A highway along charging facility layout planning method

Abstract

The application provides a highway along charging facility layout planning method, and constructs a double-layer optimization model. The project constructs a double-layer optimization model, and optimizes and solves the charging station position and corresponding charging pile quantity. The upper model is a multi-objective layout optimization model of the charging station. The lower model is a dynamic traffic distribution model of multi-user stochastic user equilibrium. The method provided by the application analyzes the charging demand of electric vehicles on the intercity road network, combines the network energy structure, introduces the multi-user path selection behavior, constructs a road network dynamic traffic flow simulation algorithm, realizes the full-day dynamic estimation of the charging demand in the mixed traffic network, reflects the influence of charging cost, charging facility type, position and capacity, electric vehicle proportion and the like on the charging demand distribution, provides quantitative support for adjusting the charging demand distribution and stabilizing the power grid load, and provides decision support for charging facility operation planning.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a method for planning the layout of charging facilities along highways. Background Technology

[0002] With the increasing prominence of climate change and the energy crisis, developing the electrification of vehicles in the transportation sector is of great significance for reducing dependence on fossil fuels, building a clean energy supply system, improving resource allocation capabilities, and achieving dual-carbon goals. The number of electric vehicles on the road is growing rapidly. As of 2021, the number of new energy vehicles in China reached 5.51 million. It is projected that by 2025, the number of new energy vehicles will reach 25 million, accounting for 25% of new car sales.

[0003] With the continuous improvement of electric vehicle (EV) range and charging efficiency, the demand for long-distance intercity travel among EV users is increasing. A well-developed highway charging network is crucial for improving EV accessibility and promoting EV use. On the one hand, the existing highway charging infrastructure falls far short of the growing demand for intercity EV travel. As of the end of 2018, only 1,800 charging stations were operational on highways nationwide. On the other hand, due to a lack of reasonable infrastructure planning, the utilization rate of charging facilities is uneven, with most facilities remaining idle for extended periods, resulting in resource waste. The "2017-2018 China Charging Infrastructure Development Report" indicates that the average utilization rate of highway charging facilities is currently less than 15%. These phenomena have prompted governments and researchers to focus on the resource allocation of highway charging facilities. Summary of the Invention

[0004] The embodiments of the present invention provide a method for planning the layout of charging facilities along highways to solve the following problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution.

[0006] A method for planning the layout of charging facilities along highways, comprising:

[0007] S1 models and calculates the travel route selection behavior of multiple users based on the factors influencing user travel, and obtains the route travel time, route travel energy consumption and the generalized travel cost of multiple users. The factors influencing user travel include the difference in behavior between fuel vehicle and electric vehicle travelers, road congestion status, vehicle energy consumption, highway energy distribution, charging facility layout and charging demand.

[0008] S2 analyzes the charging demand of electric vehicles on the highway network, and combines the highway network topology, the layout of charging facilities along the highway, the highway energy network structure, the driving energy consumption of different types of vehicles and the range anxiety of electric vehicle drivers to construct a spatiotemporal extended network for fuel vehicles and electric vehicles, and depict the service process of fast charging of electric vehicles on the way.

[0009] S3 achieves dynamic information interaction between road network operation status, vehicle driving characteristics and charging facility status by constructing a dynamic traffic flow allocation model that includes the fast charging behavior of electric vehicle users during the journey, a stochastic dynamic user equilibrium model under mixed traffic flow, and a dynamic traffic flow iterative algorithm that integrates charging queuing service simulation, thereby obtaining the charging demand of electric vehicles on highways under time-varying networks.

[0010] S4 constructs a two-layer location optimization model based on path travel time, path travel energy consumption, multi-user path generalized travel cost, the service process of electric vehicle fast charging on the way, and the charging demand of electric vehicles on highways under time-varying networks. By optimizing the layout of fast charging facilities on highways through the two-layer location optimization model, the location and capacity of fast charging facilities along the highway are planned.

[0011] The upper-layer model of the two-layer site selection optimization model is a highway charging facility network optimization model considering reverse construction sequence and green energy structure, while the lower-layer model is a mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions. The mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions is solved by a traffic simulation algorithm based on iterative weighting method to obtain the dynamic charging demand distribution under stochastic user equilibrium conditions, and then transferred to the highway charging facility network optimization model considering reverse construction sequence and green energy structure. The highway charging facility network optimization model considering reverse construction sequence and green energy structure is solved by a multi-objective heuristic algorithm to obtain the planning results of the location and capacity of fast charging facilities along the highway.

