydrogen demand. Then, based on the cargo transportation volume, the number of bus routes, and the number of traditional fuel vehicles at different demand points, the allocation ratio of FCEVs to each demand point is calculated. Combined with the number of FCEVs promoted, the total number of FCEVs required for each demand point is determined. Finally, the hydrogen demand for each demand point in each application scenario can be calculated based on the estimated number of FCEVs, daily hydrogen refueling demand, and annual operating days.

[0145] Specifically, the present invention only considers the commercial field of hydrogen fuel cell vehicles, and the application scenarios of hydrogen fuel cell vehicles can be divided into the following six categories: freight trunk transportation, freight city distribution, public transportation, commuter passenger transportation, sanitation and postal services.

[0146] In terms of hydrogen fuel cell vehicle scale prediction, the expected number of hydrogen fuel cell vehicles (hydrogen fuel cell vehicle prediction) applied to each scenario at each demand point can be predicted based on the current hydrogen fuel cell vehicle promotion plan and the progress of the hydrogen energy industry. The specific calculation method of the hydrogen fuel cell vehicle prediction at each demand point is shown in formula (38). In order to ensure that the calculation results meet the actual application requirements, the obtained values ​​can be rounded up.

[0147]

[0148] In the formula, a is the application scenario of hydrogen fuel cell vehicles, where a=1 represents the freight trunk transportation scenario, a=2 represents the freight city distribution scenario, a=3 represents the public transportation scenario, a=4 represents the commuter passenger transportation scenario, a=5 represents the sanitation scenario, and a=6 represents the postal scenario. a The predicted number of hydrogen fuel cell vehicles used in scenario a in the planning area, is the annual freight volume of demand point l in the freight trunk line transportation scenario, is the annual concrete output of the concrete mixing station at demand point l in freight city distribution, is the number of bus routes at demand point l in the bus scenario, is the number of commuting routes of demand point l in the commuter passenger transport scenario, is the number of sanitation vehicles owned by demand point l in the sanitation scenario, is the number of postal transport vehicles at demand point l in the postal scenario, is the predicted quantity of hydrogen fuel cell vehicles applied to scenario a at demand point l.

[0149] Furthermore, the hydrogen demand can be predicted. Specifically, the annual hydrogen demand of each hydrogen demand point can be calculated based on the predicted amount of hydrogen fuel cell vehicles at each hydrogen demand point, the average daily hydrogen demand, and the annual operating time, as shown in formula (39).

[0150]

[0151] In the formula, H l represents the annual hydrogen demand at hydrogen demand point l (kg / year), represents the average daily hydrogen demand per vehicle for hydrogen demand point l in scenario a (kg / (day·vehicle)), is the annual operating days of hydrogen fuel cell vehicles at hydrogen demand point l in scenario a.

[0152] In terms of hydrogen refueling demand allocation, the hydrogen demand of each hydrogen demand point obtained above can be allocated to each hydrogen refueling demand point according to the Huff model to obtain the hydrogen refueling demand of each hydrogen refueling demand point. It should be noted that Huff believes that the probability of consumers going to a certain business district for consumption is proportional to the attractiveness of the business district and inversely proportional to the distance consumers travel to the business district, and proposed a model widely used in predicting the probability of consumers choosing a business district in an urban area - the Huff model.

[0153] Specifically, according to the Huff model, the probability of the hydrogen demand of the hydrogen demand point being allocated to the hydrogenation demand point can be determined, as shown in formula (40).

[0154]

[0155] The attractiveness of each hydrogenation demand point to hydrogen fuel cell vehicle users for hydrogenation services is related to the hydrogen price of the nearest hydrogenation station. The present invention assumes that each hydrogenation station is deployed by the same company and provides the same hydrogen and price. Therefore, the attractiveness of each hydrogenation demand point is the same. Based on this, the above probability model can be simplified to the following formula (41).

[0156]

[0157] Where, d ij is the distance from the hydrogen refueling demand point to the hydrogen refueling station.

[0158] Therefore, the hydrogenation demand at hydrogenation demand point i can be obtained, as shown in formula (42):

[0159]

[0160] The supply and demand balance constraints specifically include:

[0161] The hydrogen demand of the demand point for each hydrogen refueling station shall not exceed the annual hydrogen supply of the hydrogen refueling station. The candidate hydrogen refueling station points that are not selected as hydrogen refueling stations have no hydrogen refueling capacity. The details are shown in formula (43).

