Multi-type energy station multi-stage planning method and system considering mixed traffic flow

Abstract

The present invention belongs to the technical field of energy station planning, and specifically relates to a multi-stage planning method and system for multi-type energy stations taking into account mixed traffic flows; wherein, the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows includes: constructing a traffic topology; considering a time-varying road resistance function model, obtaining the traffic conditions of vehicles with different types of energy in the traffic topology; considering the travel needs of different types of energy vehicles, respectively planning the charging paths of various types of energy vehicles; based on the obtained charging paths of various types of energy vehicles, constructing a mixed traffic flow model with the goal of minimizing the total cost, and completing the multi-stage planning of multi-type energy stations. Considering the travel needs of different types of energy vehicles, multi-stage planning of multi-type energy stations is carried out with the goal of minimizing the total cost. While realizing the planning of mixed energy stations, it effectively avoids the problems of equipment redundancy and advanced investment in the early planning stage and insufficient equipment and reduced service quality in the later planning stage.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy station planning, and in particular relates to a multi-stage planning method and system for multi-type energy stations taking mixed traffic flows into consideration. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As a green means of transportation, new energy vehicles have great advantages and potential in reducing carbon dioxide emissions and alleviating the energy crisis. With the continuous development of new energy vehicles, the demand for vehicle charging is also growing, and reasonable charging station planning is the key to meeting user needs, improving charging efficiency, reducing urban land pressure and reducing the burden on the power grid. In the existing charging station planning, only a single type of charging station (such as charging stations, hydrogen stations, etc.) is often planned, while ignoring the travel needs of different types of energy vehicles in daily life, and it is impossible to maximize the diverse transportation needs. At the same time, most of the existing planning strategies are single-stage static planning, which is prone to redundant equipment in the early stage and insufficient equipment in the later stage, making it difficult to meet the rapidly growing vehicle charging needs. Summary of the invention

[0004] To solve the above problems, the present invention proposes a multi-stage planning method and system for multi-type energy stations taking into account mixed traffic flows, taking into account the travel needs of different types of energy vehicles, and conducting multi-stage planning of multi-type energy stations with the goal of minimizing total cost. While realizing the planning of hybrid energy stations, it effectively avoids the problems of equipment redundancy and advance investment in the early stage of planning and insufficient equipment and reduced service quality in the later stage of planning.

[0005] According to some embodiments, a first solution of the present invention provides a multi-stage planning method for multi-type energy stations taking into account mixed traffic flows, using the following technical solutions:

[0006] A multi-stage planning method for multi-type energy stations taking into account mixed traffic flows, comprising:

[0007] Constructing traffic topology;

[0008] Considering the time-varying road resistance function model, the traffic conditions of vehicles with different types of energy in the constructed traffic topology are obtained;

[0009] Considering the travel needs of different types of energy vehicles, the charging routes of different types of energy vehicles are planned according to the acquired traffic conditions;

[0010] Based on the obtained charging paths of various types of energy vehicles, a mixed traffic flow model is constructed with the goal of minimizing the total cost;

[0011] Based on the constructed mixed traffic flow model, multi-stage planning of multiple types of energy stations is completed.

[0012] As a further technical limitation, in the process of planning charging paths for various types of energy vehicles, the acquired vehicle traffic conditions are integrated and the Dijkstra algorithm is used to plan vehicle travel paths.

[0013] Furthermore, the process of planning the vehicle travel path using the Dijkstra algorithm is as follows: obtaining the real-time road resistance function and start and end nodes of the vehicle based on the adjacency matrix and road resistance function of the traffic topology network; based on the obtained start and end nodes, calculating the optimal path from all nodes of the vehicle to the target node to complete the planning of the vehicle travel path.

[0014] As a further technical limitation, in the multi-stage planning process of multi-type energy stations, the objective function of the mixed traffic flow model is normalized, and a multi-stage planning method based on data envelopment analysis is used to evaluate the normalized objective function at different stages, so as to meet the charging needs of different types of energy vehicles at different stages of the construction sequence and obtain a planning scheme for multi-type energy stations.

[0015] Furthermore, in the process of evaluation at different stages, different planning schemes for the same stage are obtained by changing the number of different types of energy stations and their corresponding candidate nodes; the different planning schemes for the same stage are evaluated based on data envelopment analysis to obtain the optimal construction plan for that stage; the next stage of planning is carried out based on the obtained optimal construction plan until the multi-stage planning is completed, and the final planning scheme for each stage is obtained to complete the evaluation at different stages.