[0012] Preferably, the behavioral differences between fuel-powered vehicles and electric vehicles in step S1 include factors considered by fuel-powered vehicle users when choosing routes and factors considered by electric vehicle users when choosing routes; the factors considered by fuel-powered vehicle users when choosing routes include route travel time and route travel energy consumption; the factors considered by electric vehicle users when choosing routes include route travel time, energy structure of charging stations, charging time, and queuing time.

[0013] Step S1 includes:

[0014] S11 Calculation Formula Based on Point Queuing Model

[0015] (1)

[0016] (2)

[0017] Calculate the real-time travel time for each road segment; where, In order to be in t time m Sections of road for vehicles of this type a x The travel time is determined by the road segment. a x Free-flow travel time and road section delay time Together they form; for t Time and Section a x The queue length.

[0018] S12 calculates the real-time average speed of each road segment based on the highway network topology and the real-time travel time of each segment, and then uses the formula...

[0019] (3)

[0020] The driving energy consumption of gasoline-powered vehicles and electric vehicles is modeled and calculated to obtain their real-time energy consumption; where, for t time m Sections of road for vehicles of this type a x Energy consumption during driving; and Each is a road segment a x The average speed and length;

[0021] S13, based on factors influencing user travel, combines real-time travel time for each road segment, real-time energy consumption of gasoline and electric vehicles, and real-time operating conditions of charging infrastructure, and calculates the formula using the PSL model.

[0022] (4)

[0023] Calculate the real-time generalized travel costs for users of gasoline-powered and electric vehicles; where, and They are respectively m Type of vehicle via OD ( r,s The time value of driving and the time value of charging services; For charging stations c i Energy prices; a m for m Equivalent car coefficient for vehicle class; For electric vehicles At the charging station c i The energy replenishment demand; for t Always ready to set off m Sections of road for vehicles of this type a x Travel time; and Electric vehicles At the charging station c i Queuing time and charging time; To determine the charging station c i Is it in OD pair ( r,s Inter-path k The 0-1 variable on the charging station c i In the path k hour, ,otherwise .

[0024] Preferably, S2 includes:

[0025] S21, based on the shortest path obtained using Dijkstra's algorithm for any OD pair, and all paths obtained through depth-first search of OD pairs, takes the highway topology as input and uses the formula...

[0026] (5)

[0027] The effective path set for gasoline-powered vehicles is calculated; where, For OD Between, Path Length; For OD The shortest path length between; This is the magnification factor;

[0028] S22 takes the layout of charging facilities along the highway topology, the highway energy network structure and the electric vehicle driving energy consumption model as inputs to obtain a set of travel routes under hybrid energy that can meet the range anxiety of electric vehicle users.

[0029] Range anxiety for electric vehicle users stems from battery constraints, including battery constraints during the journey and at the destination.

[0030] (6)

[0031] Obtain; where, , and Electric vehicles The vehicle's SOC (State of Charge) is measured from the starting point to the charging station, from charging station 1 to charging station 2, and when leaving the highway terminus. S 1 and S 2 These represent the minimum SOC required for electric vehicles while driving and when leaving the road network, respectively. To determine the path k Select charging station c i Is it feasible to use 0-1 variables when the path is... k choose c i When charging is feasible, ,otherwise ;

[0032] S23 Based on queuing theory, a fast charging behavior model of electric vehicles during driving is constructed to simulate the dynamic process of electric vehicles arriving at charging stations, queuing, receiving services, and leaving charging stations during dynamic traffic flow.

[0033] The queuing simulation process is based on the waiting system principle and the first-come, first-served principle. Arriving users choose the charging station with the shortest remaining service time at the current moment to queue for charging. The charging formula is as follows.

[0034] (7)

[0035] As shown; in the formula, For electric vehicles At the charging station Required charging time, Total battery capacity ( ), For charging stations Charging energy consumption factor For electric vehicles At the charging station Vehicle SOC at that time.

