Hydrogenation network node site selection method based on hydrogen source diversity and service platform

Abstract

The invention relates to a hydrogenation network node site selection method based on hydrogen source diversity and a service platform, and the method comprises the steps: determining the planning number of to-be-selected oil-hydrogen stations in a target region and hydrogen energy vehicle traffic flow parameters on a plurality of target driving paths, and according to the planning number and the hydrogen energy vehicle traffic flow parameters, determining the to-be-selected oil-hydrogen stations in the target region; generating corresponding first site selection decision information; after hydrogenation decision information currently corresponding to each target driving path is received, the first site selection decision information is updated by using a preset genetic algorithm and the hydrogenation decision information, corresponding second site selection decision information is generated, and the hydrogenation decision information is solved by using a solver according to the first target function and the first site selection decision information; and according to the fitness of the second site selection decision information calculated based on the second target function, the genetic algorithm and the corresponding hydrogenation decision information are utilized to iterate the second site selection decision information until target site selection decision information is obtained, and a site selection result is determined according to the target site selection decision information.

Description

Technical Field

[0001] The present application relates to the technical field of hydrogen energy transportation site selection, and in particular to a hydrogenation network node site selection method and service platform based on hydrogen source diversity. Background Art

[0002] Hydrogen has the advantages of zero emissions, wide sources, and multiple applications. It is the best choice for large-scale deep decarbonization in the transportation sector. The development and utilization of hydrogen has become one of the main directions of energy transformation. Hydrogen is a renewable resource. It can be produced from a variety of primary energy sources in a variety of ways, but only renewable energy electrolysis of water to produce hydrogen can achieve true zero carbon emissions. Therefore, renewable energy power stations with hydrogen production capabilities, such as wind farms and photovoltaic power stations, can be regarded as hydrogen production points.

[0003] In the related art, the hydrogen for hydrogen refueling stations is provided by hydrogen production points, but the production capacity of a single hydrogen production point may not be able to meet the hydrogen demand of the hydrogen refueling station, so multiple hydrogen production points are required to jointly supply hydrogen to the hydrogen refueling station; in the related art, the layout of the site selection of hydrogen refueling network nodes is a long-term planning process, which needs to meet the different future needs of heterogeneous customers, and needs to consider the carbon emission cost of hydrogen production points, hydrogen production cost and hydrogen supply cost, but the existing related art for the site selection of hydrogen refueling network nodes assumes that the demand for hydrogen is static, and does not consider the diversity of hydrogen sources (corresponding to hydrogen production capacity) and the carbon emission cost of multiple hydrogen sources. At the same time, the site selection of hydrogen refueling points does not take into account the mileage anxiety values ​​of different customers (hydrogen vehicles) and their driving satisfaction with the route, making the site selection and planning of the hydrogen refueling network unable to meet future hydrogen refueling needs, and the site selection and construction costs of hydrogen refueling network nodes are high.

[0004] At present, regarding the site selection scheme for hydrogen refueling network nodes in related technologies, there are problems such as the site selection and planning of the hydrogen refueling network cannot meet future hydrogen refueling needs, and the site selection and construction costs of the hydrogen refueling network nodes are high. No effective solution has yet been proposed. Summary of the invention

[0005] The embodiments of the present application provide a method and service platform for hydrogen refueling network node site selection based on hydrogen source diversity, as well as a storage medium, to at least solve the problem that the hydrogen refueling network site selection and planning in the related art cannot meet future hydrogen refueling needs and the hydrogen refueling network node site selection and construction costs are high.

[0006] In the first aspect, an embodiment of the present application provides a method for site selection of hydrogen refueling network nodes based on hydrogen source diversity, including: determining the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen-powered vehicle traffic flow parameters on multiple target driving paths, and performing an initial site selection decision based on the planned number and the hydrogen-powered vehicle traffic flow parameters to generate first site selection decision information corresponding to the multiple target driving paths; after receiving the hydrogen refueling decision information currently corresponding to each of the target driving paths, using a preset genetic algorithm and the hydrogen refueling decision information to update the first site selection decision information to generate second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes a second oil and hydrogen station sited on each of the target driving paths, a second hydrogen production station that supplies hydrogen to all the second oil and hydrogen stations, and a second hydrogen supply distribution information, and the hydrogen refueling decision information is based on the first objective function and the first site selection decision information, using the target The solver solves and generates, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to refuel at the first oil and hydrogen station corresponding to the first site selection decision information. The first objective function is used to characterize the driving satisfaction corresponding to all the target driving paths; according to the constructed second objective function, the fitness of the second site selection decision information is calculated, and according to the fitness, the genetic algorithm and the corresponding hydrogenation decision information are used to update and iterate the second site selection decision information made at the time until the second site selection decision information is generated as the target site selection decision information, and the site selection result is determined according to the target site selection decision information, wherein the second objective function is constructed according to the hydrogenation revenue parameter generated by the second oil and hydrogen station, the cost parameter generated by the second oil and hydrogen station and the second hydrogen production station, and the transportation cost parameter corresponding to the second gas supply distribution information, and the hydrogenation revenue parameter is associated with the corresponding first objective function.

[0007] In a second aspect, an embodiment of the present application provides a service platform, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for selecting a hydrogenation network node based on hydrogen source diversity in the first aspect are implemented.

[0008] In a third aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for selecting a hydrogenation network node site based on hydrogen source diversity as described in the first aspect above.

[0009] Compared with the related art, the hydrogenation network node site selection method and service platform based on hydrogen source diversity provided in the embodiment of the present application adopts the method of determining the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen-powered vehicle traffic flow parameters on multiple target driving paths, and performs an initial site selection decision according to the planned number and the hydrogen-powered vehicle traffic flow parameters, and generates first site selection decision information corresponding to the multiple target driving paths; after receiving the hydrogenation decision information currently corresponding to each of the target driving paths, the first site selection decision information is updated using a preset genetic algorithm and the hydrogenation decision information to generate the second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes the second oil and hydrogen station sited on each of the target driving paths, the second hydrogen production station that supplies hydrogen to all the second oil and hydrogen stations, and the second hydrogen supply distribution information; the hydrogenation decision information is generated by solving the first objective function and the first site selection decision information using a target solver, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to perform hydrogenation at the first oil and hydrogen station corresponding to the first site selection decision information; the first objective function is used to characterize the driving satisfaction corresponding to all the target driving paths; according to the constructed second objective function, the fitness of the second site selection decision information is calculated, and according to the fitness, the genetic algorithm and the corresponding hydrogenation decision information are used to update and iterate the second site selection decision information made at the time, until the second site selection decision information as the target site selection decision information is generated, and the site selection result is determined according to the target site selection decision information, wherein the second objective function is constructed according to the hydrogenation revenue parameter generated by the second oil and hydrogen station, the cost parameter generated by the second oil and hydrogen station and the second hydrogen production station, and the transportation cost parameter corresponding to the second gas supply allocation information. The hydrogenation revenue parameter is associated with the corresponding first objective function, which solves the problem that the hydrogenation network planned by the site selection scheme of the hydrogenation network node selection in the related technology cannot meet the future hydrogenation demand, and the site selection and construction cost of the hydrogenation network node is high. Through the interactive optimization of upper and lower-level decisions, the balance between the site selection of hydrogenation stations and the hydrogenation behavior of hydrogen-powered vehicles is achieved. The site-planned hydrogenation network can achieve effective carbon emission reduction, the hydrogenation network has good resilience, and the driving user has low mileage anxiety.

[0010] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1It is a hardware structure block diagram of a terminal of a method for selecting a hydrogenation network node based on hydrogen source diversity according to an embodiment of the present application; Figure 2 is a flow chart of a method for selecting a hydrogenation network node site based on hydrogen source diversity according to an embodiment of the present application; Figure 3 It is a structural block diagram of a hydrogenation network node site selection device based on hydrogen source diversity according to an embodiment of the present application. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the contents disclosed in the present application, some changes such as design, manufacturing or production based on the technical contents disclosed in the present application are only conventional technical means, and should not be understood as insufficient contents disclosed in the present application.

