Urban hydrogen refueling station locating and sizing optimization method, system, equipment and medium

Abstract

The invention provides an urban hydrogen refueling station locating and sizing optimization method, system, equipment and medium, and belongs to the field of hydrogen energy industry infrastructure planning, and the method comprises the steps: converting traffic network data and hydrogen fuel cell vehicle data into a hydrogenation demand OD matrix through a graph theory, a double-constraint gravity model and a flow-demand conversion model; establishing a deviation demand capture objective function by taking capture of the maximum hydrogenation demand quantity as an objective; establishing an annual comprehensive cost objective function of the hydrogen refueling station by taking minimization of the annual comprehensive cost of the hydrogen refueling station as an objective; based on the objective function, a linear weighting method is adopted, and an urban hydrogen refueling station locating and sizing model captured based on the deviation demand is constructed; and calling a linear solver for solving to obtain a position and capacity scheme of the urban hydrogen refueling station. According to the method, the hydrogen demand deviation in the city is accurately captured, the site selection and capacity configuration of the hydrogen refueling station are optimized, the actual demand can be met, efficient utilization can be achieved on resource configuration, and efficient operation of the urban hydrogenation network is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of hydrogen energy industry infrastructure planning, and specifically relates to a method, system, equipment and medium for optimizing the site selection and capacity determination of a city hydrogen refueling station. Background Art

[0002] Hydrogen is known as one of the promising clean energy sources in the 21st century and plays a key role in promoting energy transformation and sustainable development. With the development and utilization of hydrogen energy and the advancement of hydrogen fuel cell technology around the world, hydrogen fuel cell vehicles have become one of the options for future clean energy transportation with their advantages of zero emissions, high energy density, fast refueling and a wide range of application scenarios. As a key infrastructure for delivering hydrogen to users, hydrogen refueling stations provide convenient and fast hydrogen refueling services and play a vital and fundamental role in supporting the promotion of hydrogen fuel cell vehicles.

[0003] There are two common site selection models for hydrogen refueling stations, namely the point model and the flow model. The point model does not take into account the behavior of drivers refueling hydrogen during travel, and the planning result is often that the hydrogen refueling station is near home or work. The flow model is because it aims to capture vehicle flow, and vehicle flow is not the amount of hydrogen refueling, which makes the flow model unable to be applied to capacity planning strategies. Based on the results of the current site selection model, there will be insufficient number of hydrogen refueling stations and unreasonable distribution. It is necessary to carry out optimization research on the location and capacity of urban hydrogen refueling stations. Summary of the invention

[0004] In order to solve the limitation problem of layout distribution of hydrogen refueling stations, the present invention provides a method, system, equipment and medium for optimizing the site selection and capacity determination of urban hydrogen refueling stations.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing the site selection and capacity of a city hydrogen refueling station comprises the following steps:

[0007] Acquire traffic network data and hydrogen fuel cell vehicle data, wherein the traffic network data includes network topology, node weights, and road section lengths; calculate the shortest distance of all OD pairs of origin and destination points in the traffic network data according to the network topology and road section lengths; calculate the traffic volume of the OD pairs according to the node weights; convert the shortest distance of the OD pairs, the traffic volume of the OD pairs, and the hydrogen fuel cell vehicle data into the total amount of hydrogen required to be refueled for all OD pairs, i.e., the hydrogenation demand OD matrix;

[0008] According to the hydrogenation demand OD matrix, a hydrogenation demand objective function is established with the goal of capturing the maximum hydrogenation demand; an annualized comprehensive cost objective function of the hydrogenation station is established with the goal of minimizing the annualized comprehensive cost of the hydrogenation station;

[0009] A linear weighted method is used for the hydrogenation demand objective function and the annualized comprehensive cost objective function to construct a site selection and capacity determination model for urban hydrogenation stations based on deviation demand capture;

[0010] Solve the city hydrogen station site selection and capacity determination model to obtain the location and capacity of the city hydrogen station

[0011] Preferably, the shortest distances of all OD pairs of origins and destinations in the traffic network data are calculated according to the network topology and the length of the road section; and the traffic flow of the OD pairs is calculated according to the node weights, specifically comprising the following steps:

[0012] The Dijkstra algorithm in graph theory is used to calculate the shortest distance of all OD pairs in the traffic network data, and calculate the nodes that all deviation paths will pass through; the origin and destination OD pairs represent the starting point and arrival point of a certain transportation or trip;

[0013] Based on the shortest distance of the OD pair, the double-constraint gravity model is used to calculate the traffic flow of the OD pair, specifically:

[0014]

[0015] Where q is the index of the OD pair, Q is the set of all OD pair indexes; f q is the traffic flow of OD to q; P o is the traffic volume generated at the starting point O; A d is the traffic attraction of the terminal D; f(l q ) is the road impedance function, which is related to the travel length l q about; o and b d is the equalization factor, calculated as follows:

[0016]

[0017] Preferably, the shortest distance of the OD pairs, the vehicle flow of the OD pairs and the hydrogen fuel cell vehicle data are converted into the total amount of hydrogen required to be filled for all OD pairs using a flow-demand conversion model. The flow-demand conversion model is specifically as follows:

[0018]

[0019] Dem q =Pro q Q q f q ,

[0020] Where ε is the critical threshold of hydrogenation, which is expressed as a percentage, and Pro q The probability that a vehicle with OD q needs hydrogen refueling is related to the trip length lq is proportional to, and inversely proportional to the maximum driving range r of the vehicle under full hydrogen condition, Q q is the hydrogen demand of the vehicle each time it is refueled. This value is determined by the remaining hydrogen volume on board before refueling and the maximum hydrogen volume on board f u Decision, Dem q is the total amount of hydrogen required for the OD pair q, that is, the hydrogenation demand OD matrix.

[0021] Preferably, the hydrogenation demand objective function is specifically:

[0022]

[0023] In the formula, y q is the percentage of hydrogenation demand captured by the deviation path of q.

[0024] Preferably, the annualized comprehensive cost objective function of the hydrogen refueling station is specifically:

[0025]

[0026] Where C is the sum of the annualized comprehensive costs of all hydrogen refueling stations; C inv,j,a is the annualized investment cost of the hydrogen refueling station at point j; C ope,j is the annual operating cost of the hydrogen refueling station at point j; x j It is the decision variable for building a website.

[0027] Preferably, the urban hydrogen station site selection and capacity determination model based on deviation demand capture is specifically:

[0028]

[0029] F=Max(λF1-F2),

[0030] Where F1 is the function for maximizing hydrogenation demand capture, F2 is the function for minimizing annualized comprehensive cost, and p H2 is the selling price of hydrogen; d ope is the number of days the hydrogen refueling station is in operation.

[0031] Preferably, it also includes setting constraints on the hydrogenation demand objective function and the annualized comprehensive cost objective function. The present invention also provides a system for optimizing the site selection and capacity of urban hydrogenation stations, specifically comprising:

[0032] The data acquisition module is used to acquire traffic network data and hydrogen fuel cell vehicle data, wherein the traffic network data includes network topology, node weights and section lengths; the shortest distance of all OD pairs of starting and ending points in the traffic network data is calculated according to the network topology and section lengths; the traffic flow of the OD pairs is calculated according to the node weights; and the shortest distance of the OD pairs, the traffic flow of the OD pairs and the hydrogen fuel cell vehicle data are converted into the total amount of hydrogen required to be refueled for all OD pairs, i.e., the hydrogenation demand OD matrix.

[0033] The target establishment module is used to establish a hydrogenation demand objective function based on the hydrogenation demand OD matrix with the goal of capturing the maximum hydrogenation demand; and to establish an annualized comprehensive cost objective function of the hydrogenation station with the goal of minimizing the annualized comprehensive cost of the hydrogenation station.

[0034] The model module is used to adopt a linear weighted method for the hydrogenation demand objective function and the annualized comprehensive cost objective function to construct a city hydrogenation station site selection and capacity determination model based on deviation demand capture.

[0035] The site selection and capacity determination module is used to solve the site selection and capacity determination model of the urban hydrogen refueling station to obtain the location and capacity of the urban hydrogen refueling station.