[0036] Preferably, step S3 includes:

[0037] S31 Through-type

[0038] (8)

[0039] (9)

[0040] Construct a dynamic traffic flow assignment model that incorporates the fast-charging behavior of electric vehicle users during their journey, and a stochastic dynamic user equilibrium model under mixed traffic flow; where, for m Type of vehicle tChoose the path at any time k The probability of; The coefficient of variation reflects the discrepancy in perceived differences among users; for t Time Path k of m Broadly defined travel costs for similar vehicles; For OD pair ( r,s Inter-path k Correction terms; and Each is a road segment a x and path k Length; To determine the path k Does it include road sections? a x 0-1 variables, Representing a path k Including road sections ,otherwise ;

[0041] S32 Through-type

[0042] (10)

[0043] A dynamic traffic flow iterative algorithm integrating charging queuing service simulation is constructed; where, for t Time OD pair ( r,s )between m The number of vehicles loaded for this type of vehicle. for t Time OD pair ( r,s )between m Vehicle route selection k The number of vehicles loaded;

[0044] S33 uses a link transmission mechanism to simulate the transmission process of vehicles entering, traveling, and leaving road segments in the road network, obtaining the charging demand distribution that satisfies the equilibrium state of random users at different time periods; specifically, it includes any one of the following three cases:

[0045] When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is less than the remaining time in the current time period and the vehicle is not on the last road segment of the route, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. Vehicle location update is when the vehicle enters the next road segment or leaves the charging station; vehicle time update is when the remaining time in the current time period is updated to the difference between the remaining time in the current time period and the travel time in the current time period, and the travel time of the current road segment is updated to the travel time of the next road segment; electric vehicle SOC update includes: if the vehicle is in a driving state, the current SOC of the electric vehicle is updated to the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; if the vehicle is in a charging state, the current SOC of the electric vehicle is updated to 100% battery level.

[0046] When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is greater than the remaining time in the current time period, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. Vehicle location update indicates that the vehicle is still in the current road segment or charging station; vehicle time update indicates that the remaining time in the current time period is updated to zero; travel time of the current road segment is updated to the difference between the travel time of the current road segment and the remaining time in the current time period; electric vehicle SOC update includes: if the vehicle is in a driving state, the current SOC of the electric vehicle is updated to the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; if the vehicle is in a charging state, the current SOC of the electric vehicle is updated to the SOC of the previous moment plus the charging amount in the current time period.

[0047] When a vehicle is located on the last segment of the route and the travel time of the segment is less than or equal to the remaining time in the current time period, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. Vehicle location update is when the vehicle leaves the road network; vehicle time update is when both the remaining time in the current time period and the travel time of the segment are updated to zero; electric vehicle SOC update includes: the current electric vehicle SOC is updated to the SOC at the end of the previous segment minus the driving energy consumption of the current segment.

[0048] Preferably, the solution process for the lower-level model of the two-level location optimization model includes:

[0049] Through the objective function

[0050] (11)

[0051] (12)

[0052] (13)

[0053] (14)

[0054] (15)

[0055] The optimization calculation aims to minimize the sum of the annual average construction cost, grid connection cost, and operation and maintenance cost of fast charging facilities; where, , , They represent During the phase The proposed plan includes the construction cost of charging facilities, grid connection cost, and operation and maintenance cost. This is the capital recovery factor; The interest rate is 6.8%. The capital recovery period; and They represent During the phase Under the proposed plan, the decision is whether to build a station and the size of the facilities. for During the phase, determine the service area exist The plan specifies a 0-1 variable for whether to build a website; if a website is built, then... ,otherwise ; for During the phase, service area exist The number of charging stations to be built under the plan; for During the phase, charging stations The transformer capacity; The construction cost per kilometer of a 10kV overhead line is 200,100 yuan / km. For the site Distance to the substation (1 km); A route adjustment factor of 0.2 is used to avoid redundant investment in the line. The electricity price is 0.76 yuan / kWh; The average load of the charging station's power distribution equipment is 15kW. for During the phase, charging stations Total working hours within the year; The average annual labor cost per charging station is 12,000 yuan. The average annual management cost per unit transformer capacity is 59.2 yuan / kVA; , for During the phase, Electric vehicle traffic allocation results under the scheme, charging stations Total working hours within the year;

[0056] Through the objective function

[0057] (16)

[0058] (17)

[0059] (18)

[0060] Construct an optimized calculation based on the proportion of green electricity in the charging infrastructure network;

[0061] S33 Based on the calculation results of sub-step S32, the NSGA-II algorithm is used to calculate the planning results of the location and capacity of fast charging facilities along the highway; the calculation process has the following constraints:

[0062] Reverse construction constraints

[0063] (19)

[0064] (20);

[0065] Constraints on the average charging service time of electric vehicle users at charging stations

[0066] (twenty one),

[0067] In the formula, for w Phased Plan Below, charging station i Average charging service time for vehicles within the vehicle. for w Under this phase, the vehicle charging service time threshold is defined. Network reachability constraints are proposed to ensure the connectivity of the charging infrastructure network.