[0013] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0014] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation and may represent the singular or plural. The terms "including", "comprising", "having" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include unlisted steps or units, or may also include other steps or units inherent to these processes, methods, products or devices. The "multiple links" involved in this application refer to links greater than or equal to two. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" may represent: A exists alone, A and B exist at the same time, and B exists alone. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects and do not represent a specific ordering of objects.

[0015] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. Taking running on a terminal as an example, Figure 1 1 is a hardware structure block diagram of a terminal of a method for selecting a hydrogenation network node based on hydrogen source diversity according to an embodiment of the present application. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0016] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for selecting a node in a hydrogenation network based on hydrogen source diversity in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0017] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0018] This embodiment provides a method for selecting a hydrogenation network node site based on hydrogen source diversity, which is run on the above-mentioned terminal. Figure 2 is a flow chart of a method for selecting a hydrogenation network node site based on hydrogen source diversity according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0019] Step S201, determine the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen vehicle traffic flow parameters on multiple target driving routes, perform an initial site selection decision based on the planned number and the hydrogen vehicle traffic flow parameters, and generate first site selection decision information corresponding to the multiple target driving routes.

[0020] In this embodiment, the site selection method implemented is to select alternative oil and hydrogen sites (the target information of the corresponding oil and hydrogen sites, such as location information, construction area, and hydrogen refueling capacity, is known or can be obtained) within the target area (for example, a province or municipality) to construct the corresponding oil and hydrogen site. At the same time, hydrogen production sites that meet the hydrogen refueling needs of all selected oil and hydrogen sites are planned, and hydrogen supply distribution relationships are allocated (which oil and hydrogen sites the hydrogen production sites supply hydrogen to and how much hydrogen is supplied); in this embodiment, the total number of oil and hydrogen sites selected and planned in the target area is determined based on the predicted total amount of future hydrogenation (hydrogen consumption, hydrogen use) demand in the target area and the hydrogen refueling capacity of the corresponding oil and hydrogen sites after construction, and the number of hydrogen production sites is determined based on the total hydrogenation amount after construction of all oil and hydrogen sites and the unit hydrogen production capacity of a single hydrogen production site; in this embodiment, the number of vehicles using hydrogen energy in the target area is determined based on the vehicle range and structure. The structure is divided into different types according to the department structure (for example, the passenger transport department and the freight transport department are divided into public transport and private transport according to the sub-departments, and for example, the sub-departments are divided into various types of transportation, such as taxis, small and micro cars, cars, and urban SUVs), and based on this, target driving paths corresponding to different hydrogen-powered vehicles are formed, that is, the target driving paths are formed by the driving of hydrogen-powered vehicles; in this embodiment, the result of the site selection planning is at least to match the corresponding oil and hydrogen stations on different driving paths, so it is necessary to determine the hydrogen refueling demand corresponding to each target driving path. In this embodiment, the total hydrogen refueling demand corresponding to each target driving path is determined by determining the hydrogen-powered vehicle traffic flow parameters corresponding to each target driving path. At the same time, the hydrogen-powered vehicle traffic flow parameters are determined according to the set proportions of all traffic flow parameters corresponding to each target driving path in the target area.

[0021] In this embodiment, after determining the hydrogen-powered vehicle traffic flow parameters corresponding to each target driving route, the number of oil and hydrogen stations that need to be sited for each target driving route can be determined, and then an initial site selection decision is made based on the determined number to generate the first site selection decision information corresponding to each target driving route.

[0022] Step S202, after receiving the hydrogenation decision information currently corresponding to each target driving path, the first site selection decision information is updated using the preset genetic algorithm and the hydrogenation decision information to generate the second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes the second oil and hydrogen site selected on each target driving path, the second hydrogen production site that supplies hydrogen to all second oil and hydrogen sites, and the second hydrogen supply distribution information. The hydrogenation decision information is generated by solving the first objective function and the first site selection decision information using the objective solver, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to refuel at the first oil and hydrogen site corresponding to the first site selection decision information. The first objective function is used to characterize the driving satisfaction corresponding to all target driving paths.

[0023] In this embodiment, the target objects participating in the site selection of hydrogen refueling network nodes include the upper-level subject (corresponding to the hydrogen refueling facility operator) and the lower-level subject (corresponding to the hydrogen vehicle user and heterogeneous customer). After completing the initialization decision, the upper-level subject will send the first site selection decision information (including the oil and hydrogen station layout corresponding to the target driving path) to the lower-level subject. The lower-level subject will make a hydrogenation decision based on the received oil and hydrogen station layout to maximize its satisfaction, that is, make a decision to generate hydrogenation decision information. Then, the lower layer will feed back the made hydrogenation decision to the upper-level subject, so that the upper-level subject can perform decision planning iteration based on the genetic algorithm based on the corresponding feedback (corresponding to the hydrogenation decision information currently corresponding to each target driving path) and the generated first site selection decision information, thereby generating new site selection decision information, that is, generating the second site selection decision information corresponding to the current decision.

[0024] In this embodiment, the lower-level subject makes a hydrogenation decision by using a target solver to construct a corresponding model of the lower level. Based on the oil and hydrogen station layout completed by the upper-level subject, it solves how to select the oil and hydrogen station for hydrogenation and the amount of hydrogenation at the corresponding oil and hydrogen station, and outputs the corresponding hydrogenation decision, the remaining amount of hydrogen, and the driving satisfaction corresponding to the target driving path on each target driving path. In this embodiment, when the lower-level subject makes a hydrogenation decision, the first objective function Tsa set is: T sa = Among them, T sa represents the weighted sum of driving satisfaction corresponding to all target driving paths; Q represents the set of the shortest round-trip target driving paths q with the starting and ending points in the target area, and N represents the set of oil and hydrogen stations i; N q represents the set of oil and hydrogen stations i on the target driving path q; H is the set of hydrogen production stations h; T is the set of types t of hydrogen-powered vehicles; wq,t represents the traffic flow parameter of the tth type of hydrogen-powered vehicle on the target driving path q; is the driving satisfaction of the target driving path q; is the satisfaction function, which represents the satisfaction of the t-th type of hydrogen-powered vehicle refueling at the i-th oil-hydrogen station on the target driving route q; is the remaining amount of hydrogen when the t-th type of hydrogen-powered vehicle reaches the i-th oil-hydrogen station on the target driving path q, is the critical value of the range anxiety of the tth type of hydrogen-powered vehicle, Represents the maximum hydrogen storage capacity of the tth type of hydrogen-powered vehicle.

[0025] The constraints of the first objective function include: (1) Constraints on oil and hydrogen site selection: in, The constraints on the choice of oil-hydrogen stations for hydrogen vehicles, Y iq,t =1, indicating that in the target driving path q, the t-th type of hydrogen-powered vehicle chooses the i-th oil-hydrogen station to stop for hydrogenation, otherwise it is 0; L is a maximum constant; X i is the decision variable, X i =1, indicating that the i-th oil-hydrogen station is selected from the alternative site to the oil-hydrogen station on the corresponding driving path, otherwise, Xi=0.

[0026] (2) Initial hydrogen capacity constraints for hydrogen vehicles: in, represents the initial hydrogen capacity constraint of hydrogen vehicles, B oq represents the hydrogen content of the hydrogen vehicle at the starting point of the target driving path q, X oq Indicates whether the starting point o of the target driving path q has a gas station. If yes, X oq =1, otherwise 0; Represents the maximum hydrogen storage capacity of the tth type of hydrogen-powered vehicle.

