A multi-stage planning method and system for various types of energy stations considering mixed traffic flow.

Abstract

This invention provides a multi-stage planning method and system for various types of energy stations that take into account mixed traffic flow. [Solution] A multi-stage planning method for various types of energy stations considering mixed traffic flow includes the steps of: constructing a traffic topology; considering a time-dependent road impedance function model and obtaining traffic conditions in the traffic topology of different types of energy vehicles; considering the travel needs of different types of energy vehicles and planning various different types of energy refueling routes for each; and constructing a mixed traffic flow model based on the obtained various different types of energy refueling routes for each type of energy vehicle with the goal of minimizing total costs, thereby completing the multi-stage planning for various types of energy stations.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This invention claims priority to the Chinese patent application filed with the China National Intellectual Property Administration on September 11, 2024, application number 202411266150.7, titled "Multi-stage planning method and system for various energy stands considering mixed traffic flow," the entire contents of which are incorporated by reference to this invention for all purposes and constitute part of this invention.

[0002] The present invention belongs to the technical field of energy station planning, and more specifically, relates to a multi-stage planning method and system for various types of energy stations that take into account mixed traffic flow. [Background technology]

[0003] This section merely provides background information related to the present invention and does not necessarily constitute prior art.

[0004] New energy vehicles (NEVs) possess significant advantages and potential as an environmentally friendly mode of transportation, particularly in terms of reducing carbon dioxide emissions and mitigating the energy crisis. With the continued development of NEVs, the demand for vehicle energy refueling is also steadily increasing. However, rational planning of energy refueling stations is key to meeting user needs, improving energy refueling efficiency, reducing urban land use pressure, and alleviating the load on the power grid. Conventional energy refueling station planning often focuses on only one type of energy refueling station (e.g., charging stations, hydrogen stations), failing to consider the actual mobility needs of different types of energy vehicles in daily life and thus failing to fully meet diverse transportation needs. Furthermore, existing planning policies are mostly static, single-stage plans, prone to over-equipment in the initial stages and under-equipment in later stages, making it difficult to meet the rapidly increasing energy refueling needs of vehicles. [Overview of the project]

[0005] To solve the above problems, the present invention proposes a multi-stage planning method and system for various types of energy stations considering mixed traffic flow, taking into account the movement needs of different types of energy vehicles, and performing multi-stage planning of various types of energy stations with the goal of minimizing the total cost. While realizing the planning of mixed energy stations, it effectively avoids problems such as equipment surplus and upfront investment in the initial stage of planning, and equipment shortage and service quality decline in the later stage of planning.

[0006] According to some embodiments, the first aspect of the present invention provides a multi-stage planning method for various types of energy stations considering mixed traffic flow, which adopts the following technical solutions.

[0007] Constructing a traffic topology; Considering the time-varying road impedance function model and obtaining the passing status of different types of energy vehicles in the constructed traffic topology; Considering the movement needs of different types of energy vehicles and planning the energy filling routes of various different types of energy vehicles respectively based on the obtained passing status; Constructing a mixed traffic flow model with the goal of minimizing the total cost based on the obtained energy filling routes of various different types of energy vehicles; Completing the multi-stage planning of various types of energy stations based on the constructed mixed traffic flow model; A multi-stage planning method for various types of energy stations considering mixed traffic flow, including the above steps.

[0008] As a further technical limitation, in the process of planning the energy filling routes of various different types of energy vehicles, the obtained vehicle passing status is incorporated, and the Dijkstra algorithm is used to plan the vehicle movement routes.

[0009] Furthermore, the process of planning vehicle travel routes using Dijkstra's algorithm involves obtaining the vehicle's real-time road impedance function and departure / destination nodes based on the adjacency matrix and road impedance function of the traffic topology network, calculating the optimal route from all the vehicle's nodes to the target node according to the obtained departure / destination nodes, and completing the vehicle travel route planning.

[0010] As a further technical limitation, in the multi-stage planning process for various types of energy stations, the target function of the mixed traffic flow model is normalized, a multi-stage planning method based on data envelope analysis is used, different stages are evaluated for the normalized target function, and the energy refueling needs of different types of energy vehicles at different stages, taking into account the construction sequence, are met, and a plan for various types of energy stations is obtained.