[0016] As a further technical limitation, the objective function of the constructed hybrid traffic flow model includes at least charging station cost, hydrogen refueling station cost, hybrid charging station cost and carbon emission cost; the constraints of the objective function include at least flow constraints, electric energy storage constraints, hydrogen production and storage constraints and carbon emission flow constraints.

[0017] According to some embodiments, a second solution of the present invention provides a multi-type energy station multi-stage planning system taking into account mixed traffic flows, using the following technical solutions:

[0018] A multi-stage planning system for multi-type energy stations taking into account mixed traffic flows, comprising:

[0019] An acquisition module is configured to construct a traffic topology, consider a time-varying road resistance function model, and obtain the traffic conditions of vehicles of different types of energy in the constructed traffic topology;

[0020] A preliminary planning module, which is configured to consider the travel needs of different types of energy vehicles and plan charging paths for different types of energy vehicles according to the acquired traffic conditions;

[0021] A modeling module is configured to construct a mixed traffic flow model with a goal of minimizing total cost according to the obtained charging paths of various types of energy vehicles;

[0022] The multi-stage planning module is configured to complete the multi-stage planning of multiple types of energy stations according to the constructed mixed traffic flow model.

[0023] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0024] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in the first solution of the present invention.

[0025] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0026] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in the first solution of the present invention are implemented.

[0027] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0028] A computer program product includes software codes, wherein the program in the software codes executes the steps in the multi-stage planning method for multiple types of energy stations taking into account mixed traffic flows as described in the first embodiment of the present invention.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention takes into account the travel needs of different types of energy vehicles, and conducts multi-stage planning of multiple types of energy stations with the goal of minimizing total cost. While realizing the planning of hybrid energy stations, it effectively avoids the problems of equipment redundancy and advance investment in the early stage of planning and insufficient equipment and reduced service quality in the later stage of planning.

[0031] The present invention captures the charging load of electric vehicles and hydrogen fuel vehicles in the same traffic network by constructing a mixed traffic flow model. Compared with the traffic flow model that only considers one type of vehicle, it is more in line with the travel needs of different types of energy vehicles in real life. On the basis of planning charging stations and hydrogen refueling stations, the hybrid charging station planning is realized, which can improve the land utilization rate in high-density traffic areas, reduce the land cost of charging station construction, and reduce the investment cost of the same equipment in the two types of charging stations. It can effectively avoid equipment redundancy in the early planning stage, advance investment, insufficient equipment in the later planning stage, and reduced service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0033] Figure 1 This is a schematic diagram of multi-stage planning considering construction sequence in the first embodiment of the present invention;

[0034] Figure 2 This is a flow chart of multi-stage planning based on data envelopment analysis in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0039] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0040] Embodiment 1

[0041] Embodiment 1 of the present invention introduces a multi-stage planning method for multi-type energy stations taking into account mixed traffic flows.

[0042] For the planning of different types of energy stations, this embodiment comprehensively considers mixed traffic flows and plans charging stations and hydrogen refueling stations in a unified manner; specifically, the traffic network topology is modeled, and the time-varying road resistance function is combined to describe the traffic conditions of electric vehicles (EV) and hydrogen fuel cell vehicles (HFCV) in the traffic network; taking HFCV as an example, the charging path of HFCV is planned based on the improved Dijkstra algorithm, and the charging load of different nodes in the traffic network is captured, providing candidate nodes for the planning of charging stations, hydrogen refueling stations and hybrid charging stations; finally, the objective function of multi-type charging station planning is established with the lowest annualized total cost of the system, and a multi-stage planning method based on data envelopment analysis (DEA) is used for phased planning to meet the charging needs of users at different stages. Compared with traditional charging station planning, this embodiment takes into account the travel needs of different types of vehicles during planning, which is more in line with actual life conditions. At the same time, it can effectively avoid problems such as equipment redundancy, advanced investment in the early planning stage, insufficient equipment in the later planning stage, and reduced service quality.

[0043] This embodiment defines nodes in the network as intersections, arcs between nodes as road segments; free travel time is used Current carrying capacity Characterizes the basic information of the road section, such as length and width; among them, free travel time For road section The time it takes for a vehicle to travel in free flow conditions (vehicles can travel "freely" with minimal traffic); carrying capacity For roads without congestion The maximum traffic volume allowed to pass. In the traffic network, OD (Origin-Destination) pairs are often used to represent the starting node and destination node of vehicle travel. The traffic volume between OD pairs represents the total traffic volume on the path with the same starting point and end point, that is:

[0044] ;

[0045] in, is the OD pair matrix, For slave nodes Departure to the node of traffic flow.