[0162]

[0163] In the formula, x ij S is the hydrogen supply from candidate hydrogen refueling station j to demand point i (that is, the hydrogen demand from demand point i to hydrogen refueling station j). j is the construction capacity of hydrogen refueling station j, and T is the annual operating days of the hydrogen refueling station. j is a 0-1 variable, where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. jA value of 1 indicates that the hydrogen refueling station candidate point j is selected as the hydrogen refueling station.

[0164] In addition, when the demand point is within the service range of a hydrogen refueling station, the demand point is allowed to be served by multiple hydrogen refueling stations, and the hydrogen demand of the demand point for any hydrogen refueling station (i.e., the hydrogen supply of any hydrogen refueling station to the demand point) does not exceed the hydrogen demand of the demand point itself, as shown in formula (44).

[0165]

[0166] Among them, c ij is the coverage factor of hydrogen refueling station j to demand point i, G i is the annual hydrogen demand at demand point i.

[0167] Coverage coefficient constraint: When the distance between the demand point and the hydrogen refueling station is within the service radius of the hydrogen refueling station, the coverage coefficient c ij The value is 1, otherwise the coverage coefficient c ij is 0. The specific formula is as shown in formula (45).

[0168]

[0169] In the formula, R j It is the maximum distance allowed from the demand point to the hydrogen refueling station, that is, the service radius of the hydrogen refueling station.

[0170] And, the constraint that the demand of each demand point must be fully satisfied, as shown in formula (46).

[0171]

[0172] In the formula, G i is the annual hydrogen demand at demand point i.

[0173] The construction capacity constraints of hydrogen refueling stations are as follows: the construction capacity of hydrogen refueling stations is greater than or equal to the minimum construction capacity of hydrogen refueling stations, and less than or equal to the maximum construction capacity of hydrogen refueling stations. The construction capacity of hydrogen refueling stations can be determined based on the hydrogen demand of each demand point for hydrogen refueling stations and the safety reserve capacity.

[0174] Specifically, in order to ensure that the basic demand for hydrogen fuel in the city is met while ensuring economic benefits and operational efficiency, the minimum construction capacity of the hydrogen refueling station is restricted; in order to ensure the safe and reliable operation of the hydrogen refueling station, the maximum construction capacity of the hydrogen refueling station is restricted. The construction capacity constraint of the hydrogen refueling station is shown in formula (47).

[0175]

[0176] In the formula, S j is the construction capacity of hydrogen refueling station j, S minis the minimum construction capacity of the hydrogen refueling station, S max The maximum construction capacity of a hydrogen refueling station.

[0177] In addition, the construction capacity of the hydrogen refueling station can be obtained by reserving a safe reserve capacity based on the total demand of all demand points for the hydrogen refueling station. In other words, the construction capacity of the hydrogen refueling station can be determined based on the total demand (sum) of hydrogen gas for the hydrogen refueling station from each demand point and the safe reserve capacity.

[0178] In order to improve the economy and standardization of hydrogen refueling stations, the scale design of hydrogen refueling stations must meet the following three principles:

[0179] (1) The scale of a single hydrogen refueling station should be an integer multiple of the benchmark scale of 100 kg / d.

[0180] (2) The capacity of the hydrogen refueling station must meet the needs of the demand points and reserve safety reserve capacity.

[0181] (3) Increase the hydrogen utilization rate of hydrogen refueling stations as much as possible.

[0182] From the perspective of safe operation, the construction capacity of the hydrogen refueling station should be slightly larger than the total demand of each demand point for the hydrogen refueling station. Combined with the capacity design principle, the construction capacity of the hydrogen refueling station (daily hydrogen refueling capacity) S j As shown in formula (48):

[0183]

[0184] In the formula, m j is the hydrogen demand of each demand point for hydrogen refueling station j, kg / d, η is the capacity coefficient of the hydrogen refueling station, η is (1,1.5], T is the annual operating days of the hydrogen refueling station.