[0027] (3) The remaining hydrogen quantity equation of each oil-hydrogen station on the target driving route q is: Among them, i and j represent two adjacent oil and hydrogen sites. is the remaining amount of hydrogen at the jth oil-hydrogen station on the target driving path q for the tth type of hydrogen-powered vehicle; is the remaining amount of hydrogen at the i-th oil-hydrogen station on the target driving path q for the t-th type of hydrogen-powered vehicle; d ij is the distance between adjacent oil and hydrogen sites i and j; is the conversion factor, which represents the driving distance per unit amount of hydrogen of the tth type of hydrogen-powered vehicle.

[0028] (4) Hydrogenation amount constraints: in, is the hydrogen refueling amount constraint, representing the hydrogen refueling amount of the t-th type of hydrogen-powered vehicle at each oil-hydrogen station on the target driving route, is the amount of hydrogen refueling of the t-th type of hydrogen-powered vehicle at the i-th oil-hydrogen station on the target driving path q.

[0029] (5) Intermediate variable constraints: in, Indicates whether the current amount of hydrogen can reach the next oil and hydrogen station. If not, the default target driving route q is R after the i-th oil and hydrogen station. iq,t =0,B iq,t =0; f is an intermediate variable, f=1 means the remaining amount of hydrogen at the i-th oil-hydrogen station on the target driving path q for the t-th type of hydrogen-powered vehicle is , continue driving long enough to reach the jth oil and hydrogen station, otherwise, f=0.

[0030] (6) Decision-making quantity constraints: Among them, Y iq,t =1, indicating that in the target driving path q, the tth type of hydrogen-powered vehicle chooses the i-th oil-hydrogen station to stop for hydrogen refueling, otherwise it is 0; represents the maximum hydrogen storage capacity of the tth type of hydrogen-powered vehicle; represents the amount of hydrogen added per unit vehicle of the t-th type of hydrogen-powered vehicle on the target driving route q at the oil-hydrogen station i; is the remaining amount of hydrogen when the t-th type of hydrogen-powered vehicle reaches the i-th oil-hydrogen station on the target driving path q; N is the set of oil-hydrogen stations i; Q represents the set of the shortest round-trip target driving paths q with starting and ending points in the target area; T is the set of types t of hydrogen-powered vehicles.

[0031] In this embodiment, by sa Perform equivalent substitution according to the following formula: T sa = in, It represents the minimum satisfaction of the t-th type of hydrogen-powered vehicle for the target driving path q.

[0032] In this embodiment, the satisfaction function is replaced by the following formula: Among them, μ1 and μ2 represent random quantities.

[0033] In this embodiment, the target solver includes but is not limited to the Gurobi solver; at the same time, it can be understood that the decision planning iteration based on the genetic algorithm is clear to those skilled in the art and does not constitute an unclear limitation of the present application.

[0034] Step S203, according to the constructed second objective function, calculate the fitness of the second site selection decision information, and according to the fitness, use the genetic algorithm and the corresponding hydrogenation decision information to update and iterate the second site selection decision information made at the time, until the second site selection decision information is generated as the target site selection decision information, and determine the site selection result according to the target site selection decision information, wherein the second objective function is constructed according to the hydrogenation revenue parameters generated by the second oil and hydrogen site, the cost parameters generated by the second oil and hydrogen site and the second hydrogen production site, and the transportation cost parameters corresponding to the second gas supply distribution information, and the hydrogenation revenue parameters are associated with the corresponding first objective function.

[0035] In this embodiment, after the upper-level subject performs a new decision-making planning iteration, it is necessary to judge the fitness corresponding to the location decision information generated by the decision-making planning iteration to evaluate whether the corresponding location layout is appropriate. In this embodiment, when calculating the fitness of the corresponding location decision information, it is calculated based on the second objective function constructed by considering the layout of the hydrogenation network nodes (oil and hydrogen site layout, hydrogen production site layout) on the target driving path of the lower-level subject and the driving satisfaction. In this embodiment, the second objective function R set for calculating the fitness of the corresponding second location decision information max for: R max =R revenue -C1-C2-C3 Among them, Rmax is the maximum benefit function (corresponding to the fitness function), R revenueis the total revenue corresponding to the hydrogen refueling operation; C1 is the transportation cost of hydrogen from the hydrogen production site to the oil and hydrogen site; C2 is the carbon emission cost generated by hydrogen production; C3 is the land use cost of the oil and hydrogen site; it can be understood that when calculating the fitness of the site selection decision information, the second objective function reflects the benefits and costs of the facility operator, including: calculating the revenue based on the traffic flow on each target driving path and the distribution of oil and hydrogen sites, calculating the transportation cost based on the distance between the hydrogen production site and the oil and hydrogen site, considering the carbon emission intensity of the hydrogen production site, and calculating the carbon emission cost; considering the land use cost of the oil and hydrogen site, and constructing the constraints as penalty items: including the hydrogen supply capacity limit, budget limit, and site quantity limit of the hydrogen production site.

[0036] Among them, the total income R revenue for: Where Q represents the set of the shortest round-trip target driving paths q with the starting and ending points in the target area, N is the set of oil and hydrogen stations i; H is the set of hydrogen production stations h; T is the set of hydrogen vehicle types t, H price is the unit sales price of hydrogen; w q,t represents the traffic flow parameter of the t-th type of hydrogen-powered vehicle on the target driving path q; d q is the total distance of the target driving path q; is the driving distance per unit amount of hydrogen of the tth type of hydrogen-powered vehicle; is the driving satisfaction of the target driving path q.

[0037] The transportation cost C1 is: Among them, C fix Refers to the unit activation cost of the tube bundle vehicle for transporting hydrogen; C trans is the unit transportation cost of the bundle truck; d hi is the distance from hydrogen production site h to oil hydrogen site i; S hi is the decision variable, S hi =1, indicating that hydrogen production site h provides hydrogen to oil hydrogen site i, otherwise, S hi =0.

[0038] The carbon emission cost C2 is: Among them, C coal is the carbon trading price in the carbon trading market; K h The amount of carbon dioxide produced for each unit of hydrogen produced at the hth oil-hydrogen station; represents the amount of hydrogen added per unit vehicle of the tth type of hydrogen-powered vehicle on the target driving route q at the oil-hydrogen station i.

[0039] The land use cost C3 of the oil-hydrogen site is: in, is the price per unit area for the i-th oil and hydrogen station; is the area of ​​the i-th oil and hydrogen site; X i is the decision variable, X i =1, indicating that the i-th oil-hydrogen station is selected from the candidate site to the oil-hydrogen station on the corresponding driving path, otherwise, X i =0.

[0040] The constraints corresponding to the second objective function include: (1) Hydrogen supply capacity constraints: in, represents the hydrogen supply capacity constraint to ensure that the demand of the oil hydrogen site is less than the supply capacity of the hydrogen production site; w q,t represents the traffic flow parameter of the t-th type of hydrogen-powered vehicle on the target driving path q; S represents the amount of hydrogen added per unit vehicle at the oil-hydrogen station i for the t-th type of hydrogen-powered vehicle on the target driving route q; hi is the decision variable, S hi =1, indicating that hydrogen production site h provides hydrogen to oil hydrogen site i, otherwise, S hi =0;D h represents the supply capacity of the hth hydrogen production site.

[0041] (2) Investment constraints: Among them, CB i represents the initial investment cost of the i-th oil-hydrogen station; F i represents the annual operating cost of the i-th oil-hydrogen site; r represents the discount rate, which is the cost of using funds; the present value of the initial investment cost of the oil-hydrogen site (including equipment, buildings, etc.) plus the annual operating cost (including hydrogen production, transportation, maintenance, and labor) must be less than or equal to the investment funds.

[0042] (3) Constraints on the number of oil and hydrogen stations: in, represents the number constraint of oil and hydrogen stations, N station Indicates the planned number of oil and hydrogen sites to be selected.