[0036] The present invention also provides a computer device, including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the method for optimizing the site selection and capacity of a city hydrogen refueling station.

[0037] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, the steps described in the method for optimizing the site selection and capacity of a city hydrogen refueling station can be executed.

[0038] The method for optimizing the site selection and capacity of a city hydrogen refueling station provided by the present invention has the following beneficial effects:

[0039] Obtain traffic network data and hydrogen fuel cell vehicle data, calculate the shortest distance of OD pairs and the traffic flow of OD pairs in the traffic network based on the traffic network data, and convert the obtained shortest distance of OD pairs, traffic flow of OD pairs and hydrogen fuel cell vehicle data into a hydrogenation demand OD matrix to capture the amount of hydrogenation based on deviation demand in the city and apply it to the site selection planning strategy, so as to avoid the unreasonable number and location arrangement of hydrogenation stations caused by considering the traffic flow alone. Based on the hydrogenation demand OD matrix, establish the capture objective function and the annualized comprehensive cost objective function of the hydrogenation station, and use the linear weighted method to construct a city hydrogenation station site selection and capacity determination model based on deviation demand capture to obtain the location and capacity of the city hydrogenation station. The site selection and capacity configuration of the hydrogenation station obtained by establishing the model with demand deviation as the target can not only meet the actual needs, but also improve the coverage level and reliability of hydrogenation stations, and reduce construction and operation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 The present invention is a flowchart of a method for optimizing the site selection and capacity determination of a city hydrogen refueling station according to an embodiment of the present invention.

[0042] Figure 2 This is the traffic network topology diagram in Example 2 of the present invention.

[0043] Figure 3 This is a schematic diagram of the optimization results of Example 2 of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0045] Example 1

[0046] The present invention provides a method for optimizing the site selection and capacity of a city hydrogen refueling station, specifically: Figure 1 As shown, the following steps are included:

[0047] S1. Obtain traffic network data and hydrogen fuel cell vehicle data, and use graph theory, dual-constraint gravity model, and flow-demand conversion model to convert traffic network data and hydrogen fuel cell vehicle data into a hydrogen refueling demand OD matrix.

[0048] Traffic network data specifically includes network topology, node weights, and road section lengths; hydrogen fuel cell vehicle data specifically includes the maximum driving range of the vehicle, the maximum amount of hydrogen on board, etc. OD pairs are the starting and ending points, indicating the starting and arrival points of a certain transport or trip, and are often used to study the flow patterns and traffic needs of people, logistics, and vehicles.

[0049] Step S11: First, input the topological structure and section length of the traffic network, and use the Dijkstra algorithm in graph theory to calculate the shortest distances of all OD pairs in the traffic network.

[0050] Take the deviation percentage as 10%, find the intermediate node by enumeration method, and compare whether the shortest distance of the OD pair passing through the intermediate node exceeds 10% of the shortest distance of the OD pair. If not, the intermediate node is on the deviation path of the OD pair. If it exceeds, it is not.

[0051] Step S12: The double-constraint gravity model is a model used to predict and analyze traffic flow or passenger flow. The model takes into account both the supply constraints of the departure point and the demand constraints of the destination, ensuring that the total flow is consistent with the actual supply and demand. Based on the node weight data, the double-constraint gravity model is used to calculate the traffic flow of all OD pairs. The traffic flow of OD pairs is the traffic flow data between the departure point and the arrival point of the transportation or trip. The formula is as follows:

[0052]

[0053] Where q is the index of the OD pair, Q is the set of all OD pair indexes; f q is the traffic flow of OD to q; P o is the traffic volume generated at the starting point O; A d is the traffic attraction of the terminal D; f(l q ) is the road impedance function, which is related to the travel length l q about; o and b d is the equalization factor, calculated as follows:

[0054]

[0055] Step S13: If the trip will cause the amount of hydrogen on board to fall below a certain threshold, the driver needs to find a hydrogen refueling station to refuel. Since different ODs have different trip lengths, the amount of hydrogen consumed during the trip is also different. Obviously, drivers with longer trips are more likely to refuel, while drivers with shorter trips are less likely to refuel. The traditional flow capture model does not take this difference into account. Therefore, the present invention proposes a flow-demand conversion model to address this phenomenon. The formula is as follows:

[0056]

[0057]

[0058] Dem q =Pro q Q q f q (6).