[0068] A fully charged electric vehicle can meet the energy consumption constraints between any two points.

[0069] (twenty two)

[0070] (twenty three);

[0071] In the formula, for During this phase, a 0-1 variable is used to determine whether a service area has been established; if so, then... ,otherwise ; for During the phase, service area The number of internal charging stations built; and These are the minimum and maximum number of charging piles to be built, respectively. for During the phase, charging stations Average waiting time for vehicles inside the vehicle; The threshold for vehicle waiting time within the station; To determine the path Select charging station Whether a path is reachable is a 0-1 variable, when the path is reachable. choose When charging is available, ,otherwise .

[0072] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention provides a method for planning the layout of charging facilities along highways, and constructs a two-layer optimization model. The project constructs a two-layer optimization model to optimize the location of charging stations and the corresponding number of charging piles. The upper-layer model is a multi-objective layout optimization model for charging stations. The lower-layer model is a dynamic traffic allocation model for random user equilibrium among multiple users. The method provided by the present invention has the following advantages: By analyzing the charging demand of electric vehicles in intercity road networks and combining it with the network energy structure, a dynamic traffic flow simulation algorithm for road networks is constructed by introducing multi-user path selection behavior. While realizing the dynamic estimation of charging demand throughout the day in a mixed traffic network, it can also reflect the impact of charging costs, charging facility type, location and capacity, and the proportion of electric vehicles on the distribution of charging demand, providing quantitative support for adjusting the distribution of charging demand and stabilizing the grid load, and providing decision support for the operation planning of charging facilities; facing the intercity travel demand of electric vehicles, considering the energy supply structure of the traffic network and the dynamic charging demand of users, a two-layer optimization model for the deployment of fast charging facilities on highways is proposed. The model takes into account the dynamic changes in the proportion of EVs and the coverage area of ​​green energy at different development stages, and proposes a dynamic optimization scheme for the allocation of charging facility resources, providing decision support for the orderly planning of charging facilities.

[0073] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 A flowchart illustrating a method for planning the layout of charging facilities along a highway, provided by this invention.

[0076] Figure 2 A flowchart illustrating the processing of a dynamic traffic flow model for a highway charging facility layout planning method provided by this invention.

[0077] Figure 3 A schematic diagram of a multi-user spatiotemporal extension network for a highway charging facility layout planning method provided by the present invention;

[0078] Figure 4 A flowchart of an improved genetic algorithm for a highway charging facility layout planning method provided by the present invention;

[0079] Figure 5 This is a schematic diagram of a two-layer optimization model for a highway charging facility layout planning method provided by the present invention. Detailed Implementation

[0080] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0081] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0082] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0083] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0084] This invention provides a method for planning the layout of charging facilities along highways, which addresses the following problems existing in the prior art:

[0085] Charging demand is a crucial input for charging infrastructure deployment. Considering drivers tend to choose chargers closer to their origin or destination, one approach is to assume fixed point-based charging demand, with charging activity occurring only in the origin or destination area. However, since the driving range of electric vehicles (EVs) is much greater than their travel distance, this simplified approach is only suitable for short-distance urban travel. For medium- to long-distance intercity travel, where the travel distance is close to or even exceeds the EV's driving range, EVs require at least one charging trip while traveling on highways. Furthermore, due to the lack of consideration for road network structure and vehicle travel behavior, this point-based demand approach is unsuitable for studying highway charging demand.

[0086] Another approach treats charging demand as a flow demand. Researchers use traffic flow assignment models to obtain the charging demand during electric vehicle (EV) trips. Based on the traffic distribution of EVs within the road network, the charging demand along their routes is estimated. However, most layout planning studies use static traffic assignment models, calculating the allocation based on hourly or daily travel demand matrices. Current traffic flow-based charging facility layout planning neglects time-varying travel demand characteristics and drivers' route selection behavior.

[0087] In reality, time-varying characteristics of travel demand and changing characteristics of driver choice behavior are prevalent in transportation systems. Previous studies on driver choice behavior have shown significant differences in travel demand across different time periods on highway networks. Electric vehicle drivers' route selection behavior often depends on the origin-destination (OD) of their trip, the location of charging facilities, the status of charging facilities, and the energy mix. The status of charging facilities exhibits strong temporal variability. Therefore, if these two dynamic characteristics are not considered in charging facility layout models, charging demand cannot be accurately estimated, significantly impacting the reliability of layout planning results.