[0011] Furthermore, by changing the number of different types of energy stands and their corresponding candidate nodes in the evaluation process at different stages, different plan designs for the same stage are obtained. A plan evaluation based on data envelope analysis is then performed on the obtained different plan designs for the same stage to obtain the optimal construction plan for that stage. The next stage of planning is then carried out based on the obtained optimal construction plan, and this process continues until the multi-stage planning is completed, obtaining the final plan for each stage and completing the evaluation of each stage.

[0012] As a further technical limitation, the target function of the constructed mixed traffic flow model includes at least charging station costs, hydrogen station costs, hybrid energy refueling station costs, and carbon emission costs, and the constraints on the target function include at least tidal constraints, electric energy storage constraints, hydrogen production and storage constraints, and carbon emission flow constraints.

[0013] According to several embodiments, a second solution of the present invention provides a multi-stage planning system for various types of energy stations that take mixed traffic flow into consideration, employing the following technical solution.

[0014] An acquisition module configured to construct a traffic topology, consider a time-varying road impedance function model, and acquire traffic conditions for different types of energy vehicles in the constructed traffic topology, An initial planning module is configured to plan energy refueling routes for various different types of energy vehicles, taking into account the mobility needs of different types of energy vehicles and based on acquired traffic conditions. A modeling module configured to construct a mixed traffic flow model with the goal of minimizing total cost based on the energy refueling paths of various different types of energy vehicles obtained, A multi-stage planning module configured to complete multi-stage planning for various types of energy stations based on a constructed mixed traffic flow model, This is a multi-stage planning system for various types of energy stations that takes into account mixed traffic flow.

[0015] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium employing the following technical solution.

[0016] A computer-readable storage medium on which a program is stored, and when the program is executed by a processor, the steps of the multi-stage planning method for various types of energy stands considering mixed traffic flow described in the first proposal of the present invention are realized.

[0017] According to some embodiments, a fourth solution of the present invention provides an electronic device employing the following technical solution.

[0018] The electronic device includes memory, a processor, and a program stored in the memory and executed on the processor, wherein when the processor executes the program, the steps of the multi-stage planning method for various types of energy stands considering mixed traffic flow as described in the first version of the present invention are realized.

[0019] According to several embodiments, a fifth solution of the present invention provides a computer program product that employs the following technical solution.

[0020] The present invention is a computer program product that includes software code, the program in which the program executes the steps of a multi-stage planning method for various types of energy stations considering mixed traffic flow as described in the first proposal of the present invention.

[0021] Compared to conventional technology, the beneficial effects of the present invention are as follows:

[0022] This invention considers the mobility needs of different types of energy vehicles and aims to minimize total costs by implementing a multi-stage planning system for various types of energy stations. This system realizes the planning of a mixed energy station while effectively avoiding challenges such as over-equipment and upfront investment in the initial planning stages, and under-equipment and reduced service quality in the later planning stages.

[0023] This invention constructs a mixed traffic flow model that captures the energy refueling loads of electric vehicles and hydrogen fuel cell vehicles within the same transportation network, and is more closely suited to the mobility needs of different types of energy vehicles in real life compared to a traffic flow model that considers only one type of vehicle. Based on the planning of charging stations and hydrogen stations, it realizes the planning of hybrid energy refueling stations, improves land use efficiency in areas with high vehicle traffic, reduces land costs for energy refueling station construction, and reduces the investment cost of the same equipment at two types of energy refueling stations. Furthermore, it effectively avoids problems such as over-equipment and upfront investment in the early planning stages, and under-equipment and reduced service quality in the later planning stages.

[0024] The drawings in this specification, which are part of this embodiment, are for the purpose of further understanding this embodiment, and the exemplary embodiments and their descriptions are for the purpose of interpreting this embodiment and are not intended to improperly limit this embodiment. [Brief explanation of the drawing]

[0025] [Figure 1] This is a schematic diagram of a multi-stage plan considering the construction sequence in Embodiment 1 of the present invention. [Figure 2] This is a flowchart of a multi-stage plan based on data envelope analysis in Example 1 of the present invention. [Modes for carrying out the invention]

[0026] The present invention will be further described below with reference to drawings and embodiments. It should be noted that the following detailed description is illustrative and intended to further illustrate this application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which this invention pertains.

[0027] It should be noted that the terms used herein are merely for describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. For example, unless otherwise specified in the context, the singular form used herein is intended to include plural forms, and it should also be understood that when the terms “contains” and / or “includes” are used herein, it indicates the presence of features, processes, operations, devices, assemblies and / or combinations thereof.