[0185] m j The sum of the hydrogen demand of each demand point for hydrogen refueling station j, x ij is the hydrogen demand of demand point i for hydrogen refueling station j. As shown in formula (49):

[0186]

[0187] The floor area constraints of the hydrogen refueling station are as follows: the floor area of ​​the hydrogen refueling station is greater than or equal to the minimum floor area of ​​the hydrogen refueling station, and less than or equal to the maximum floor area of ​​the hydrogen refueling station.

[0188] It should be noted that for safety reasons, hydrogenation, gasification, refueling, charging and other process facilities in hydrogenation stations and various combined stations should not be arranged crosswise and require a safe distance. Therefore, the area of ​​a single hydrogenation station should not be too small, and the minimum floor area of ​​the hydrogenation station needs to be constrained. Since the present invention establishes an oil-hydrogen combined station, the candidate point of the hydrogenation station is a gas station, and the combined station cannot occupy the surrounding planned land, so the maximum floor area of ​​the hydrogenation station needs to be constrained. The specific constraints on the floor area of ​​the hydrogenation station are shown in formula (50):

[0189]

[0190] The hydrogen refueling station quantity constraint (ie, hydrogen refueling station network constraint) is as follows: the number of hydrogen refueling stations (ie, the total number of hydrogen refueling station candidate points selected as hydrogen refueling stations) is greater than or equal to the minimum number of hydrogen refueling stations.

[0191] It should be noted that the number of hydrogen refueling stations can reflect the scale of the hydrogen refueling station network. From the perspective of the entire hydrogen refueling station network, the minimum number of hydrogen refueling stations is constrained. As shown in formula (51):

[0192]

[0193] Where N min is the minimum number of hydrogen refueling stations, X j is a 0-1 variable, where X j 0 means that the candidate point j is not selected as a hydrogen refueling station. j A value of 1 indicates that the hydrogen refueling station candidate point j is selected as the hydrogen refueling station.

[0194] In some embodiments, in step 330, when the computing device 200 solves the hydrogen refueling station network planning optimization model, the computing device 200 uses an optimizer to solve the hydrogen refueling station network planning optimization model based on the longitude and latitude of each hydrogen refueling station candidate point and the annual hydrogen supply, and the longitude and latitude of each demand point and the annual hydrogen demand, to obtain the optimal hydrogen refueling station network planning solution.

[0195] In some specific embodiments, the present invention can utilize the flexibility and powerful data processing capabilities of the Python programming language, combined with the advanced algorithms of the Gurobi optimizer, to construct and solve the hydrogen station network planning optimization model. First, the objective function (first objective function, second objective function) and various constraints (hydrogenation demand constraints, supply and demand balance constraints, hydrogen station construction capacity constraints, hydrogen station floor space constraints, hydrogen station quantity constraints) of the hydrogen station network planning optimization model can be defined in the Python environment, and then the hydrogen station network planning optimization model can be input into the optimizer through Gurobi's Python API. Furthermore, the hydrogen refueling station network planning optimization model is solved by the optimizer. Specifically, the longitude and latitude of each hydrogen refueling station candidate point and the annual hydrogen supply, and the longitude and latitude of each demand point and the annual hydrogen demand can be input into the optimizer, so as to solve the hydrogen refueling station network planning optimization model based on the longitude and latitude of each hydrogen refueling station candidate point and the annual hydrogen supply, and the longitude and latitude of each demand point and the annual hydrogen demand, and output the optimal hydrogen refueling station network planning scheme (including the location of each hydrogen refueling station and the hydrogen supply scheme).

[0196] It should be noted that the hydrogen station network planning optimization model constructed according to the embodiment of the present invention is a dual-objective optimization model. The two objectives (the first objective: the total construction and operation cost of the hydrogen station network is minimized; the second objective: the double-layer network effect of the hydrogen station network is maximized) cannot be optimal at the same time, and each needs to have a weight. Based on this, in some embodiments of the present invention, when solving the hydrogen station network planning optimization model, the dual-objective optimization model can be first converted into a single-objective optimization model using the subjective and objective integrated weighting method. Specifically, the subjective and objective integrated weighting method can be used to determine the first comprehensive weight corresponding to the first objective function and the second comprehensive weight corresponding to the second objective function in the hydrogen station network planning optimization model. Then, the first comprehensive weight corresponding to the first objective function and the first objective function, and the second comprehensive weight corresponding to the second objective function can be used to construct a comprehensive objective function. Based on this, the hydrogen station network planning optimization model is converted from a dual-objective optimization model to a single-objective optimization model. By solving the comprehensive objective function (single-objective optimization model), the optimal hydrogen station network planning scheme can be obtained. It should be noted that the subjective and objective integrated weighting method integrates the hierarchical analysis method and the entropy weight method. The subjective weight can be solved by the hierarchical analysis method, and the objective weight can be solved by the entropy weight method. Combining the two, the subjective and objective comprehensive weight can be obtained. The specific calculation is shown in formula (52):