[0059] Where ε is the critical threshold of hydrogenation, and its value is a percentage. Formula (4) calculates the probability Pro that OD for a vehicle q needs hydrogenation q , which is related to the stroke length l q It is directly proportional to the maximum driving range r of the vehicle. Formula (5) calculates the hydrogen demand Q of the vehicle each time it is refueled q , which is determined by the remaining amount of hydrogen on board before refueling and the maximum amount of hydrogen on board f u Formula (6) calculates the total amount of hydrogen required for the OD pair q, that is, the hydrogenation demand OD matrix.

[0060] S2. With the goal of capturing the maximum hydrogenation demand, a deviation demand capture model is established.

[0061] Step S21: With the goal of capturing the maximum hydrogenation demand, an objective function is established, and the formula is as follows:

[0062]

[0063] In the formula, y q is the percentage of hydrogenation demand captured by the deviation path of q.

[0064] Step S22: Establish corresponding constraint conditions, as follows:

[0065]

[0066] Where j is the location index of the candidate hydrogen refueling station; J is the location set of the candidate hydrogen refueling stations; y qj is a decision variable between 0 and 1, indicating whether the deviation path of q is captured by the hydrogen refueling station at point j; N qj is whether the deviation path of q contains the 0-1 parameter of j; x j is a 0-1 variable indicating whether the hydrogen refueling station is built at location j; s j is the capacity of the hydrogen refueling station at point j.

[0067] Formula (8) specifies the number of hydrogen refueling stations to be built in the network; Formula (9) ensures that the deviation path of q must pass through at least point j, and the hydrogen refueling demand of the deviation path of q can be captured by point j; Formula (10) restricts that the hydrogen refueling demand of the deviation path of q can only be captured by point j when there is a hydrogen refueling station at point j; Formulas (11) and (12) calculate the sum of the percentages of the deviation path of q that are captured, and y q is at most 1; formula (13) ensures that the sum of the demands captured by any facility does not exceed its capacity.

[0068] Due to various reasons such as technical level, it is impossible to set a single site hydrogen refueling station to unlimited capacity. Therefore, it is necessary to limit the capacity of the hydrogen refueling station to prevent it from capturing hydrogen beyond its carrying capacity. The formula is as follows:

[0069]

[0070] In the formula, s mi n and s max are the minimum and maximum capacities of a hydrogen refueling station. Formula (14) limits the upper and lower limits of the capacity of a hydrogen refueling station.

[0071] Step S3: Establish an annualized cost model for hydrogen refueling stations with the goal of minimizing the annualized comprehensive cost of hydrogen refueling stations. The annualized comprehensive cost model for hydrogen refueling stations uses the present value-annual value conversion method and uses the annualized comprehensive cost index to comprehensively evaluate various financial conditions, including the annualized investment cost and the annualized operating cost. The formula is as follows:

[0072]

[0073] Where C is the sum of the annualized comprehensive costs of all hydrogen refueling stations; C inv,j,a is the annualized investment cost of the hydrogen refueling station at point j; C ope,j is the annual operating cost of the hydrogen refueling station at point j.

[0074] Annual investment cost C inv,j,a The calculation is the investment cost multiplied by the present value-annual value conversion factor, which is calculated as follows:

[0075]

[0076] In the formula, C inv,j is the total capital investment cost; T0 is the operating life of the hydrogen refueling station; τ is the discount rate; a is the year. Investment cost C inv,j It includes fixed investment costs and variable equipment investment costs of capacity-related facilities. Variable equipment investment costs include: investment costs of hydrogen storage system, compression system, filling system, cooling system and other equipment. The calculation is as follows:

[0077] Cinv,j =C f +(c tank H tank,j +c com P com,j +c dis N dis,j +C cool +C oth,j ) (17).