[0028] In this invention, the directions or positional relationships indicated by terms such as "up," "down," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" are based on the drawings and are merely relative terms established to easily explain the structural relationships of each component or element of this invention. They do not specifically refer to any component or element of this invention and should not be understood as limiting the invention.

[0029] The embodiments and features of the present invention can be combined with each other, as long as they do not contradict each other.

[0030] Example 1 Embodiment 1 of the present invention introduces a multi-stage planning method for various types of energy stations that take into account mixed traffic flow.

[0031] In this embodiment, for planning different types of energy stations, mixed traffic flow is comprehensively considered, and charging stations and hydrogen stations are planned integrally. Specifically, the traffic network topology is modeled, and the traffic conditions of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) in the traffic network are described by a time-dependent road impedance function. Using HFCVs as an example, the energy refueling routes for HFCVs are planned based on an improved Dijkstra algorithm, the energy refueling loads of different nodes in the traffic network are captured, candidate nodes are provided for the planning of charging stations, hydrogen stations, and hybrid energy refueling stations, and finally, a target function for planning multiple types of energy refueling stations is established so that the total annual system cost is minimized, and multi-stage planning is performed using a multi-stage planning method based on data envelope analysis (DEA) to meet the energy refueling needs of users at different stages. Compared to conventional energy refueling station planning, this embodiment takes into account the mobility needs of different types of vehicles during the planning stage, making it more suitable for actual living conditions, and effectively avoiding problems such as over-equipment and upfront investment in the early stages of planning, and under-equipment and reduced service quality in the later stages of planning.

[0032] In this embodiment, nodes in the network are defined as intersections, and arcs between nodes are defined as road sections, and free passage time

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[0033] It should be explained that the vehicle flow rate in this embodiment is not a single type of vehicle flow rate, but includes both EVs and HFCVs.

[0034] Each route is formed by connecting different road sections, and the traffic volume of each route is the cumulative traffic volume of the road sections it passes through. Therefore, the traffic volume of a road section can be expressed as follows:

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[0035] In actual vehicle movement, since the road may be congested, in the method of determining the travel time only by distance, the calculated travel time will be shorter than the actual one, and the traffic situation of the vehicle cannot be accurately obtained. Therefore, the present invention adopts the road impedance function model provided by the Bureau of Public Roads (BPR) in the United States to show the actual time for a vehicle to pass through a certain road section with the travel time changing over time, that is, as follows:

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[0036] The hydrogen filling time and hydrogen filling amount of an HFCV are determined by the driving distance and the state of hydrogen (SOH) of the HFCV. At the end or start of a journey, the driver determines whether to fill hydrogen based on the SOH at this time and the future driving distance. That is, if the SOH of the HFCV exceeds the driving requirements to the current destination, driving continues until the end of the journey in this section. If the SOH is below the threshold or cannot meet the current journey, hydrogen is filled nearby. When the hydrogen filling amount meets the distance requirements of the current destination, the hydrogen filling stops.

[0037] Assuming that the remaining driving distance of the HFCV is shorter than the driving distance, it is guaranteed that each HFCV passes through at least one hydrogen station and there is sufficient fuel in the HFCV to drive to the hydrogen station and complete hydrogen filling. When the SOH of the HFCV at time t satisfies H t ≦ H(d(i,t)), the vehicle performs a hydrogen filling behavior to find the nearest hydrogen station at this time. Here, H tH(d(i,t)) is the State of Health (SOH) of the HFCV at time t, d(i,t) is the distance between the position of the i-th car at time t and the destination, and H(d(i,t)) represents the SOH required to complete the distance d(i,t).

[0038] Since the logic of EV charging is similar to that of HFCV hydrogen refueling, a detailed explanation will be omitted in this embodiment.

[0039] In this embodiment, based on the Dijkstra algorithm, real-time road condition information is incorporated to dynamically plan the shortest travel route for the user. Specifically, each time the user's vehicle travels and arrives at a node, the algorithm automatically adjusts the initial planned route in response to changes in road conditions and determines the next target node, as follows:

[0040] (a) Obtain real-time road impedance functions and origin / destination nodes based on the adjacency matrix and road impedance function OD pairs of the transportation network. Divide all nodes in the transportation network into two sets, S and W, and record nodes for which the minimum road impedance has been determined and nodes for which the minimum road impedance has not been determined.