[0043] (4) Constraints on the number of hydrogen production sites: in, is the number constraint of hydrogen production sites, N producer Indicates the number of sites to be selected for hydrogen production.

[0044] (5) Constraints on hydrogen supply allocation:

[0045] in, Limiting an oil-hydrogen station to only be supplied with hydrogen by one hydrogen production station; S hi It is stipulated that hydrogen must be transported from oil and hydrogen stations.

[0046] In this embodiment, after completing the fitness calculation of the second site selection decision information, it will be determined whether the fitness of the second site selection decision information corresponding to each target driving path meets the preset requirements (not less than the preset fitness threshold), so as to determine whether it is necessary to continue to iterate the decision on the second site selection decision information generated after each decision, until the maximum number of iterations is reached or the fitness converges, and then obtain the target site selection decision information corresponding to each target driving path. Because the site selection decision information corresponding to each target driving path includes at least one hydrogenation network node site selection and hydrogen supply distribution plan, the one with the best fitness is selected from multiple plans as the final site selection result.

[0047] Through the above steps S201 to S203, the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen vehicle traffic flow parameters on multiple target driving paths are determined, and the site selection decision is initialized according to the planned number and the hydrogen vehicle traffic flow parameters to generate first site selection decision information corresponding to the multiple target driving paths; after receiving the hydrogenation decision information currently corresponding to each target driving path, the first site selection decision information is updated using the preset genetic algorithm and the hydrogenation decision information to generate second site selection decision information corresponding to the current decision; the fitness of the second site selection decision information is calculated according to the constructed second objective function, and the fitness of the second site selection decision information is calculated according to the fitness function. The genetic algorithm and the corresponding hydrogenation decision information are used to update and iterate the second site selection decision information made at the time until the second site selection decision information is generated as the target site selection decision information, and the site selection result is determined according to the target site selection decision information. This solves the problems in the related technology that the hydrogenation network site selection scheme for hydrogenation network node selection cannot meet the future hydrogenation demand, and the site selection and construction costs of hydrogenation network nodes are high. Through the interactive optimization of upper and lower-level decisions, a balance is achieved between the site selection of hydrogenation stations and the hydrogenation behavior of hydrogen-powered vehicles. The site selection and planned hydrogenation network can achieve effective carbon emission reduction, the hydrogenation network has good resilience, and the driving users have low mileage anxiety.

[0048] It should be noted that the hydrogen refueling network node site selection method of this embodiment takes into account the optimization of the hydrogen refueling network layout by taking into account heterogeneous customer needs (different types of hydrogen-powered vehicles) and hydrogen source diversity (diverse alternative hydrogen source options, diverse hydrogen production methods at hydrogen production sites, and strong hydrogen supply capacity); by dividing hydrogen-powered vehicle customers on the transportation network into public transportation, private transportation, and freight transportation, on the one hand, there are differences in the mileage anxiety thresholds of the three types of customers, and the oil and hydrogen site planning needs to minimize the anxiety of heterogeneous customers, and the traffic flow on the path is allocated according to the needs of heterogeneous customers, thereby rationally planning the oil and hydrogen sites; on the other hand, through bottom-up The hydrogenation demand prediction method can obtain the total hydrogenation demand prediction value and determine the planned number of oil-hydrogen stations; it can be understood that the upper and lower entities involved in the hydrogenation network node site selection method of this application include two types of decision-making entities, namely, pioneers and followers. Among them, the upper layer is the pioneer, that is, the operator of the hydrogenation infrastructure, specifically including oil-hydrogen combined stations and hydrogen production plants. The operator's goal is to maximize revenue and complete the site selection of oil-hydrogen stations and reasonable decisions on hydrogen supply under the constraints of hydrogen supply capacity, infrastructure quantity and site selection funds; the lower layer is the follower, that is, heterogeneous customers, and their goal is to maximize user satisfaction. On the premise that the layout of the hydrogenation infrastructure is completed, customers will choose the stop point and the number of hydrogenation based on the distribution of hydrogenation stations; the site selection decision of the upper operator will directly affect the hydrogenation decision of the lower customer, which is specifically manifested in which hydrogenation station the customer chooses to refuel at and the customer's satisfaction on the path. In turn, the hydrogenation behavior of the lower customer will also affect the revenue of the upper operator and the hydrogen transportation cost between the plant and the station, thereby further affecting the upper hydrogenation network layout. Therefore, the choices of the two types of decision-making entities interact with each other and eventually reach an equilibrium state.

[0049] It should be further explained that the hydrogenation network node site selection method of the embodiment of the present application has clearly set the scenario and the corresponding demand before implementation, that is, the decision structure made by the hydrogenation network node site selection method meets the predicted scenario. In this embodiment, the set scenarios include the baseline scenario (also called the reference scenario, the reference scenario), the technology development scenario and the demand side management scenario, wherein the technology development scenario refers to the impact of future technological progress on the energy system, simulating the changes in the energy system caused by technological innovation, R&D breakthroughs or large-scale application of new technologies. The purpose is to evaluate the impact of technological progress at different time nodes, such as fuel cell technology, energy storage technology, hydrogen production technology, etc. Common technology development scenarios include: electric vehicles and hydrogen fuel cell technology have been greatly improved, resulting in a significant reduction in energy consumption intensity and cost, for example: improving fuel economy scenario, the fuel economy of hydrogen energy vehicles has gradually improved, setting low, medium and high intensity scenarios, and predicting the energy consumption reduction under different policy intensities in the future; the demand side management scenario assumes that energy demand is reduced by changing consumer behavior, policy guidance or technical means, with the focus on reducing energy consumption or optimizing energy use.

[0050] In some embodiments, determining the hydrogen vehicle traffic flow parameters on multiple target driving paths is achieved by the following steps:

[0051] Step 21, extracting the total traffic flow corresponding to each target driving route from the traffic flow data generated by the preset map.

[0052] Step 22, in the total traffic flow corresponding to each target driving route, select a set percentage of traffic flow to obtain hydrogen-powered vehicle traffic flow parameters corresponding to each target driving route, wherein the set percentage is used to characterize the proportion of hydrogen-powered vehicles in the existing vehicle flow.

[0053] In this embodiment, the estimation of the hydrogen vehicle traffic flow parameters is performed by setting the path flow distribution of the hydrogen vehicle to be consistent with the existing traffic flow obtained through the preset map, and multiplying the existing traffic flow by a proportional coefficient. ,Traffic flow parameters for replacing hydrogen vehicles, , where Q HFCV,q represents the traffic flow of hydrogen vehicles on the target driving path q (unit: vehicle / day), Q current,q represents the existing vehicle traffic flow on the target driving path q (unit: vehicle / day), is the proportionality coefficient, which indicates the proportion of hydrogen vehicles in the existing vehicle flow. It can be understood that according to the existing traffic flow data Q current,q Multiply by a scaling factor , the traffic flow of hydrogen vehicles is estimated, and the coefficient It reflects the proportion of hydrogen-powered vehicles in the total traffic flow in the future.