[0078] In the formula, C f is the fixed investment cost of the hydrogen refueling station; c tank 、c comp and c dis are the investment cost coefficients of the hydrogen storage system, compression system, and filling system; H tank,j is the capacity of the hydrogen storage system of the hydrogen refueling station at point j; P com,j is the rated power of the compression system of the hydrogen refueling station at point j; N dis,j is the number of refueling guns at the hydrogen refueling station at point j; C cool is the investment cost of the cooling system of the hydrogen refueling station; C oth,j It is the investment cost of other related equipment.

[0079] The capacity of the hydrogen storage system, the rated power of the compression system, and the number of filling guns at the hydrogen filling station can be calculated using the following formula:

[0080]

[0081] Where η tan k is the ratio of the capacity of the hydrogen refueling station to the capacity of the hydrogen storage system under standard configuration; η comp is the ratio of the capacity of the hydrogen station in standard configuration to the rated power of the compression system; is the average amount of hydrogen that can be dispensed per day per filling gun; It is to round up the calculation result.

[0082] Other investment costs of hydrogen refueling stations mainly include insurance and safety system costs, which can be analyzed using the factor method based on empirical data and calculated as follows:

[0083]

[0084] Where σ is the factor weight of other investment costs.

[0085] The annual operating cost can be calculated as a percentage of the annual investment cost plus the hydrogen procurement cost, as follows:

[0086]

[0087] In the formula, δ is the proportional coefficient of operating cost; It is the purchase price per kilogram of hydrogen.

[0088] S4. Using the linear weighted method, a model for site selection and sizing of urban hydrogen refueling stations based on deviation demand capture is constructed, including dual objective functions and constraints.

[0089] The present invention optimizes the location and capacity of hydrogen refueling stations to achieve two goals: capturing more hydrogen refueling needs in cities and reducing the related costs of hydrogen refueling stations, thereby improving the coverage level and economy of hydrogen refueling stations and achieving efficient operation of urban hydrogen refueling networks. j and S j are two optimization variables.

[0090] Considering that maximizing demand capture F1 and minimizing annualized comprehensive cost F2 are two contradictory goals, in order to solve this problem, the linear weighted sum method is used to combine them into a comprehensive goal. Demand capture, that is, the amount of hydrogen sold by HRS, is positively correlated with the income of the hydrogen refueling station, so the two objective functions are weighted using the economic coefficient method. The coefficient of F1 is λ, which is constructed as the product of the hydrogen price and the number of operating days. The coefficient of F2 is 1. The new objective function is shown in formula (26):

[0091]

[0092] F = Max(λF1-F2)(26).

[0093] In the formula, p H2 is the selling price of hydrogen; d ope is the number of days the hydrogen refueling station is in operation.

[0094] The constraints are equations (8)-(14), (16)-(22).

[0095] S5. The location and capacity plan of the urban hydrogen refueling station is obtained by calling a linear solver to solve the urban hydrogen refueling station site selection and capacity determination model based on the deviation demand capture.

[0096] Example 2

[0097] A 24-node traffic network is used as the target traffic network, and its traffic network topology is as follows: Figure 2 To plan the location and capacity of 5 hydrogen refueling stations, the input parameters are shown in Table 1.

[0098] Table 1 Input parameters of target transportation network

[0099]

[0100]

[0101] Consider the following cases for comparison to demonstrate the effect of the urban hydrogen station site selection and sizing model, and the results are shown in Table 2. Case 1: The goal is to capture the maximum vehicle flow without considering deviation.

[0102] Case 2: Aiming to capture the maximum hydrogenation demand without considering the deviation.

[0103] Case 3: Aiming to capture the maximum traffic flow, taking into account the deviation.

[0104] Case 4: Aiming to capture the maximum hydrogenation demand, taking into account deviations.

[0105] Table 2 Optimization results of 4 cases

[0106]

[0107] The relationship between traffic capture rate, demand capture rate and net profit in the four cases is as follows Figure 3 . Compared with the model without considering the deviation path, the model considering the deviation path has increased flow capture rate and demand capture rate, and increased the total net profit of the hydrogen refueling station. Specifically, the flow capture rate of Case 3 is 1.17% higher than that of Case 1, and the flow capture rate of Case 4 is 3.72% higher than that of Case 2. The demand capture rate of Case 3 is 1.56% higher than that of Case 1, and the demand capture rate of Case 4 is 1.73% higher than that of Case 2. This shows that the models considering the deviation path are more effective in increasing the captured flow and demand. In addition, these models are more consistent with the drivers' hydrogen refueling behavior, which helps to adjust the capacity of hydrogen refueling stations more reasonably.