[0197]

[0198] In the formula, w i The comprehensive weight obtained by integrating subjective and objective weighting; v i 、ui are subjective weight and objective weight respectively; r is the decision maker's preference coefficient for subjective and objective factors, and its value is between 0 and 1.

[0199] Therefore, the comprehensive objective function can be obtained, as shown in formula (53):

[0200] minEC=w1E+w2C J (53)

[0201] Wherein, w1 is the second comprehensive weight corresponding to the second objective function, and w2 is the first comprehensive weight corresponding to the first objective function.

[0202] In addition, in some embodiments, the hydrogen refueling station network planning optimization model constructed by the present invention can be applied to the hydrogen refueling network layout in Beijing to verify the effectiveness and perform sensitivity analysis on the hydrogen refueling station network planning optimization model.

[0203] Specifically, the Beijing hydrogenation network can be selected as a case, and the hydrogenation station network planning optimization model constructed by the present invention can be applied. The specific parameter settings are shown in Table 1.

[0204] Table 1 Input parameters of hydrogen station site selection model

[0205]

[0206] Figure 4 A schematic diagram of the distribution of hydrogen demand points in Beijing is shown. Figure 5 A schematic diagram showing the distribution of candidate hydrogen refueling stations in Beijing is shown.

[0207] Among them, the demand point information comes from the construction tasks of the Beijing-Tianjin-Hebei fuel cell vehicle demonstration city cluster and the Beijing Hydrogen Fuel Cell Vehicle Industry Development Plan (2020-2025). A total of 118 demand points are set, and the distribution of hydrogen refueling demand points is as follows: Figure 4 shown.

[0208] The distribution of candidate hydrogen refueling stations in Beijing is as follows: Figure 5 As shown, a total of 206 candidate hydrogen refueling stations are set up, of which 11 candidate sites are already built hydrogen refueling stations.

[0209] In addition, in order to verify the effectiveness of the model, in some embodiments of the present invention, three schemes are set for comparative analysis. The scheme setting table is shown in Table 2.

[0210] Table 2 Scheme settings

[0211]

[0212] The comparison of the solution results of the three solutions is shown in Table 3.

[0213] Table 3 Comparison of solution results

[0214]

[0215] Figure 6 A schematic diagram of model effectiveness analysis in some embodiments of the present invention is shown.

[0216] like Figure 6 As shown in Table 3, compared with the three schemes, Scheme 1 has the lowest construction and operation cost, but requires a large number of new hydrogen stations and has the lowest double-layer network effect; Scheme 2 has the largest double-layer network effect, but has the largest construction and operation cost; Scheme 3 takes both the double-layer network effect and cost into consideration, and performs well. It can be seen that adding the double-layer network effect of the hydrogen station network to the objective function can optimize the layout strategy of the hydrogen station network and improve the effectiveness of the hydrogen station network service, thereby verifying the effectiveness of the dual-objective model established by the present invention.

[0217] For the above-mentioned Beijing hydrogen refueling network case, after programming and solving, it was finally determined to build 40 new hydrogen refueling stations, forming a double-layer hydrogen refueling station network consisting of 51 hydrogen refueling stations and 118 demand points. The construction and operation cost of the hydrogen refueling stations is 765.92 million yuan, and the double-layer network effect of the hydrogen refueling stations is 0.9718. Figure 7 A schematic diagram of the location distribution of hydrogen refueling stations in Beijing is shown, wherein blue represents candidate hydrogen refueling stations that have not been selected as hydrogen refueling stations, and red represents candidate hydrogen refueling stations that have been selected as hydrogen refueling stations.