[0054] In some of the optional methods, the traffic flow of hydrogen vehicles is allocated to private transportation, public transportation and freight transportation. The corresponding hydrogen vehicle traffic flow parameters are estimated as follows: Assuming that the total traffic flow of hydrogen vehicles has been obtained, the traffic flow is allocated according to the predicted values ​​of private transportation, public transportation and freight transportation. The allocated weight is based on the estimated proportion of each type of transportation in the total hydrogen vehicles. The corresponding formula is expressed as follows: , , ,in, represents the traffic flow parameter of hydrogen vehicles allocated to private transportation on the target driving path q, represents the traffic flow parameter of hydrogen energy vehicles allocated to public transportation on the target driving path q, represents the traffic flow parameter of hydrogen energy vehicles allocated to freight traffic on the target driving path q; , , They represent the weights of private transportation, public transportation and freight transportation respectively; the corresponding weights are calculated using the following formula: Assuming that the predicted number of hydrogen-powered vehicles for private transportation, public transportation and freight transportation is N private 、N public and N freight , then the weight of each type of transportation can be expressed as: , , , where N private represents the predicted number of hydrogen vehicles for private transportation, N public represents the predicted number of hydrogen vehicles for public transportation, N freight The predicted number of hydrogen-powered vehicles in freight traffic; the proportion of private traffic, public transportation, and freight traffic in hydrogen-powered vehicles is calculated through the predicted values, and the proportion (i.e., weight) is used to allocate the traffic flow parameters of hydrogen-powered vehicles. The total hydrogen-powered vehicle flow Q on each target driving route HFCV,q When allocating to the three types of transportation, weights are allocated based on the estimated proportion of each type of transportation, and the hydrogen vehicle flow parameters for each type of transportation are calculated.

[0055] The total traffic flow corresponding to each target driving route is extracted from the traffic flow data generated from the preset map in the above steps; in the total traffic flow corresponding to each target driving route, a set percentage of traffic flow is selected to obtain the hydrogen vehicle traffic flow parameters corresponding to each target driving route. The set percentage is used to characterize the proportion of hydrogen vehicles in the existing vehicle flow, so as to determine the traffic flow on each target driving route, provide a decision-making basis for the layout of the oil and hydrogen stations on each target driving route, and improve the accuracy and feasibility of the site selection decision.

[0056] In some embodiments, determining the planned number of oil and hydrogen sites to be sited in the target area is achieved by the following steps:

[0057] Step 31, using the Gompertz model, predicting vehicle ownership parameters corresponding to various target categories of hydrogen-powered vehicles in the target area within a preset planning period.

[0058] In this embodiment, hydrogen energy vehicles are divided into three types (public transportation, private transportation, and freight transportation) and nine types of vehicles (type 1 is taxis, small and micro buses, and large and medium-sized buses; type 2 is sedans and SUVs; type 3 is light trucks, medium trucks, and heavy trucks) according to vehicle usage and vehicle characteristics. The Gompertz model is used to predict the future number of hydrogen energy vehicles per thousand people of the three major types of hydrogen energy vehicles. In this embodiment, when predicting the number of vehicles, the corresponding number of hydrogen energy vehicles per thousand people is predicted based on statistical economic data (for example, annual per capita GDP forecast data) and estimated parameters.

[0059] Step 32, based on the vehicle ownership parameters corresponding to each target category of hydrogen-powered vehicles, determine the corresponding total vehicle ownership parameters within the target area within a preset planning period.

[0060] Step 33, inputting the total vehicle ownership parameter into the preset Leap model, and predicting and outputting the corresponding total hydrogen refueling demand of the target area within the preset planning period.

[0061] In this embodiment, the average annual mileage and energy consumption per 100 kilometers corresponding to the corresponding total vehicle ownership parameters are input into the LEAP model, and the corresponding total hydrogen refueling demand within the preset planning period is calculated and output. The corresponding calculation formula is: total hydrogen refueling demand = total vehicle ownership parameter × average annual mileage × comprehensive energy consumption / 100.

[0062] Step 34, determine the annual filling volume corresponding to the alternative oil and hydrogen stations located in the target area, and determine the planning number corresponding to the target area based on the total hydrogen filling demand and the annual filling volume corresponding to each alternative oil and hydrogen station, wherein the annual filling volume is used to characterize the annual hydrogen filling capacity of each alternative oil and hydrogen station, and each alternative oil and hydrogen station is set with the same annual filling volume.

[0063] In this embodiment, the annual filling capacity of the candidate oil and hydrogen station is calculated by multiplying its daily filling capacity by 365 days. For example, the daily filling capacity of a candidate oil and hydrogen station i is C daily,i (Unit: kg / day), then its annual filling capacity C anmual,i For: C anmual,i =C daily,i ×365; Then, based on the total annual hydrogen demand in the target area (Unit: kg / year), the annual refueling capacity C of each alternative oil and hydrogen station can be calculated. anmual,i , calculate and determine the planned number of oil and hydrogen stations that need to be planned in the target area. Specifically, the required number of planned stations Nstations in the target area is calculated using the following formula: .

[0064] By using the Gompertz model in the above steps, the vehicle ownership parameters corresponding to various target categories of hydrogen-powered vehicles in the target area within the preset planning period are predicted; based on the vehicle ownership parameters corresponding to each target category of hydrogen-powered vehicles, the corresponding total vehicle ownership parameters in the target area within the preset planning period are determined; the total vehicle ownership parameters are input into the preset Leap model to predict and output the total hydrogen refueling demand corresponding to the target area within the preset planning period; the annual refueling volume corresponding to the alternative oil and hydrogen stations located in the target area is determined, and based on the total hydrogen refueling demand and the annual refueling volume corresponding to each alternative oil and hydrogen station, the corresponding planning number of the target area is determined. The annual refueling volume is used to characterize the annual hydrogen refueling capacity of each alternative oil and hydrogen station. The same annual refueling volume is set for each alternative oil and hydrogen station to achieve the determination of the demand planning number of oil and hydrogen stations deployed on each target driving path, providing a decision basis for making site selection decisions.

[0065] In some of the embodiments, an initial location decision is made based on the planned number and the hydrogen vehicle traffic flow parameters to generate first location decision information corresponding to multiple target driving routes, which is achieved by the following steps:

[0066] Step 41, based on the hydrogen vehicle traffic flow parameters, determine the target number of oil and hydrogen stations to be located on each target driving route, wherein the sum of all target numbers is equal to the planned number.

[0067] Step 42, randomly select multiple alternative oil and hydrogen site groups from all alternative oil and hydrogen sites corresponding to each target driving route, and randomly select an alternative hydrogen production site group to supply hydrogen to each alternative oil and hydrogen site group from the alternative hydrogen production sites, wherein each alternative oil and hydrogen site group includes a target number of alternative oil and hydrogen sites, each alternative hydrogen production site group includes at least one alternative hydrogen production site, and the total hydrogen supply corresponding to each alternative hydrogen production site group is not less than the total hydrogen refueling corresponding to each alternative oil and hydrogen site group.

[0068] Step 43, determine the allocation relationship information of hydrogen supply from the alternative hydrogen production site group to the corresponding alternative oil and hydrogen site group, encode multiple alternative oil and hydrogen site groups, multiple alternative hydrogen production site groups and corresponding allocation relationship information, and generate first site selection decision information including multiple first coding bodies, wherein the first coding body includes an alternative oil and hydrogen site group, an alternative hydrogen production site group corresponding to the corresponding alternative oil and hydrogen site group and a corresponding allocation relationship information, and is used to characterize a hydrogen refueling network node site selection and hydrogen supply allocation plan.

[0069] In some embodiments, the current corresponding site selection decision information is updated by the following steps:

[0070] Step 51, obtain multiple current coding bodies corresponding to the current site selection decision information, and obtain the first function value of the first objective function corresponding to each current coding body from the corresponding hydrogenation decision information, wherein the current site selection decision information includes one of the following: the first site selection decision information to be updated, and the second site selection decision information to be updated and iterated.

[0071] Step 52: Determine a second function value of a second objective function corresponding to each current encoding body according to the first function value, and use the second function value as the fitness corresponding to the corresponding current encoding body.

[0072] Step 53, based on the corresponding fitness, perform genetic operations corresponding to the genetic algorithm on multiple current encoding bodies, and iterate the genetic operations on the generated candidate encoding bodies to generate the second site selection decision information corresponding to the current decision, wherein the genetic operations include: selection, crossover and mutation.