[0108] According to the comparison results, the models based on hydrogen refueling demand show a unique feature: although their flow capture rate is lower than that of the flow-based models, their demand capture rate and total net profit are higher. These results show that the models based on hydrogen refueling demand are superior in meeting the needs of service vehicles. This also enables hydrogen refueling stations to achieve greater net profits. The optimization results are shown in the schematic diagram Figure 3 shown.

[0109] Therefore, the urban hydrogen refueling station site selection and capacity determination model can not only meet the needs of more vehicles and accelerate the promotion and application of HFCV, but also increase the net profit of hydrogen refueling stations, meet the preferences of investors, and is more suitable for the early planning strategy of urban hydrogen refueling stations.

[0110] The present invention also provides a system for optimizing the site selection and capacity of a city hydrogen refueling station, which specifically includes:

[0111] The data acquisition module is used to obtain traffic network data and hydrogen fuel cell vehicle data. The traffic network data includes network topology, node weights and road section lengths; the shortest distance of all OD pairs of origin and destination points in the traffic network data is calculated based on the network topology and road section lengths; the traffic flow of OD pairs is calculated based on the node weights; the shortest distance of OD pairs, the traffic flow of OD pairs and hydrogen fuel cell vehicle data are converted into the total amount of hydrogen required for all OD pairs, that is, the hydrogenation demand OD matrix.

[0112] The target establishment module is used to establish the hydrogenation demand objective function based on the hydrogenation demand OD matrix with the goal of capturing the maximum hydrogenation demand; and to establish the annualized comprehensive cost objective function of the hydrogenation station with the goal of minimizing the annualized comprehensive cost of the hydrogenation station.

[0113] The model module is used to adopt the linear weighted method for the hydrogen refueling demand objective function and the annualized comprehensive cost objective function to construct a city hydrogen refueling station site selection and capacity determination model based on deviation demand capture.

[0114] The site selection and capacity determination module is used to solve the site selection and capacity determination model of urban hydrogen refueling stations to obtain the location and capacity of urban hydrogen refueling stations.

[0115] Each module in the above-mentioned urban hydrogen refueling station site selection and capacity optimization system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0116] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps in an embodiment of a method for optimizing the site selection and capacity of a city hydrogen refueling station. The specific implementation method can be found in the method embodiment, which will not be described in detail here.

[0117] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, and a computer program is stored on the storage medium. For example, a memory containing instructions, the above instructions can be executed by a processor of a computer device to complete the above method. For example, a non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, it can implement the steps in an embodiment of a method for optimizing the site selection and capacity of a city hydrogen refueling station. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0118] It will be appreciated by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A method for optimizing the site selection and capacity of urban hydrogen refueling stations, characterized in that: The following steps are involved: Acquire traffic network data and hydrogen fuel cell vehicle data, wherein the traffic network data includes network topology, node weights, and road section lengths; calculate the shortest distance of all OD pairs of origin and destination points in the traffic network data according to the network topology and road section lengths; calculate the traffic volume of the OD pairs according to the node weights; convert the shortest distance of the OD pairs, the traffic volume of the OD pairs, and the hydrogen fuel cell vehicle data into the total amount of hydrogen required to be refueled for all OD pairs, i.e., the hydrogenation demand OD matrix; According to the hydrogenation demand OD matrix, a hydrogenation demand objective function is established with the goal of capturing the maximum hydrogenation demand; an annualized comprehensive cost objective function of the hydrogenation station is established with the goal of minimizing the annualized comprehensive cost of the hydrogenation station; A linear weighted method is used for the hydrogenation demand objective function and the annualized comprehensive cost objective function to construct a site selection and capacity determination model for urban hydrogenation stations based on deviation demand capture; The city hydrogen refueling station site selection and capacity determination model is solved to obtain the location and capacity of the city hydrogen refueling station.

2. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: Calculating the shortest distances of all OD pairs of origins and destinations in the traffic network data according to the network topology and the length of the road section; and calculating the traffic flow of the OD pairs according to the node weights, specifically comprising the following steps: The Dijkstra algorithm in graph theory is used to calculate the shortest distance of all OD pairs in the traffic network data, and calculate the nodes that all deviation paths will pass through; the origin and destination OD pairs represent the starting point and arrival point of a certain transportation or trip; Based on the shortest distance of the OD pair, the double-constraint gravity model is used to calculate the traffic flow of the OD pair, specifically: Where q is the index of the OD pair, Q is the set of all OD pair indexes; f q is the traffic flow of OD to q; P o is the traffic volume generated at the starting point O; A d is the traffic attraction of the terminal D; f(l q ) is the road impedance function, which is related to the travel length l q about; o and b d is the equalization factor, calculated as follows:

3. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: The flow-demand conversion model is used to convert the shortest distance of the OD pair, the traffic volume of the OD pair, and the hydrogen fuel cell vehicle data into the total amount of hydrogen required to be filled for all OD pairs. The flow-demand conversion model is specifically as follows: Dem q =For q Q q f q , Where ε is the critical threshold of hydrogenation, which is expressed as a percentage, and Pro q The probability that a vehicle with OD q needs hydrogen refueling is related to the trip length l q is proportional to, and inversely proportional to the maximum driving range r of the vehicle under full hydrogen condition, Q q is the hydrogen demand of the vehicle each time it is refueled. This value is determined by the remaining hydrogen volume on board before refueling and the maximum hydrogen volume on board f u Decision, Dem q is the total amount of hydrogen required to be added for the OD pair q, that is, the hydrogenation demand OD matrix.

4. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: The hydrogenation demand objective function is specifically: In the formula, y q is the percentage of hydrogenation demand captured by the deviation path of q.

5. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: The annualized comprehensive cost objective function of the hydrogen refueling station is specifically: Where C is the sum of the annualized comprehensive costs of all hydrogen refueling stations; C inv,j,a is the annualized investment cost of the hydrogen refueling station at point j; C ope,j is the annual operating cost of the hydrogen refueling station at point j; x j It is the decision variable for building a website.

6. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: The urban hydrogen station site selection and capacity determination model based on deviation demand capture is specifically: λ=p H2 d ope , F=Max(λF1-F2), Where F1 is the function for maximizing hydrogenation demand capture, and F2 is the function for minimizing annualized comprehensive cost. is the selling price of hydrogen; d ope is the number of days the hydrogen refueling station is in operation.

7. The method for optimizing the site selection and capacity of a city hydrogen refueling station according to claim 1, characterized in that: It also includes setting constraints on the hydrogenation demand objective function and the annualized comprehensive cost objective function.

8. A city hydrogen refueling station site selection and capacity optimization system, characterized in that: include: A data acquisition module is used to acquire traffic network data and hydrogen fuel cell vehicle data, wherein the traffic network data includes network topology, node weights, and road section lengths; calculate the shortest distance of all OD pairs of origin and destination points in the traffic network data according to the network topology and road section lengths; calculate the traffic volume of the OD pairs according to the node weights; convert the shortest distance of the OD pairs, the traffic volume of the OD pairs, and the hydrogen fuel cell vehicle data into the total amount of hydrogen required to be refueled for all OD pairs, i.e., the hydrogenation demand OD matrix; A target establishment module is used to establish a hydrogenation demand objective function based on the hydrogenation demand OD matrix with the goal of capturing the maximum hydrogenation demand; and to establish an annualized comprehensive cost objective function of the hydrogenation station with the goal of minimizing the annualized comprehensive cost of the hydrogenation station; A model module is used to construct a site selection and capacity determination model for urban hydrogen refueling stations based on deviation demand capture by using a linear weighted method for the hydrogen refueling demand objective function and the annualized comprehensive cost objective function; The site selection and capacity determination module is used to solve the site selection and capacity determination model of the urban hydrogen refueling station to obtain the location and capacity of the urban hydrogen refueling station.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, the computer program can execute the steps of the method according to any one of claims 1 to 7.

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