[0046] It should be noted that the vehicle flow involved in this embodiment includes EV and HFCV, rather than a single type of vehicle flow.

[0047] Each driving route is connected by different road sections. The traffic flow of each route is accumulated on the road sections it passes through, so the traffic flow of the road section can be expressed as:

[0048] ;

[0049] in, For road section Traffic volume; For OD ( As a starting point, The path below Traffic volume; For OD The set of all connected paths of is a binary number. If OD is Path in Passing section ,but ,on the contrary ; is a collection of road segments in the transportation network.

[0050] In actual vehicle travel, due to the existence of road congestion, the method of measuring travel time by distance alone will result in the calculated travel time being shorter than the actual one, thus failing to accurately obtain the vehicle's travel conditions. Therefore, the present invention adopts the road resistance function model provided by the Bureau of Public Roads (BPR) of the United States, and uses the time-varying travel time to describe the actual time it takes for a vehicle to pass through a certain road section, namely:

[0051] ;

[0052] in, For road section The actual travel time; For road section Free passage time in unobstructed conditions; For road section The traffic capacity; , is the correction function of the model, which is often taken in general urban traffic networks. , .

[0053] The HFCV mileage and hydrogen state (SOH) determine the time and amount of HFCV hydrogen refueling. When a trip ends or begins, the owner determines whether to refuel based on the current SOH and future mileage: when the HFCV's SOH exceeds the current destination's driving requirements, the vehicle continues to drive until the end of the trip; when the SOH is below the threshold or cannot meet the current trip, hydrogen is refueled nearby, and when the amount of hydrogen meets the current destination's mileage requirements, hydrogen refueling is stopped.

[0054] Assuming that the remaining mileage of the HFCV is less than the travel mileage, it is ensured that each HFCV passes through a hydrogen refueling station at least once, and the HFCV has enough fuel to travel to the hydrogen refueling station to complete hydrogenation. SOH satisfaction at all times When , the vehicle performs hydrogen refueling and finds the hydrogen refueling station with the shortest distance at this time; For HFCV SOH at the moment; For the Car The distance between the location at the moment and the destination; Indicates the distance completed SOH required.

[0055] The EV charging logic is similar to the HFCV hydrogen refueling logic, and will not be described in detail in this embodiment.

[0056] This embodiment is based on the Dijkstra algorithm and incorporates real-time traffic information to dynamically plan the shortest travel path for users. That is, every time the user's vehicle travels to a node, the algorithm can automatically adjust the pre-planned path according to the changes in traffic conditions and determine the next target node; specifically:

[0057] (a) Obtain the real-time road resistance function and start and end nodes based on the traffic network adjacency matrix and road resistance function OD pair. Divide all nodes in the road network into two sets and , respectively record the nodes for which the minimum road resistance has been found and the nodes for which the minimum road resistance has not yet been found.

[0058] (b) Initialization. Only the starting point is included and , Contains except Nodes outside of Road resistance value between midpoint and starting point ,in Indicates the road resistance of all roads.

[0059] (c) By calculate The minimum resistance value from the middle node to the starting point, and record the replacement information .

[0060] (d) From Select the node with the smallest resistance , the node Add to collection , and at the same time Move out.

[0061] (e) Repeat steps (c) and (d) until all nodes are traversed, using the backtracking method to find Read the optimal path to the target node at this stage.

[0062] (f) Drive to the next node according to the path found in step (e) and record the real-time optimal path. Update Go to step (a) until you reach the target node.

[0063] The vehicle initial SOH is generated according to the normal distribution of the initial HFCV hydrogen remaining capacity, and the first The remaining hydrogen amount in the vehicle is:

[0064] ;

[0065] in, is the energy consumption coefficient; It is the hydrogen consumption per 100 km of HFCV; for Moment The remaining amount of hydrogen in the vehicle; for Time has come Moment The distance traveled by the vehicle.

[0066] There are two trigger conditions for hydrogenation:

[0067] (1) When the remaining hydrogen is less than When the amount of hydrogen added for ;

[0068] (2) When When the remaining hydrogen capacity of the vehicle cannot support it to reach the destination, the amount of hydrogen refueling for ;

[0069] in, It is the rated capacity of hydrogen storage on board the HFCV; It is the minimum remaining capacity of hydrogen storage on HFCV.