[0073] In some of the embodiments, the site selection result is determined according to the target site selection decision information, which is achieved by the following steps:

[0074] Step 61, obtaining a plurality of candidate codes corresponding to the second site selection decision information serving as the target site selection decision information.

[0075] Step 62, based on the fitness corresponding to each alternative coding body, select the alternative coding body with the largest fitness to obtain the target coding body, wherein the site selection result includes the target coding body, and the target coding body includes a target oil and hydrogen site group, a target hydrogen production site group corresponding to the target oil and hydrogen site group, and corresponding target allocation relationship information.

[0076] In some of the embodiments, according to the fitness, the second site selection decision information determined at the time is updated and iterated by using a genetic algorithm and corresponding hydrogenation decision information until the second site selection decision information as the target site selection decision information is generated, including the following steps:

[0077] Step 71, determining whether the fitness is less than a preset fitness threshold.

[0078] Step 72, when it is determined that the fitness is not less than the fitness threshold, the second site selection decision information determined at that time is used as the target site selection decision information.

[0079] Step 73, when it is determined that the fitness is less than the fitness threshold, repeatedly execute the step of using the genetic algorithm and the corresponding hydrogenation decision information to update and iterate the second site selection decision information made at the time until the fitness is not less than the fitness threshold, and obtain the second site selection decision information as the target site selection decision information.

[0080] This embodiment also provides a hydrogenation network node site selection device based on hydrogen source diversity, which is used to implement the above embodiments and preferred implementations, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0081] Figure 3 is a structural block diagram of a hydrogenation network node site selection device based on hydrogen source diversity according to an embodiment of the present application, such as Figure 3 As shown, the device includes a determination module 31, a planning module 32 and a processing module 33, wherein: The determination module 31 is used to determine the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen vehicle traffic flow parameters on multiple target driving paths, and to perform an initial site selection decision based on the planned number and the hydrogen vehicle traffic flow parameters, and to generate first site selection decision information corresponding to the multiple target driving paths; The planning module 32 is coupled to the determination module 31 and is used to update the first site selection decision information by using a preset genetic algorithm and the hydrogenation decision information after receiving the hydrogenation decision information currently corresponding to each target driving path, and generate the second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes the second oil and hydrogen site selected on each target driving path, the second hydrogen production site that supplies hydrogen to all the second oil and hydrogen sites, and the second hydrogen supply distribution information. The hydrogenation decision information is generated by solving the first objective function and the first site selection decision information using the objective solver, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to perform hydrogenation at the first oil and hydrogen site corresponding to the first site selection decision information. The first objective function is used to characterize the driving satisfaction corresponding to all target driving paths; The processing module 33 is coupled to the planning module 32, and is used to calculate the fitness of the second site selection decision information according to the constructed second objective function, and according to the fitness, use the genetic algorithm and the corresponding hydrogenation decision information to update and iterate the second site selection decision information made at the time until the second site selection decision information is generated as the target site selection decision information, and determine the site selection result according to the target site selection decision information, wherein the second objective function is constructed based on the hydrogenation revenue parameters generated by the second oil and hydrogen site, the cost parameters generated by the second oil and hydrogen site and the second hydrogen production site, and the transportation cost parameters corresponding to the second gas supply distribution information, and the hydrogenation revenue parameters are associated with the corresponding first objective function.

[0082] Through the hydrogenation network node site selection device based on hydrogen source diversity in the embodiment of the present application, the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen-powered vehicle traffic flow parameters on multiple target driving paths are determined, and the site selection decision is initialized according to the planned number and the hydrogen-powered vehicle traffic flow parameters, and the first site selection decision information corresponding to the multiple target driving paths is generated; after receiving the hydrogenation decision information currently corresponding to each target driving path, the first site selection decision information is updated using the preset genetic algorithm and the hydrogenation decision information, and the second site selection decision information corresponding to the current decision is generated; according to the constructed second objective function, the fitness of the second site selection decision information is calculated According to the fitness, the genetic algorithm and the corresponding hydrogenation decision information are used to update and iterate the second site selection decision information made at the time until the second site selection decision information is generated as the target site selection decision information, and the site selection result is determined according to the target site selection decision information. This solves the problem that the hydrogenation network planned by the site selection scheme of the hydrogenation network node in the related technology cannot meet the future hydrogenation demand, and the site selection and construction cost of the hydrogenation network node is high. Through the interactive optimization of upper and lower-level decisions, a balance is achieved between the site selection of hydrogenation stations and the hydrogenation behavior of hydrogen-powered vehicles. The site selection and planned hydrogenation network can achieve effective carbon emission reduction, the hydrogenation network has good resilience, and the driving users have low mileage anxiety.

[0083] In some embodiments, the determination module 31 further includes:

[0084] The extraction unit is used to extract the total traffic flow corresponding to each target driving route from the traffic flow data generated by the preset map.

[0085] The first selection unit is coupled to the extraction unit and is used to select a set percentage of traffic flow from the total traffic flow corresponding to each target driving path to obtain hydrogen-powered vehicle traffic flow parameters corresponding to each target driving path, wherein the set percentage is used to characterize the proportion of hydrogen-powered vehicles in the existing vehicle flow.

[0086] In some embodiments, the determination module 31 further includes:

[0087] The first prediction unit is used to predict vehicle ownership parameters corresponding to multiple target categories of hydrogen-powered vehicles in a target area within a preset planning period by using a Gompertz model.

[0088] The calculation unit is coupled to the first prediction unit and is used to determine the corresponding total vehicle ownership parameters in the target area within a preset planning period based on the vehicle ownership parameters corresponding to each target category of hydrogen-powered vehicles.

[0089] The second prediction unit is coupled to the calculation unit and is used to input the total vehicle ownership parameter into a preset Leap model to predict and output the total hydrogen refueling demand corresponding to the target area within a preset planning period.

[0090] The first determination unit is coupled to the second prediction unit, and is used to determine the annual filling volume corresponding to the alternative oil and hydrogen sites located in the target area, and determine the planning number corresponding to the target area based on the total hydrogen filling demand and the annual filling volume corresponding to each alternative oil and hydrogen site, wherein the annual filling volume is used to characterize the annual hydrogen filling capacity of each alternative oil and hydrogen site, and the same annual filling volume is set for each alternative oil and hydrogen site.

[0091] In some embodiments, the planning module 32 further includes:

[0092] The second determination unit is used to determine the target number of oil and hydrogen stations to be selected on each target driving route based on the hydrogen vehicle traffic flow parameters, wherein the sum of all target numbers is equal to the planned number.

[0093] The second selection unit is coupled to the second determination unit, and is used to randomly select multiple alternative oil and hydrogen site groups from all alternative oil and hydrogen sites corresponding to each target driving path, and randomly select an alternative hydrogen production site group from the alternative hydrogen production sites to supply hydrogen to each alternative oil and hydrogen site group, wherein each alternative oil and hydrogen site group includes a target number of alternative oil and hydrogen sites, each alternative hydrogen production site group includes at least one alternative hydrogen production site, and the total hydrogen supply corresponding to each alternative hydrogen production site group is not less than the total hydrogen refueling corresponding to each alternative oil and hydrogen site group.

[0094] The encoding unit is coupled to the second selection unit, and is used to determine the allocation relationship information of the hydrogen supply from the alternative hydrogen production site group to the corresponding alternative oil and hydrogen site group, encode multiple alternative oil and hydrogen site groups, multiple alternative hydrogen production site groups and corresponding allocation relationship information, and generate first site selection decision information including multiple first encoding bodies, wherein the first encoding body includes an alternative oil and hydrogen site group, an alternative hydrogen production site group corresponding to the corresponding alternative oil and hydrogen site group, and a corresponding allocation relationship information, and is used to characterize a hydrogenation network node site selection and hydrogen supply allocation plan.