[0218] Figure 8 The figure shows the relationship between hydrogen refueling stations providing hydrogen refueling services to demand points. Figure 8 As shown in the figure, blue represents hydrogen refueling stations, green represents demand points, and the connection line between hydrogen refueling stations and demand points indicates the existence of hydrogen refueling service relationship.

[0219] From the perspective of the hydrogen refueling station network, the hydrogen refueling solution provided by the present invention can fully cover the demand points in 6 types of application scenarios. The numbers of some hydrogen refueling stations, service demand point numbers, longitudes, latitudes and the number of service demand points are shown in Table 4. Among them, the number of demand points served by hydrogen refueling station No. B64 is the largest, covering the hydrogen demand of 6 demand points. The service demand points are numbered A68, A72, A73, A113, A115, and A116, and the types of service objects are rich.

[0220] Table 4 Service information of some hydrogen refueling stations

[0221]

[0222]

[0223] It should also be noted that, from the perspective of the hydrogen station network, the service radius of the hydrogen station reflects the service capacity of the hydrogen station, which is crucial to the construction of the hydrogen station network. The present invention also analyzes the impact of the change in the service radius of the hydrogen station on the construction and operation costs of the hydrogen station and the double-layer network effect of the hydrogen station. The service radius can be increased from 9 to 11 at intervals of 0.5 to analyze the change in the objective function.

[0224] Fig. 9 The figure shows a sensitivity analysis diagram of the hydrogen station service radius. Fig. 9 As shown in the figure, as the service radius of the hydrogen refueling station increases, the construction and operation costs of the hydrogen refueling station gradually decrease, and the double-layer network effect gradually increases. This is because as the service radius of the hydrogen refueling station increases, the service capacity of a single hydrogen refueling station increases, fewer hydrogen refueling stations can meet the same demand, and the construction and operation costs are reduced. When the service radius is too small, the service capacity of a single hydrogen refueling station is too small, which is not conducive to the formation of a hydrogen refueling station network. When the service radius of the hydrogen refueling station increases from 9.5 to 10, the number of newly built hydrogen refueling stations decreases significantly, and then slowly decreases. The double-layer network effect also increases significantly, and then slowly increases, which indicates that the service radius of the hydrogen refueling station should be greater than 9.5.

[0225] In addition, from the perspective of demand, the increase in demand will bring challenges to the service capacity of hydrogen refueling stations. In order to meet the growth in demand, the construction and operation costs of the hydrogen refueling station network and the double-layer network effect will change accordingly. The present invention also analyzes the impact of different demands on the construction and operation costs of hydrogen refueling stations and the double-layer network effect, and analyzes the changes in the objective function by reducing the existing demand by 20%, 10% and increasing it by 10%, 20% respectively.

[0226] Fig.10 Figure 2 shows a schematic diagram of the sensitivity analysis of demand. Fig.10 As shown in the figure, as the demand increases, the construction and operation costs of hydrogen refueling stations gradually increase, while the double-layer network effect gradually decreases. This is because as the demand increases, the number of new hydrogen refueling stations required increases, the equipment purchase cost increases, and the construction and operation costs increase; and the increase in demand will cause the service capacity of some hydrogen refueling stations to be unable to meet the demand, and other new hydrogen refueling stations will need to be built, thereby reducing the double-layer network effect.

[0227] In addition, the present invention also analyzes the impact of changes in hydrogen operation subsidies on construction and operation costs and double-layer network effects, and analyzes the changes in the objective function by reducing the hydrogen operation subsidies by 20%, 10% and increasing them by 10%, 20% respectively.

[0228] Fig.11 A schematic diagram of the change rate of hydrogen operation subsidies is shown. Fig.11As shown in the figure, as the hydrogen operation subsidy increases, the construction and operation costs of hydrogen refueling stations decrease, and the double-layer network effect increases. This is because, as the hydrogen operation subsidy increases, fewer hydrogen refueling stations can meet the demand, the cost of purchasing hydrogen and related equipment costs decrease, the construction and operation costs decrease, and the double-layer network effect of hydrogen refueling stations increases. When the operation subsidy decreases from 20% to unchanged, the double-layer network effect of hydrogen refueling stations increases rapidly, and then increases slowly. The hydrogen operation subsidy in the case is 10 yuan / kg, which indicates that the hydrogen operation subsidy should be no less than 10 yuan / kg.