[0041] (b) Perform initialization. In S, only the starting point v0 is included and d(v0,v0)=0, and in W, all nodes except v0 are included, and the road impedance value from the node in W to the starting point is

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[0042] (c)

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[0043] (d) From W, node v has the smallest road impedance value. k Select node v k Add it to set S, and remove it from W.

[0044] (e) Repeat steps (c) and (d) until all nodes have been traversed, and use the backtracking method to find the optimal path from p to the target node at this stage.

[0045] (f) Travel to the next node following the path determined in step (e), and record the optimal path in real time. Update v0 and proceed to step (a), continuing until the target node is reached.

[0046] Based on the initial hydrogen remaining capacity of the HFCV, the vehicle's initial SOH is generated according to a normal distribution, and the hydrogen remaining capacity of the kth vehicle is updated based on the travel path as follows:

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[0047] There are two types of trigger conditions for hydrogen refueling. (1) Hydrogen remaining

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[0048] When the kth vehicle travels along a route and the hydrogen refueling condition is triggered in a certain road section a, the hydrogen refueling load for that road section is distributed uniformly to the nodes at both ends of that road section, and the hydrogen refueling load is as follows:

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[0049] The acquisition of the EV charging load is similar to that of HFCV, and therefore a detailed explanation is omitted in this invention.

[0050] According to the above flow, the energy refueling load at different nodes in the transportation network can be captured, and candidate nodes for charging stations, hydrogen stations, and hybrid energy refueling stations can be determined based on the magnitude of the energy refueling load at each node. Here, a hybrid energy refueling station includes charging and hydrogen refueling facilities, and nodes with both large charging and hydrogen refueling loads can be selected as candidate nodes for hybrid energy refueling stations.

[0051] In this embodiment, the goal is to minimize the total annual system cost, including investment and construction costs, operation and maintenance costs, and carbon emission costs for charging stations, hydrogen stations, and hybrid energy refueling stations, as follows: minF=C cs +C hrs +C hcs +C ce In the formula, C cs , C hrs , C hcs , C ce These represent the cost of charging stations, hydrogen stations, hybrid energy refueling stations, and carbon emissions, respectively.

[0052] (1) Charging stand cost

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[0053] (2) Hydrogen station costs

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[0054] (3) Hybrid energy refueling station costs

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[0055] (4) Carbon emission costs

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[0056] Constraints (1) Tidal flow constraints When an energy charging stand is connected to the power grid as a load, it inevitably affects the power flow in the grid, causing changes in the node voltage and branch current of the grid. In this embodiment, the voltage and current of the power grid are determined by the second cone relaxation of the radial power grid AC power flow model.

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[0057] (2) Electrical Energy Storage (ESS) constraints

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[0058] (3) Hydrogen production / hydrogen storage constraints Hydrogen stations and hybrid energy refueling stations involve hydrogen production, storage, and utilization. Hydrogen is produced using electrolytic cells, and electrical energy is obtained from the power grid. HFCVs obtain hydrogen energy from hydrogen stations through refueling, thereby achieving energy conversion and transmission.

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[0059]

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[0060] (4) Carbon Emission Flow Model

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[0061] Most energy refueling stations employ only a single-stage planning method when conducting long-term planning. This means that in long-term plans with a total planning cycle of 10 years or more, the facility capacity of the energy refueling station is determined all at once, resulting in overcapacity and upfront investment in the early stages of planning. Furthermore, some energy refueling station plans do not take into account factors such as the increase in vehicles, and determine the planned capacity based only on existing data, leading to equipment shortages and problems such as a decline in service quality in the later stages of the energy refueling station's operation. Therefore, the planning of energy refueling stations needs to be closely linked to the development course of EVs and FCEVs. A multi-stage planning method that considers the construction sequence can meet the charging requirements of users at different stages, and a schematic diagram of this method is shown in Figure 1.

[0062] Data Envelopment Analysis (DEA) is an economic method for evaluating organizational performance. It can be used to assess organizational efficiency and compare the efficiencies of different organizations. DEA is an efficiency evaluation method based on a multivariate linear model. It evaluates organizational efficiency by comparing the inputs and outputs of an organization, and its advantage is that it can evaluate organizational efficiency without being affected by the size and structure of the organization.