[0095] In some of the embodiments, the site selection device is also used to obtain multiple current coding bodies corresponding to the current site selection decision information, and obtain the first function value of the first objective function corresponding to each current coding body from the corresponding hydrogenation decision information, wherein the current site selection decision information includes one of the following: the first site selection decision information to be updated, the second site selection decision information to be updated and iterated; according to the first function value, determine the second function value of the second objective function corresponding to each current coding body, and use the second function value as the fitness corresponding to the corresponding current coding body; based on the corresponding fitness, perform genetic operations corresponding to the genetic algorithm on the multiple current coding bodies, and iterate the genetic operations on the generated candidate coding bodies to generate the second site selection decision information corresponding to the current decision, wherein the genetic operations include: selection, crossover and mutation.

[0096] In some embodiments, the processing module 33 further includes:

[0097] The acquisition unit is used to acquire a plurality of candidate codes corresponding to the second site selection decision information serving as the target site selection decision information.

[0098] The third selection unit is coupled to the acquisition unit and is used to select the alternative coding body with the largest fitness based on the fitness corresponding to each alternative coding body to obtain the target coding body, wherein the site selection result includes the target coding body, and the target coding body includes a target oil and hydrogen site group, a target hydrogen production site group corresponding to the target oil and hydrogen site group, and corresponding target allocation relationship information.

[0099] In some of the embodiments, the processing module 33 is also used to determine whether the fitness is less than a preset fitness threshold; when it is determined that the fitness is not less than the fitness threshold, the second site selection decision information determined at that time is used as the target site selection decision information; when it is determined that the fitness is less than the fitness threshold, the steps of using the genetic algorithm and the corresponding hydrogenation decision information to update and iterate the second site selection decision information determined at that time are repeated until the fitness is not less than the fitness threshold, and the second site selection decision information is obtained as the target site selection decision information.

[0100] This embodiment further provides a service platform, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0101] Optionally, the service platform may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0102] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0103] S1, determine the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen vehicle traffic flow parameters on multiple target driving routes, perform an initial site selection decision based on the planned number and the hydrogen vehicle traffic flow parameters, and generate first site selection decision information corresponding to the multiple target driving routes.

[0104] S2, after receiving the hydrogenation decision information currently corresponding to each target driving path, the first site selection decision information is updated using the preset genetic algorithm and the hydrogenation decision information to generate the second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes the second oil and hydrogen site selected on each target driving path, the second hydrogen production site that supplies hydrogen to all second oil and hydrogen sites, and the second hydrogen supply distribution information. The hydrogenation decision information is generated by solving the first objective function and the first site selection decision information using the objective solver, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to refuel at the first oil and hydrogen site corresponding to the first site selection decision information. The first objective function is used to characterize the driving satisfaction corresponding to all target driving paths.

[0105] S3. According to the constructed second objective function, the fitness of the second site selection decision information is calculated. According to the fitness, the second site selection decision information determined at the time is updated and iterated using the genetic algorithm and the corresponding hydrogenation decision information until the second site selection decision information is generated as the target site selection decision information, and the site selection result is determined according to the target site selection decision information, wherein the second objective function is constructed based on the hydrogenation revenue parameters generated by the second oil and hydrogen site, the cost parameters generated by the second oil and hydrogen site and the second hydrogen production site, and the transportation cost parameters corresponding to the second gas supply distribution information, and the hydrogenation revenue parameters are associated with the corresponding first objective function.

[0106] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0107] In addition, in combination with the hydrogenation network node site selection method based on hydrogen source diversity in the above embodiments, the present application embodiment can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the hydrogenation network node site selection methods based on hydrogen source diversity in the above embodiments is implemented.

[0108] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for selecting a node in a hydrogenation network based on hydrogen source diversity, characterized in that: include: Determine the planned number of oil and hydrogen stations to be sited in the target area and the hydrogen vehicle traffic flow parameters on multiple target driving paths, perform an initial site selection decision based on the planned number and the hydrogen vehicle traffic flow parameters, and generate first site selection decision information corresponding to the multiple target driving paths; After receiving the hydrogenation decision information currently corresponding to each of the target driving paths, the first site selection decision information is updated using a preset genetic algorithm and the hydrogenation decision information to generate second site selection decision information corresponding to the current decision, wherein the second site selection decision information includes a second oil and hydrogen site selected on each of the target driving paths, a second hydrogen production site that supplies hydrogen to all of the second oil and hydrogen sites, and second hydrogen supply distribution information. The hydrogenation decision information is generated by solving using a target solver based on the first objective function and the first site selection decision information, and is used to characterize the decision of the corresponding hydrogen-powered vehicle to perform hydrogenation at the first oil and hydrogen site corresponding to the first site selection decision information. The first objective function is used to characterize the driving satisfaction corresponding to all of the target driving paths; According to the constructed second objective function, the fitness of the second site selection decision information is calculated, and according to the fitness, the genetic algorithm and the corresponding hydrogenation decision information are used to iterate the second site selection decision information made at the time, until the second site selection decision information is generated as the target site selection decision information, and the site selection result is determined according to the target site selection decision information, wherein the second objective function is constructed according to the hydrogenation revenue parameter generated by the second oil and hydrogen site, the cost parameter generated by the second oil and hydrogen site and the second hydrogen production site, and the transportation cost parameter corresponding to the second gas supply distribution information, and the hydrogenation revenue parameter is associated with the corresponding first objective function.

2. The method according to claim 1, characterized in that: Determine hydrogen vehicle traffic flow parameters on multiple target driving routes, including: Extracting the total traffic flow corresponding to each target driving route from the traffic flow data generated by the preset map; In the total traffic flow corresponding to each of the target driving paths, a traffic flow with a set percentage is selected to obtain the hydrogen-powered vehicle traffic flow parameter corresponding to each of the target driving paths, wherein the set percentage is used to characterize the proportion of hydrogen-powered vehicles in the existing vehicle flow.

3. The method according to claim 2, characterized in that Determine the planned number of oil and hydrogen sites to be sited in the target area, including: Using the Gompertz model, predicting vehicle population parameters corresponding to various target categories of hydrogen-powered vehicles in the target area within a preset planning period; Based on the vehicle ownership parameter corresponding to each target category of hydrogen-powered vehicles, determining the corresponding total vehicle ownership parameter within the target area within a preset planning period; Input the total vehicle ownership parameter into a preset Leap model, and predict and output the total hydrogen refueling demand corresponding to the target area within a preset planning period; Determine the annual filling volume corresponding to the alternative oil and hydrogen sites located in the target area, and determine the planned number corresponding to the target area based on the total hydrogen filling demand and the annual filling volume corresponding to each of the alternative oil and hydrogen sites, wherein the annual filling volume is used to characterize the annual hydrogen filling capacity of each of the alternative oil and hydrogen sites, and the same annual filling volume is set for each of the alternative oil and hydrogen sites.

4. The method according to claim 1, characterized in that: According to the planned number and the hydrogen-powered vehicle traffic flow parameter, an initialization site selection decision is performed to generate first site selection decision information corresponding to the plurality of target driving paths, including: Based on the hydrogen-powered automobile traffic flow parameters, determining the target number of the oil-hydrogen stations to be located on each of the target driving routes, wherein the sum of all the target numbers is equal to the planned number; Randomly select multiple alternative oil and hydrogen site groups from all the alternative oil and hydrogen sites corresponding to each of the target driving routes, and randomly select an alternative hydrogen production site group that supplies hydrogen to each of the alternative oil and hydrogen site groups from the alternative hydrogen production sites, wherein each of the alternative oil and hydrogen site groups includes the target number of alternative oil and hydrogen sites, each of the alternative hydrogen production site groups includes at least one of the alternative hydrogen production sites, and the total hydrogen supply corresponding to each of the alternative hydrogen production site groups is not less than the total hydrogen refueling corresponding to each of the alternative oil and hydrogen site groups; Determine the allocation relationship information of the hydrogen supply from the alternative hydrogen production site group to the corresponding alternative oil and hydrogen site group, encode multiple alternative oil and hydrogen site groups, multiple alternative hydrogen production site groups and the corresponding allocation relationship information, and generate the first site selection decision information including multiple first coding bodies, wherein the first coding body includes one alternative oil and hydrogen site group, one alternative hydrogen production site group corresponding to the corresponding alternative oil and hydrogen site group and the corresponding allocation relationship information, and is used to characterize a hydrogenation network node site selection and hydrogen supply allocation plan.