[0229] Therefore, by applying the hydrogen refueling station network planning optimization model constructed by the present invention to the layout of the hydrogen refueling network in Beijing, a site selection plan for the hydrogen refueling station and a hydrogen supply plan for the hydrogen refueling station network are given, the effectiveness of the dual-objective model is verified by comparison, and a sensitivity analysis of the hydrogen refueling service radius, hydrogen refueling demand, and operating subsidies is conducted. This can provide an application reference for the site selection planning of hydrogen refueling stations, and further promote the large-scale application of hydrogen fuel cell vehicles and the hydrogen energy industry.

[0230] In summary, according to the hydrogen refueling station network planning optimization method 300 based on the double-layer network effect of the present invention, a double-layer network effect model is constructed based on the hydrogen refueling station network, the demand point network and the degree of matching between the hydrogen refueling station network and the demand point network, which can quantify the network effect from the system level and realize the optimization of the entire hydrogen refueling station network; secondly, a hydrogen refueling station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect, and the optimal hydrogen refueling station network planning scheme is determined by solving the hydrogen refueling station network planning optimization model, which can realize the optimized layout of the hydrogen refueling station network, improve the hydrogen energy supply efficiency of the hydrogen refueling station network, and reduce the operating cost of the hydrogen refueling station network.

[0231] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0232] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0233] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0234] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0235] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiments, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into a plurality of submodules.

[0236] Unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe common objects merely indicates that different instances of similar objects are involved and is not intended to imply that the objects so described must have a given order in time, space, order or in any other manner.

Claims

1. A hydrogen station network planning optimization method based on double-layer network effect, comprising: Based on the hydrogen refueling station network, the demand point network, and the matching degree between the hydrogen refueling station network and the demand point network, a two-layer network effect model is constructed, wherein the hydrogen refueling station network includes multiple hydrogen refueling stations, the demand point network includes multiple demand points, and the hydrogen refueling station network and the demand point network are interconnected through a supply and demand relationship; Based on the double-layer network effect model, a hydrogen station network planning optimization model is constructed with the goal of minimizing the total construction and operation cost of the hydrogen station network and maximizing the double-layer network effect; By solving the hydrogen refueling station network planning optimization model, an optimal hydrogen refueling station network planning scheme is obtained, wherein the optimal hydrogen refueling station network planning scheme includes the location of each hydrogen refueling station and a hydrogen supply scheme.

2. The method of claim 1, wherein: Based on the hydrogen refueling station network, the demand point network, and the matching degree between the hydrogen refueling station network and the demand point network, a two-layer network effect model is constructed, including: Based on a two-layer network framework including a hydrogen refueling station network and a demand point network, a two-layer network effect model is constructed by comprehensively considering the average coverage coefficient of hydrogen refueling stations, the average hydrogen refueling distance coefficient of demand points, and the matching coefficient between hydrogen refueling stations and demand points. The average coverage coefficient of the hydrogen refueling station is used to represent the hydrogen supply capacity of the hydrogen refueling station network, which is the normalized average of the number of coverage demand points of the hydrogen refueling station; The average hydrogenation distance coefficient of the demand point is used to indicate the convenience of hydrogen supply of the hydrogenation station network, and is the normalized mean value of the minimum hydrogenation distance of the demand point compared with the average hydrogenation distance; The matching coefficient between the hydrogen refueling station and the demand point is used to indicate the degree of matching between the hydrogen refueling station network and the demand point network. The matching coefficient between the hydrogen refueling station and the demand point is the average of the matching coefficient of the hydrogen refueling station and the matching coefficient of the demand point, wherein the matching coefficient of the hydrogen refueling station is the average of the normalized proportion of the capacity allocated to the demand point to the capacity of the hydrogen refueling station, and the matching coefficient of the demand point is the average of the normalized proportion of the demand at the demand point to the capacity allocated to the demand point.

3. The method of claim 2, wherein: The two-layer network effect model is as follows: Among them, E is the double-layer network effect of the hydrogen refueling station network, E1 is the average coverage coefficient of the hydrogen refueling station, E2 is the average hydrogen refueling distance coefficient of the demand point, and E3 is the matching coefficient between the hydrogen refueling station and the demand point.