[0070] No. The vehicle is traveling on a certain road section The hydrogenation condition is triggered to evenly distribute the hydrogenation load of the road section to the nodes at both ends of the road section. The hydrogenation load is:

[0071] ;

[0072] in, for Time on the road Triggered hydrogenation load; for At the node Triggered hydrogenation load, where the node For road section One of the nodes at either end, For Node The number of adjacent road segments.

[0073] The acquisition of EV charging load is similar to that of HFCV, and will not be repeated in the present invention.

[0074] According to the above process, the charging load of different nodes in the transportation network can be captured, and the candidate nodes of charging stations, hydrogen refueling stations and hybrid charging stations can be determined based on the size of the node charging load; among them, the hybrid charging station includes charging and hydrogen refueling facilities. When the node charging load and hydrogen refueling load are both large, the node can be selected as a candidate node for the hybrid charging station.

[0075] This embodiment aims to minimize the annualized total cost of the system, including the investment and construction cost, operation and maintenance cost, and carbon emission cost of charging stations, hydrogen refueling stations, and hybrid charging stations, namely:

[0076] ;

[0077] in, , , , They are charging station cost, hydrogen station cost, hybrid charging station cost and carbon emission cost.

[0078] (1) Charging station cost

[0079] ;

[0080] ;

[0081] ;

[0082] in, , They are the investment cost and operating cost of the charging station; is a set of candidate nodes for charging stations; is the annual discount rate for charging stations; The planning period; , Respectively Equipment investment cost and land cost for each charging station; For whether A 0-1 variable for building a charging station; , Respectively The electricity cost and labor operating cost of each charging station.

[0083] (2) Hydrogen refueling station cost

[0084] ;

[0085] ;

[0086] ;

[0087] in, , They are the investment cost and operating cost of a hydrogen refueling station; is a set of candidate nodes for hydrogen refueling stations; is the annual discount rate for hydrogen refueling stations; , Respectively Equipment investment cost and land cost of each hydrogen refueling station; For whether A 0-1 variable for building a hydrogen refueling station; , , Respectively The electricity cost, water cost and labor operation cost of each hydrogen refueling station.

[0088] (3) Hybrid charging station cost

[0089] ;

[0090] ;

[0091] ;

[0092] in, , They are the investment cost and operating cost of the hybrid charging station; A set of candidate nodes for hybrid charging stations; is the annual discount rate of the hybrid charging station; , Respectively Equipment investment cost and land cost of a hybrid charging station; For whether A 0-1 variable for building a hybrid charging station; , , Respectively The electricity cost, water cost and labor operation cost of a hybrid charging station.

[0093] (4) Carbon emission costs

[0094] ;

[0095] in, is a set of distribution network nodes; is the carbon price; For Node of carbon emissions.

[0096] Constraints

[0097] (1) Power flow constraints

[0098] When the charging station is connected to the distribution network as a load, it will inevitably affect the power flow of the distribution network, thereby changing the node voltage and branch current of the distribution network. This embodiment uses the second-order cone relaxation of the radial distribution network AC power flow model to solve the voltage and current of the distribution network:

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] in, ,in For branch exist The current flowing at any moment; , where is the node exist Voltage at the moment; is the terminal node in the distribution network The head-end node set corresponding to the branch; The first node in the distribution network is The set of terminal nodes corresponding to the branches; and They are Branch and Branch Road The active power flowing into the head end at the moment; and They are Branch and Branch Road The reactive power flowing into the head end at the moment; and They are resistance and reactance of the branch circuit; and They are node Equivalent active and reactive power of load at all times; is the two-norm of the vector; and Node The minimum and maximum voltage allowed; and They are The minimum and maximum current allowed in a branch.

[0106] (2) Electric energy storage (ESS) constraints

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] in, ; , , They are Time Node ESS capacity and its upper and lower limits; , , They are Time Node The charging power of the ESS and its upper and lower limits; , , They are Time Node The discharge power of ESS and its upper and lower limits; , Node The charging and discharging efficiency of ESS; , are the charging and discharging states of ESS respectively.

[0113] (3) Hydrogen production / storage constraints

[0114] Hydrogen refueling stations and hybrid charging stations involve hydrogen production, storage and use. They obtain electricity from the power grid through electrolyzers to produce hydrogen, and HFCV obtains hydrogen energy from hydrogen refueling stations by replenishing hydrogen, thereby realizing energy conversion and transmission.