5. The method according to claim 4, characterized in that The method further comprises: Acquire multiple current encoding bodies corresponding to the current siting decision information, and acquire the first function value of the first objective function corresponding to each current encoding body from the corresponding hydrogenation decision information, wherein the current siting decision information includes one of the following: the first siting decision information to be updated, and the second siting decision information to be updated and iterated; Determine, according to the first function value, a second function value of the second objective function corresponding to each of the current encoding bodies, and use the second function value as the fitness corresponding to the corresponding current encoding body; Based on the corresponding fitness, genetic operations corresponding to the genetic algorithm are performed on the multiple current encoding bodies, and genetic operations are iterated on the generated candidate encoding bodies to generate the second site selection decision information corresponding to the current decision, wherein the genetic operations include: selection, crossover and mutation.

6. The method according to claim 5, characterized in that Determining a site selection result according to the target site selection decision information includes: Acquire a plurality of candidate codes corresponding to the second site selection decision information as the target site selection decision information; Based on the fitness corresponding to each of the alternative coding bodies, the alternative coding body with the largest fitness is selected to obtain a target coding body, wherein the site selection result includes the target coding body, and the target coding body includes a target oil and hydrogen site group, a target hydrogen production site group corresponding to the target oil and hydrogen site group, and corresponding target allocation relationship information.

7. The method according to claim 5, characterized in that The second objective function R max Set to: R max =R revenue -C1-C2-C3 R revenue = ； The constraints corresponding to the second objective function include: ； The first objective function T sa Set to: T sa = ； The constraints of the first objective function include: ； Among them, R max To maximize the profit function, R revenue is the total revenue corresponding to the hydrogen refueling operation; C1 is the transportation cost of hydrogen from the hydrogen production site to the oil and hydrogen site; C2 is the carbon emission cost generated by hydrogen production; C3 is the land use cost of the oil and hydrogen site; Q represents the set of the shortest round-trip target driving paths q from the starting point to the end point in the target area, and N is the set of oil and hydrogen sites i; N q represents the set of oil and hydrogen stations i on the target driving path q; H is the set of hydrogen production stations h; T is the set of types t of hydrogen vehicles; H price is the unit sales price of hydrogen; w q,t represents the traffic flow parameter of the t-th type of hydrogen-powered vehicle on the target driving path q; d q is the total distance of the target driving path q; is the driving distance per unit amount of hydrogen of the tth type of hydrogen-powered vehicle; is the driving satisfaction of the target driving path q; C fix Refers to the unit activation cost of the tube bundle vehicle for transporting hydrogen; C trans is the unit transportation cost of the bundle truck; d hi is the distance from hydrogen production site h to oil hydrogen site i; S hi is the decision variable, S hi =1, indicating that hydrogen production site h provides hydrogen to oil hydrogen site i, otherwise, S hi =0; C coal is the carbon trading price in the carbon trading market; K h The amount of carbon dioxide produced for each unit of hydrogen produced at the hth oil-hydrogen station; represents the amount of hydrogen added per unit vehicle of the t-th type of hydrogen-powered vehicle on the target driving route q at the oil-hydrogen station i; is the price per unit area for the i-th oil and hydrogen station; is the area of ​​the i-th oil and hydrogen site; X i is the decision variable, X i =1, indicating that the i-th oil-hydrogen station is selected from the candidate site to the oil-hydrogen station on the corresponding driving path, otherwise, X i =0;D h represents the supply capacity of the hth hydrogen production site; CB i represents the initial investment cost of the i-th oil-hydrogen station; F i represents the annual operating cost of the i-th oil and hydrogen station; r represents the discount rate; represents the number constraint of oil and hydrogen stations, N station Indicates the planned number of oil and hydrogen sites to be sited; is the number constraint of hydrogen production sites, N producer represents the number of sites to be selected for hydrogen production; Limiting an oil-hydrogen station to only be supplied with hydrogen by one hydrogen production station; S hi It is limited that hydrogen must be transported from oil and hydrogen stations; Tsa represents the weighted sum of driving satisfaction corresponding to all target driving routes, represents the satisfaction function, and represents the satisfaction of the t-th type of hydrogen-powered vehicle refueling at the i-th oil-hydrogen station on the target driving path q; is the remaining amount of hydrogen when the t-th type of hydrogen-powered vehicle reaches the i-th oil-hydrogen station on the target driving path q, is the critical value of range anxiety of the tth type of hydrogen-powered vehicle, represents the maximum hydrogen storage capacity of the tth type of hydrogen-powered vehicle; The constraints on the choice of oil-hydrogen stations for hydrogen vehicles, Y iq =1, indicating that in the target driving path q, the t-th type of hydrogen-powered vehicle chooses the i-th oil-hydrogen station to stop for hydrogenation, otherwise it is 0; L is a maximum constant; , represents the initial hydrogen capacity constraint of hydrogen vehicles, B oq represents the hydrogen content of the hydrogen vehicle at the starting point of the target driving path q, X oq Indicates whether the starting point o of the target driving path q has a gas station. If yes, X oq =1, otherwise 0; represents the hydrogen remaining amount equation of each oil-hydrogen station on the target driving route q, i and j represent two adjacent oil-hydrogen stations, is the remaining amount of hydrogen at the jth oil-hydrogen station on the target driving path q for the tth type of hydrogen-powered vehicle; is the remaining amount of hydrogen at the i-th oil-hydrogen station on the target driving path q for the t-th type of hydrogen-powered vehicle; d ij is the distance between adjacent oil and hydrogen sites i and j; is the conversion factor, which represents the driving distance per unit amount of hydrogen of the tth type of hydrogen-powered vehicle; is the hydrogen refueling amount constraint, representing the hydrogen refueling amount of the t-th type of hydrogen-powered vehicle at each oil-hydrogen station on the target driving route, is the amount of hydrogen refueling of the t-th type of hydrogen-powered vehicle at the i-th oil-hydrogen station on the target driving route q; Indicates whether the current amount of hydrogen can reach the next oil-hydrogen station. If not, the R of the i-th oil-hydrogen station iq,t =0,B iq,t =0; f is an intermediate variable, f=1 means the remaining amount of hydrogen at the i-th oil-hydrogen station on the target driving path q for the t-th type of hydrogen-powered vehicle is , continue driving long enough to reach the jth oil and hydrogen station, otherwise, f=0.

8. The method according to claim 1, characterized in that According to the fitness, the second site selection decision information determined at the time is updated and iterated by using the genetic algorithm and the corresponding hydrogenation decision information until the second site selection decision information as the target site selection decision information is generated, including: Determining whether the fitness is less than a preset fitness threshold; In the case where it is determined that the fitness is not less than the fitness threshold, the second location decision information determined at the time is used as the target location decision information; When it is determined that the fitness is less than the fitness threshold, the step of using the genetic algorithm and the corresponding hydrogenation decision information to iterate and update the second site selection decision information determined at the time is repeated until the fitness is not less than the fitness threshold, thereby obtaining the second site selection decision information as the target site selection decision information.

9. A service platform, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the method for selecting a hydrogenation network node based on hydrogen source diversity as described in any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for selecting a hydrogenation network node site based on hydrogen source diversity as described in any one of claims 1 to 8 is implemented.

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