[0063] The DEA method is a multidimensional evaluation method that includes multiple input variables and multiple output variables, and its evaluation flow is shown in Figure 2. In the figure, Y1 is the number of plan plans in Stage 1, Y2 is the number of plan plans derived in Stage 2 based on plan plan 1 in Stage 1, and Y3 is the number of plan plans derived in Stage 3 based on plan plan 1-1 in Stage 2.

[0064] In the evaluation process at different stages, this embodiment obtains different plan designs for the same stage by changing the number of different types of energy stands and their corresponding candidate nodes. A plan evaluation based on data envelope analysis is performed on the obtained different plan designs for the same stage to obtain the optimal construction plan for that stage. The next stage of planning is then carried out based on the obtained optimal construction plan, and this process continues until the multi-stage planning is completed, obtaining the final plan for each stage and completing the evaluation of each stage.

[0065] The multi-input, multi-output DEA method can solve the problem of selecting a suitable plan for each stage in a multi-stage programming problem. In this embodiment, in order to improve the accuracy of the evaluation, the target function of the plan is normalized, and then each stage is evaluated using the ultra-efficient DEA method, and its mathematical model is as follows.

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[0066] In this embodiment, the output variables are the costs of charging stations, hydrogen stations, hybrid energy refueling stations, and carbon emissions, while the input variables are the number of charging stations, hydrogen stations, and hybrid energy refueling stations to be constructed and the candidate nodes corresponding to each energy station. By changing the number of energy refueling stations to be constructed and the candidate nodes, different planning plans can be obtained. Each different planning plan at each stage is evaluated using the DEA method to obtain the optimal construction plan for that stage. Furthermore, the next stage of planning is carried out based on this plan, and this process continues until the multi-stage planning is completed, resulting in the final planning plan for each stage.

[0067] In this embodiment, considering the mobility needs of different types of energy vehicles, a multi-stage planning of various energy stations is carried out with the goal of minimizing total costs, thereby realizing a plan for a mixed energy station while effectively avoiding challenges such as overcapacity and upfront investment in the early stages of planning, and undercapacity and reduced service quality in the later stages of planning.

[0068] In this embodiment, a mixed traffic flow model is constructed to capture the energy refueling loads of electric vehicles and hydrogen fuel cell vehicles in the same traffic network. Compared to a traffic flow model that considers only one type of vehicle, this model better fits the mobility needs of different types of energy vehicles in real life. By planning hybrid energy refueling stations based on the plans for charging stations and hydrogen stations, the land utilization rate in areas with high vehicle traffic is improved, the land costs for energy refueling station construction are reduced, and the investment costs for the same equipment at two types of energy refueling stations are reduced. Furthermore, problems such as over-equipment and upfront investment in the early stages of planning, and under-equipment and reduced service quality in the later stages of planning, can be effectively avoided.

[0069] Example 2 Embodiment 2 of the present invention introduces a multi-stage planning system for various types of energy stations that takes mixed traffic flow into consideration.

[0070] An acquisition module configured to construct a traffic topology, consider a time-varying road impedance function model, and acquire traffic conditions for different types of energy vehicles in the constructed traffic topology, An initial planning module is configured to plan energy refueling routes for various different types of energy vehicles, taking into account the mobility needs of different types of energy vehicles and based on acquired traffic conditions. A modeling module configured to construct a mixed traffic flow model with the goal of minimizing total cost based on the energy refueling paths of various different types of energy vehicles obtained, A multi-stage planning module configured to complete multi-stage planning for various types of energy stations based on a constructed mixed traffic flow model, This is a multi-stage planning system for various types of energy stations that takes into account mixed traffic flow.

[0071] The detailed steps are the same as those for the multi-stage planning method for various energy stations considering mixed traffic flow provided in Example 1, and a detailed explanation is omitted here.

[0072] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0073] A computer-readable storage medium on which a program is stored, and when the program is executed by a processor, the steps of the multi-stage planning method for various types of energy stations considering mixed traffic flow described in Embodiment 1 of the present invention are realized.

[0074] The detailed steps are the same as those for the multi-stage planning method for various energy stations considering mixed traffic flow provided in Example 1, and a detailed explanation is omitted here.

[0075] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0076] The electronic device includes memory, a processor, and a program stored in the memory and executed on the processor, wherein when the processor executes the program, the steps of the multi-stage planning method for various types of energy stands considering mixed traffic flow described in Embodiment 1 of the present invention are realized.