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] in, ; , They are Time Node The power of the electrolyzer and its upper power limit; for Time Node The electrolytic cell is in operation state; for Time Node Compressor power at for Time flow into the node The quality of hydrogen at the compressor; for Time Node Hydrogen production of electrolyzer at is the power consumption of the compressor at the reference pressure; , , They are compressor pressure, standard atmospheric pressure and compressor reference pressure respectively; , They are the hydrogen production efficiency and the hydrogen loss rate from the electrolyzer to the compressor; is the calorific value of hydrogen.

[0120] ;

[0121] ;

[0122] ;

[0123] in, It is the operating time collection of hydrogen storage tank; , They are Time Node HFCV hydrogenation power and its upper limit; for Time Node In HFCV hydrogenation state; for Time Node Hydrogen storage tank capacity; , Node The upper and lower limits of the hydrogen storage tank capacity; They are the hydrogenation efficiency in the hydrogen use link respectively.

[0124] (4) Carbon emission flow model

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] in, , Inflow nodes The branch set and slave node Outflow branch collection; , They are the node carbon potential and the carbon emission intensity of the generating unit; , , They are branch current active power, generator injection power and branch carbon flow density; For Node The total load mean at ; , , , They are Time Node Conventional active load, EV charging power, electrolyzer power and compressor power of hydrogen and hybrid charging stations; , , They are the node active power flux matrix, node carbon potential matrix and branch power flow distribution matrix respectively. , They are the unit injection distribution matrix and the generator carbon emission coefficient matrix respectively.

[0130] Most charging stations only use a single-stage planning method when conducting long-term planning, that is, the facility capacity of the charging station is determined once in the long-term planning with a total planning period of 10 years or more, resulting in equipment redundancy and advanced investment in the early stages of planning. In addition, some charging station plans do not take into account factors such as vehicle growth, and only determine the planned capacity based on existing data, resulting in problems such as insufficient equipment and reduced service quality in the later stages of the charging station. Therefore, the planning of charging stations should closely follow the development process of EV and FCEV. A multi-stage planning method that considers the construction sequence can meet the charging needs of users at different times. The schematic diagram is shown below. Figure 1 shown.

[0131] Data Envelopment Analysis (DEA) is an economic method used to evaluate organizational performance. It can be used to measure the efficiency of an organization and to compare the efficiency of different organizations. The DEA evaluation method is an efficiency evaluation method based on a multivariate linear model. It evaluates organizational efficiency by comparing the input and output of an organization. Its advantage is that it can measure the efficiency of an organization and is not affected by the size and structure of the organization.

[0132] The DEA method is a multidimensional evaluation method that includes multiple input and output variables. Its evaluation process is as follows: Figure 2 As shown; among them, The number of planning options for Phase 1; The number of planning options derived for Phase 2 based on Planning Option 1 in Phase 1; The number of planning schemes derived for Phase 3 based on Planning Scheme 1-1 in Phase 2.

[0133] During the evaluation process at different stages, this embodiment obtains different planning schemes at the same stage by changing the number of different types of energy stations and their corresponding candidate nodes; the different planning schemes at the same stage are evaluated based on data envelopment analysis to obtain the optimal construction plan at that stage; the next stage of planning is carried out based on the obtained optimal construction plan until the multi-stage planning is completed, and the final planning scheme for each stage is obtained to complete the evaluation at different stages.

[0134] The DEA method for multiple inputs and multiple outputs can solve the problem of optimizing the schemes at each stage in the multi-stage planning problem. In order to improve the accuracy of the evaluation, the objective function of the planning needs to be normalized first, and then the evaluation of each stage is performed by the super-efficient DEA method. The mathematical model is:

[0135] ;

[0136] ;

[0137] ;

[0138] in, For planning DEA evaluation value; is the number of output variables; is the number of input variables; For planning No. input variables; , Planning Schemes No. The actual value and normalized value of the output variable; , Respectively The minimum and maximum values ​​of the output variables; , is the correlation coefficient.