[0088] Therefore, in order to promote the clean energy use of transportation, we should start from the perspective of the "source attributes" of transportation and construct a layout model of charging facilities along highways that comprehensively considers the use of green electricity in the transportation energy system, so as to increase the proportion of green energy in highway transportation electricity consumption and thus achieve energy conservation and emission reduction in highway transportation.

[0089] See Figure 1 and 2This invention provides a method for planning the layout of charging facilities along highways, comprising the following steps:

[0090] S1 models and calculates the travel route selection behavior of multiple users based on the factors influencing user travel, and obtains the route travel time, route travel energy consumption and the generalized travel cost of multiple users. The factors influencing user travel include the difference in behavior between fuel vehicle and electric vehicle travelers, road congestion status, vehicle energy consumption, highway energy distribution, charging facility layout and charging demand.

[0091] S2 analyzes the charging demand of electric vehicles on the highway network, and combines the highway network topology, the layout of charging facilities along the highway, the highway energy network structure, the driving energy consumption of different types of vehicles and the range anxiety of electric vehicle drivers to construct a spatiotemporal extended network for fuel vehicles and electric vehicles, and depict the service process of fast charging of electric vehicles on the way.

[0092] S3 achieves dynamic information interaction between road network operation status, vehicle driving characteristics and charging facility status by constructing a dynamic traffic flow allocation model that includes the fast charging behavior of electric vehicle users during the journey, a stochastic dynamic user equilibrium model under mixed traffic flow, and a dynamic traffic flow iterative algorithm that integrates charging queuing service simulation, thereby obtaining the charging demand of electric vehicles on highways under time-varying networks.

[0093] S4 constructs a two-layer location optimization model based on path travel time, path travel energy consumption, multi-user path generalized travel cost, the service process of electric vehicle fast charging on the way, and the charging demand of electric vehicles on highways under time-varying networks. By optimizing the layout of fast charging facilities on highways through the two-layer location optimization model, the location and capacity of fast charging facilities along the highway are planned.

[0094] The upper-layer model of the two-layer site selection optimization model is a highway charging facility network optimization model considering reverse construction sequence and green energy structure, while the lower-layer model is a mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions. The mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions is solved by a traffic simulation algorithm based on iterative weighting method to obtain the dynamic charging demand distribution under stochastic user equilibrium conditions, and then transferred to the highway charging facility network optimization model considering reverse construction sequence and green energy structure. The highway charging facility network optimization model considering reverse construction sequence and green energy structure is solved by a multi-objective heuristic algorithm (such as the NSGA-II algorithm) to obtain the planning results of the location and capacity of fast charging facilities along the highway.

[0095] In the preferred embodiment provided by the present invention, the specific execution process of each step is as follows.

[0096] in, Figure 5Here is the flowchart for the proposed two-level programming process. Figure 2 This is a detailed flowchart of the lower-level model. It includes the following steps:

[0097] 1. The PSL model is used to model the travel route selection behavior of multiple users.

[0098] 1.1 Investigation of Factors Influencing Multi-User Path Selection Behavior

[0099] Using the SP survey method, we conducted a survey on the route selection behavior of users of gasoline-powered vehicles and electric vehicles, and identified the factors influencing users' route selection through modeling.

[0100] Factors influencing the route choices of gasoline-powered vehicle users include: driving time and energy consumption.

[0101] Factors influencing electric vehicle users' route choices include: driving time, energy consumption, energy prices, charging time, and queuing time.

[0102] 1.2 Calculation of route travel time

[0103] To simulate the queuing and congestion of vehicles in the highway network, the real-time travel time of the path is calculated based on the point queuing model, as shown in equations (1)-(2).

[0104] (1)

[0105] (2)

[0106] in, In order to be in t time m Sections of road for vehicles of this type The travel time is determined by the road segment. a x Free-flow travel time and road section delay time Together they form; for t Time and Section a x The queue length. for t time m Type of vehicles on the road section a x Travel time.

[0107] 1.3 Calculation of energy consumption for route travel

[0108] First, the real-time average speed of each road segment is calculated based on the travel time obtained from the road network topology. Next, the energy consumption of fuel-powered vehicles and electric vehicles is modeled and calculated, as shown in Equation (3). Finally, the real-time energy consumption of multiple users along each path in all OD pairs is obtained by summing the energy consumption of each road segment.