[0077] The detailed steps are the same as those for the multi-stage planning method for various energy stations considering mixed traffic flow provided in Example 1, and a detailed explanation is omitted here.

[0078] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0079] The present invention is a computer program product that includes software code, the program in which executes the steps of the multi-stage planning method for various types of energy stations considering mixed traffic flow as described in Embodiment 1 of the present invention.

[0080] The detailed steps are the same as those for the multi-stage planning method for various energy stations considering mixed traffic flow provided in Example 1, and a detailed explanation is omitted here.

[0081] The above description is merely a preferred embodiment of this embodiment and is not intended to limit it. Those skilled in the art will know that this embodiment can be modified and altered in various ways. Any modifications, equivalent replacements, improvements, etc., made without departing from the spirit and principles of this embodiment are all covered within the scope of this embodiment.

Claims

1. A step of constructing a traffic topology using free passage time and traffic capacity representing information on the length and width of the road sections, where intersections in the network are defined as nodes and road sections between nodes are defined as arcs, The steps include: using a road impedance function model provided by the U.S. National Highway Administration (BPR), calculating the actual travel time that changes over time based on the vehicle flow rate and traffic capacity of the road section, thereby obtaining the travel time for each different energy vehicle, including electric vehicles and hydrogen fuel cell vehicles, which have different energy consumption characteristics in the constructed traffic topology; Based on a comparison of the vehicle's remaining energy state and the distance to the destination, the process involves planning energy refueling routes for various different types of energy vehicles according to the acquired travel time. The process involves planning energy refueling routes based on an improved Dijksträ algorithm, and, when an energy refueling condition is triggered in a road section, performing calculations to allocate the energy refueling load of that road section to the nodes at both ends of that road section, thereby capturing the energy refueling load of different nodes in the transportation network, and providing candidate nodes for planning charging stations, hydrogen stations, and hybrid energy refueling stations based on the magnitude of the energy refueling load of the nodes. The process involves capturing the energy refueling load at different nodes in the transportation network, determining candidate nodes for hybrid energy refueling stations, including charging stations, hydrogen stations, and charging and hydrogen refueling facilities, based on the magnitude of the energy refueling load at each node, selecting nodes with both high charging and hydrogen refueling loads as candidate nodes for hybrid energy refueling stations, and constructing a mixed traffic flow model based on the resulting energy refueling routes for various different types of energy vehicles, with the goal of minimizing total costs. This includes the step of completing a multi-stage plan for various types of energy stands based on a constructed mixed traffic flow model, The target function of the constructed mixed traffic flow model includes at least charging station costs, hydrogen station costs, hybrid energy refueling station costs, and carbon emission costs, and the constraints on the target function include at least tidal flow constraints, electric energy storage constraints, hydrogen production and storage constraints, and carbon emission flow constraints. The target function of the constructed mixed traffic flow model is as follows: minF=Ccs+Chrs+Chcs+Cce In the formula, Ccs, Chrs, Chcs, and Cce represent the cost of charging stations, hydrogen stations, hybrid energy refueling stations, and carbon emissions, respectively. In the multi-stage planning process for various types of energy stands, the target function of the mixed traffic flow model is normalized, a multi-stage planning method based on data envelope analysis is used, and different stages are evaluated for the normalized target function. In each stage, where the total planning cycle is divided into multiple periods, multiple energy stand planning plans are obtained, each with a different number of energy stands to be constructed and a different combination of corresponding candidate nodes. A computer-based multi-stage planning method for multiple types of energy stations that consider mixed traffic flow, characterized by obtaining different plan designs at the same stage by changing the number of different types of energy stations and their corresponding candidate nodes in the evaluation process at different stages, calculating evaluation values ​​for the obtained different plan designs at the same stage by data envelope analysis to obtain an optimal construction plan that satisfies the energy refueling needs at the lowest cost at that stage, planning for the next stage based on the obtained optimal construction plan, and continuing until the multi-stage planning is completed, obtaining the final plan design for each stage, and completing the evaluation of each stage.

2. A multi-stage planning method for multiple types of energy stations that take mixed traffic flow into consideration, according to claim 1, characterized in that, in the process of planning energy refueling routes for various different types of energy vehicles, acquired vehicle traffic conditions are incorporated and vehicle movement routes are planned using Dijkstra's algorithm.