4. The method according to any one of claims 1 to 3, wherein: With the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect, a hydrogen refueling station network planning optimization model is constructed, including: Taking the minimum total construction and operation cost of the hydrogen refueling station network as the first goal, the first objective function is constructed; Taking the maximum double-layer network effect of the hydrogen refueling station network as the second goal, the second objective function is constructed; Based on the first objective function and the second objective function, a hydrogen refueling station network planning optimization model is constructed.

5. The method of claim 4, wherein: By solving the hydrogen refueling station network planning optimization model, an optimal hydrogen refueling station network planning scheme is obtained, including: Determine a first comprehensive weight corresponding to a first objective function and a second comprehensive weight corresponding to a second objective function in the hydrogen station network planning optimization model by using a subjective and objective integrated weighting method; constructing a comprehensive objective function based on the first objective function and a first comprehensive weight corresponding to the first objective function, and the second objective function and a second comprehensive weight corresponding to the second objective function; By solving the comprehensive objective function, an optimal hydrogen refueling station network planning scheme is obtained.

6. The method according to any one of claims 1 to 5, wherein: By solving the hydrogen refueling station network planning optimization model, an optimal hydrogen refueling station network planning scheme is obtained, including: The optimizer solves the hydrogen station network planning optimization model based on the longitude and latitude of each hydrogen station candidate point and the annual hydrogen supply, and the longitude and latitude of each demand point and the annual hydrogen demand, to obtain the optimal hydrogen station network planning solution.

7. The method according to any one of claims 1 to 6, wherein: The total construction and operation cost of the hydrogen refueling station network includes the construction investment cost, operation cost and hydrogen storage cost of each hydrogen refueling station in the hydrogen refueling station network; Among them, the construction investment cost of the hydrogen refueling station includes the investment cost of various equipment required for the construction of the hydrogen refueling station and the cost of the land for the construction of the hydrogen refueling station; the operating cost of the hydrogen refueling station includes the maintenance cost and depreciation cost of the equipment in the station during the operation of the hydrogen refueling station; the hydrogen storage cost of the hydrogen refueling station is related to the unit storage cost of hydrogen and the construction capacity of the hydrogen refueling station.

8. The method according to any one of claims 1 to 7, wherein: With the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect, a hydrogen refueling station network planning optimization model is constructed, including: With the goal of minimizing the total construction and operation cost of the hydrogen refueling station network and maximizing the double-layer network effect, and taking into account the constraints of hydrogen refueling demand, supply and demand balance, hydrogen refueling station construction capacity, hydrogen refueling station floor space, and hydrogen refueling station quantity, a hydrogen refueling station network planning optimization model is constructed.

9. The method of claim 8, wherein: The hydrogenation demand constraint condition includes: calculating the annual hydrogen demand of each hydrogen demand point according to the predicted amount of hydrogen fuel cell vehicles, the average daily hydrogen demand and the annual operation time of each hydrogen demand point; allocating the hydrogen demand of each hydrogen demand point to each hydrogenation demand point according to the Huff model to obtain the hydrogenation demand of each hydrogenation demand point; The supply and demand balance constraint conditions include: the hydrogen demand of the demand point for each hydrogen refueling station does not exceed the annual hydrogen supply of the hydrogen refueling station; when the demand point is within the service range of the hydrogen refueling station, the demand point is allowed to be served by multiple hydrogen refueling stations, and the hydrogen supply of any hydrogen refueling station to the demand point does not exceed the hydrogen demand of the demand point itself; The construction capacity constraint conditions of the hydrogen refueling station include: the construction capacity of the hydrogen refueling station is greater than or equal to the minimum construction capacity of the hydrogen refueling station, and less than or equal to the maximum construction capacity of the hydrogen refueling station, wherein the construction capacity of the hydrogen refueling station is determined according to the hydrogen demand of each demand point for the hydrogen refueling station and the safety reserve capacity; The floor area constraint condition of the hydrogen refueling station is: the floor area of ​​the hydrogen refueling station is greater than or equal to the minimum floor area of ​​the hydrogen refueling station, and less than or equal to the maximum floor area of ​​the hydrogen refueling station. The constraint condition on the number of hydrogen refueling stations is: the number of hydrogen refueling stations is greater than or equal to the minimum number of hydrogen refueling stations.

10. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, and the program instructions include instructions for processing the method according to any one of claims 1 to 9.

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