[0139] It should be noted that the output variables in this embodiment are charging stations, hydrogen refueling stations, hybrid charging stations and carbon emission costs, and the input variables are the number of charging stations, hydrogen refueling stations and hybrid charging stations built, as well as the candidate nodes corresponding to each energy station; by changing the number of charging stations built and the candidate nodes, different planning schemes can be obtained. In the different planning schemes of each stage, the DEA method is adopted for evaluation to obtain the optimal construction plan for that stage, and then the next stage of planning is carried out based on this plan, and this is carried out until the end of the multi-stage planning, and the final planning scheme for each stage is obtained.

[0140] This embodiment takes into account the travel needs of different types of energy vehicles, and conducts multi-stage planning of multiple types of energy stations with the goal of minimizing total cost. While realizing the planning of hybrid energy stations, it effectively avoids the problems of equipment redundancy and advance investment in the early stage of planning and insufficient equipment and reduced service quality in the later stage of planning.

[0141] This embodiment captures the charging load of electric vehicles and hydrogen fuel vehicles in the same traffic network by constructing a mixed traffic flow model. Compared with the traffic flow model that only considers one type of vehicle, it is more in line with the travel needs of different types of energy vehicles in real life. On the basis of planning charging stations and hydrogen refueling stations, the planning of mixed charging stations is realized, which can improve the land utilization rate in high-density traffic areas, reduce the land cost of charging station construction, and reduce the investment cost of the same equipment in the two types of charging stations. It can effectively avoid equipment redundancy in the early planning stage, advance investment, insufficient equipment in the later planning stage, and reduced service quality.

[0142] Embodiment 2

[0143] Embodiment 2 of the present invention introduces a multi-stage planning system for multiple types of energy stations taking into account mixed traffic flows.

[0144] A multi-stage planning system for multi-type energy stations taking into account mixed traffic flows, comprising:

[0145] An acquisition module is configured to construct a traffic topology, consider a time-varying road resistance function model, and obtain the traffic conditions of vehicles of different types of energy in the constructed traffic topology;

[0146] A preliminary planning module, which is configured to consider the travel needs of different types of energy vehicles and plan charging paths for different types of energy vehicles according to the acquired traffic conditions;

[0147] A modeling module is configured to construct a mixed traffic flow model with a goal of minimizing total cost according to the obtained charging paths of various types of energy vehicles;

[0148] The multi-stage planning module is configured to complete the multi-stage planning of multiple types of energy stations according to the constructed mixed traffic flow model.

[0149] The detailed steps are the same as the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows provided in Example 1, and will not be repeated here.

[0150] Embodiment 3

[0151] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0152] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in Embodiment 1 of the present invention.

[0153] The detailed steps are the same as the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows provided in Example 1, and will not be repeated here.

[0154] Embodiment 4

[0155] A fourth embodiment of the present invention provides an electronic device.

[0156] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in the first embodiment of the present invention are implemented.

[0157] The detailed steps are the same as the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows provided in Example 1, and will not be repeated here.

[0158] Embodiment 5

[0159] Embodiment 5 of the present invention provides a computer program product.

[0160] A computer program product includes software codes, wherein the program in the software codes executes the steps in the multi-stage planning method for multiple types of energy stations taking into account mixed traffic flows as described in the first embodiment of the present invention.

[0161] The detailed steps are the same as the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows provided in Example 1, and will not be repeated here.

[0162] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A multi-stage planning method for multi-type energy stations taking into account mixed traffic flows, characterized in that: include: Constructing traffic topology; Considering the time-varying road resistance function model, the traffic conditions of vehicles with different types of energy in the constructed traffic topology are obtained; Considering the travel needs of different types of energy vehicles, the charging routes of different types of energy vehicles are planned according to the traffic conditions obtained; The charging path is planned based on the improved Dijkstra algorithm, which captures the charging load of different nodes in the transportation network and provides candidate nodes for the planning of charging stations, hydrogen stations and hybrid charging stations. The charging load of different nodes in the traffic network is captured, and candidate nodes for charging stations, hydrogen refueling stations, and hybrid charging stations are determined based on the node charging load. Among them, the hybrid charging station includes charging and hydrogen refueling facilities. When the node charging load and hydrogen refueling load are both large, the node is selected as a candidate node for the hybrid charging station. Based on the charging paths of various types of energy vehicles obtained, a mixed traffic flow model is constructed with the goal of minimizing the total cost. The objective function of the constructed hybrid traffic flow model includes at least the cost of charging stations, the cost of hydrogen refueling stations, the cost of hybrid charging stations and the cost of carbon emission; the constraints of the objective function include at least the power flow constraint, the electric energy storage constraint, the hydrogen production and storage constraint and the carbon emission flow constraint; The objective function of the constructed mixed traffic flow model is: ； in, , , , They are charging station cost, hydrogen station cost, hybrid charging station cost and carbon emission cost; Based on the constructed mixed traffic flow model, the multi-stage planning of multiple types of energy stations is completed; In the multi-stage planning process of multiple types of energy stations, the objective function of the mixed traffic flow model is normalized, and the normalized objective function is evaluated at different stages using a multi-stage planning method based on data envelopment analysis to meet the charging needs of different types of energy vehicles at different stages of the construction sequence, and obtain a planning scheme for multiple types of energy stations; In the process of evaluation at different stages, different planning schemes for the same stage are obtained by changing the number of different types of energy stations and their corresponding candidate nodes; the different planning schemes for the same stage are evaluated based on data envelopment analysis to obtain the optimal construction plan for that stage; the next stage of planning is carried out based on the obtained optimal construction plan until the multi-stage planning is completed, and the final planning scheme for each stage is obtained to complete the evaluation at different stages.