3. The multi-stage planning method for various types of energy stations considering mixed traffic flow according to Claim 2, characterized in that the process of planning a vehicle travel route using Dijkstra's algorithm is to obtain the real-time road impedance function and departure and destination nodes of the vehicle based on the adjacency matrix and road impedance function of the traffic topology network, calculate the optimal route from all the vehicle nodes to the target node according to the obtained departure and destination nodes, and complete the planning of the vehicle travel route.

4. An acquisition module configured to acquire the travel time for each of different energy vehicles, including electric vehicles and hydrogen fuel cell vehicles, which have different energy consumption characteristics in the constructed traffic topology, by defining intersections in the network as nodes and road sections between nodes as arcs, constructing a traffic topology using free passage time and traffic capacity representing information on the length and width of the road sections, and using a road impedance function model provided by the U.S. National Highway Administration (BPR) to calculate the actual travel time that changes over time based on the vehicle flow rate and traffic capacity of the road sections, and thereby acquiring the travel time for each of different energy vehicles, including electric vehicles and hydrogen fuel cell vehicles, which have different energy consumption characteristics in the constructed traffic topology, An initial planning module configured to plan energy refueling routes for various different types of energy vehicles, based on a comparison of the vehicle's remaining energy state and the distance traveled to the destination, according to the acquired travel time, Based on an improved Dijksträ algorithm, the system plans energy refueling routes and, when an energy refueling condition is triggered in a road section, calculates the energy refueling load of that road section and assigns it to the nodes at both ends of that road section. This captures the energy refueling load of different nodes in the transportation network and, based on the magnitude of the energy refueling load at those nodes, provides candidate nodes for planning charging stations, hydrogen stations, and hybrid energy refueling stations. An initial planning module that captures the energy refueling load at different nodes in the transportation network, determines candidate nodes for hybrid energy refueling stations including charging stations, hydrogen stations, and charging and hydrogen refueling facilities based on the magnitude of the energy refueling load at each node, and selects nodes with both large charging and hydrogen refueling loads as candidate nodes for hybrid energy refueling stations. A modeling module configured to construct a mixed traffic flow model with the goal of minimizing total cost based on the energy refueling paths of various different types of energy vehicles obtained, It includes a multi-stage planning module configured to complete multi-stage planning for various types of energy stands based on a constructed mixed traffic flow model, The target function of the constructed mixed traffic flow model includes at least charging station costs, hydrogen station costs, hybrid energy refueling station costs, and carbon emission costs, and the constraints on the target function include at least tidal flow constraints, electric energy storage constraints, hydrogen production and storage constraints, and carbon emission flow constraints. The target function of the constructed mixed traffic flow model is as follows: minF=Ccs+Chrs+Chcs+Cce In the formula, Ccs, Chrs, Chcs, and Cce represent the cost of charging stations, hydrogen stations, hybrid energy refueling stations, and carbon emissions, respectively. In the multi-stage planning process for various types of energy stands, the target function of the mixed traffic flow model is normalized, a multi-stage planning method based on data envelope analysis is used, and different stages are evaluated for the normalized target function. In each stage, where the total planning cycle is divided into multiple periods, multiple energy stand planning plans are obtained, each with a different number of energy stands to be constructed and a different combination of corresponding candidate nodes. A multi-stage planning system for various types of energy stations that takes mixed traffic flow into consideration, characterized by obtaining different plan designs at the same stage by changing the number of different types of energy stations and their corresponding candidate nodes in different stages of the evaluation process, calculating evaluation values ​​for the different plan designs obtained at the same stage by data envelope analysis to obtain an optimal construction plan that satisfies the energy refueling needs at the lowest cost at that stage, planning for the next stage based on the obtained optimal construction plan, continuing until the multi-stage planning is completed, obtaining the final plan for each stage, and completing the evaluation of each stage.

5. A computer-readable storage medium, characterized in that it stores a computer program, and when the program is executed by a processor, steps of a multi-stage planning method for various types of energy stations considering mixed traffic flow as described in any one of claims 1 to 3 are realized.

6. Electronic device comprising memory, a processor, and a computer program stored in the memory and executed on the processor, wherein when the processor executes the program, steps of a multi-stage planning method for various types of energy stations considering mixed traffic flow as described in any one of claims 1 to 3 are realized.

7. A computer program product including software code, wherein the program in the software code performs steps of a multi-stage planning method for various types of energy stations considering mixed traffic flow as described in any one of claims 1 to 3.

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