2. A multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in claim 1, characterized in that: In the process of planning charging paths for various types of energy vehicles, the acquired vehicle traffic conditions are integrated and the Dijkstra algorithm is used to plan vehicle travel paths.

3. A multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in claim 2, characterized in that: The process of planning a vehicle travel path using the Dijkstra algorithm is as follows: obtaining the real-time road resistance function and start and end nodes of the vehicle according to the adjacency matrix and road resistance function of the traffic topology network; and calculating the optimal path from all nodes of the vehicle to the target node according to the obtained start and end nodes to complete the planning of the vehicle travel path.

4. A multi-stage planning system for multi-type energy stations taking into account mixed traffic flows, characterized in that: include: An acquisition module is configured to construct a traffic topology, consider a time-varying road resistance function model, and obtain the traffic conditions of vehicles of different types of energy in the constructed traffic topology; A preliminary planning module, which is configured to consider the travel needs of different types of energy vehicles and plan charging paths for different types of energy vehicles according to the acquired traffic conditions; The charging path is planned based on the improved Dijkstra algorithm, which captures the charging load of different nodes in the transportation network and provides candidate nodes for the planning of charging stations, hydrogen stations and hybrid charging stations. Capture the charging load of different nodes in the transportation network, and determine the candidate nodes of charging stations, hydrogen refueling stations and hybrid charging stations based on the node charging load size; among them, the hybrid charging station includes charging and hydrogen refueling facilities. When the node charging load and hydrogen refueling load are both large, the node is selected as the candidate node of the hybrid charging station; A modeling module is configured to construct a mixed traffic flow model with a goal of minimizing total cost according to the obtained charging paths of various types of energy vehicles; The objective function of the constructed hybrid traffic flow model includes at least the cost of charging stations, the cost of hydrogen refueling stations, the cost of hybrid charging stations and the cost of carbon emission; the constraints of the objective function include at least the power flow constraint, the electric energy storage constraint, the hydrogen production and storage constraint and the carbon emission flow constraint; The objective function of the constructed mixed traffic flow model is: ； in, , , , They are charging station cost, hydrogen station cost, hybrid charging station cost and carbon emission cost; A multi-stage planning module, which is configured to complete multi-stage planning of multiple types of energy stations according to the constructed mixed traffic flow model; In the multi-stage planning process of multiple types of energy stations, the objective function of the mixed traffic flow model is normalized, and the normalized objective function is evaluated at different stages using a multi-stage planning method based on data envelopment analysis to meet the charging needs of different types of energy vehicles at different stages of the construction sequence, and obtain a planning scheme for multiple types of energy stations; In the process of evaluation at different stages, different planning schemes for the same stage are obtained by changing the number of different types of energy stations and their corresponding candidate nodes; the different planning schemes for the same stage are evaluated based on data envelopment analysis to obtain the optimal construction plan for that stage; the next stage of planning is carried out based on the obtained optimal construction plan until the multi-stage planning is completed, and the final planning scheme for each stage is obtained to complete the evaluation at different stages.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the multi-stage planning method for multi-type energy stations taking into account mixed traffic flows as described in any one of claims 1 to 3 are implemented.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the multi-stage planning method for multiple types of energy stations taking into account mixed traffic flows are implemented as described in any one of claims 1-3.

7. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the multi-stage planning method for multiple types of energy stations taking into account mixed traffic flows as described in any one of claims 1-3.

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