[0109] (3)

[0110] in, for t time m Sections of road for vehicles of this type a x Energy consumption during driving; and Each is a road segment a x The average speed and length.

[0111] 1.4 Calculation of Generalized Trip Costs for Multi-User Routes

[0112] Based on the calculated real-time path travel time, driving energy consumption and real-time operating conditions of charging facilities for different users, the PSL model is used to calculate the real-time generalized travel cost for fuel vehicle and electric vehicle users, as shown in Equation (4).

[0113] (4)

[0114] in, and They are respectively m Type of vehicle via OD ( r,s The time value of driving and the time value of charging services; For charging stations c i Energy prices; a m for m Equivalent car coefficient for vehicle class; For electric vehicles At the charging station c i The energy replenishment demand; for t Always ready to set off m Sections of road for vehicles of this type a x Travel time; and Electric vehicles At the charging station c i Queuing time and charging time; To determine the charging station ci Is it in OD pair ( r,s Inter-path k The 0-1 variable on the charging station c i In the path k hour, ,otherwise .

[0115] 2. Construct a highway travel sub-network for users of gasoline-powered vehicles and electric vehicles, and characterize the service process of fast charging of electric vehicles en route.

[0116] 2.1 By analyzing the charging demand of electric vehicles on the highway network, and based on the highway network topology, the layout of charging facilities along the highway, and the highway energy network structure, considering the energy consumption of different types of vehicles and the range anxiety of electric vehicle drivers, a spatiotemporal extended network for both gasoline-powered and electric vehicles is constructed, such as... Figure 3 As shown.

[0117] Construction of the traffic sub-network for fuel-powered vehicles: Using the highway topology as input, the shortest path is explored for any OD pair using Dijkstra's algorithm, and all paths between OD pairs are searched using depth-first search. The effective path set for fuel-powered vehicles is obtained using formula (5).

[0118] (5)

[0119] l rs,k For OD pair ( r,s Inter-path k Ω is the amplification factor. rs For OD pair ( r,s The shortest path between ( ).

[0120] Construction of a Hybrid Energy Travel Subnetwork for Electric Vehicles: Taking the layout of charging facilities along highways, the highway energy network structure, and the energy consumption model of electric vehicles as inputs, a set of travel paths under hybrid energy that satisfies the range anxiety (battery constraint) of electric vehicle users is obtained. In the battery constraint, considering the driver's secondary travel needs after highway travel and the need for one or more charging trips along the way, battery constraints are set for electric vehicles during the journey and at the destination, as shown in Equation 6, specifically: This means that when an electric vehicle travels from its starting point to the charging station, its State of Charge (SOC) must not be lower than [a certain value]. S 1 ; This indicates that when an electric vehicle travels from charging station 1 to charging station 2, its State of Charge (SOC) must not be lower than [a certain value]. S 1 ; When leaving the highway, the vehicle's State of Charge (SOC) must not be less than [amount missing]. S 2 This is to meet users' needs for secondary travel.

[0121] (6)

[0122] in, , and Electric vehicles The vehicle's SOC (State of Charge) is measured from the starting point to the charging station, from charging station 1 to charging station 2, and when leaving the highway terminus. S 1 and S 2 These represent the minimum SOC required for electric vehicles while driving and when leaving the road network, respectively. To determine the path k Select charging station c i Is it feasible to use 0-1 variables when the path is... k choose c i When charging is feasible, ,otherwise .

[0123] 2.2 Modeling the service process of fast charging of electric vehicles on the way. Based on queuing theory, the fast charging behavior of electric vehicles on the way is simulated and modeled to simulate the dynamic process of electric vehicles arriving at the charging station, queuing, receiving service and leaving the charging station during the dynamic traffic flow. The queuing system adopts a waiting system and follows the first-come, first-served rule. The queuing service of the charging station is independent of the highway network, and the queuing and charging traffic flow in its system will not affect the state of the road network. The arriving user selects the charging pile with the shortest remaining service time at the current time to queue for charging, and its charging formula is shown in (7). The charging pile is regarded as a service station in the service system, and a parallel service rule is adopted. All charging piles are fast charging type.

[0124] (7)

[0125] In the formula, For electric vehicles At the charging station Required charging time, Total battery capacity ( ), For charging stations Charging energy consumption factor For electric vehicles At the charging station Vehicle SOC at that time.

[0126] 3. Perform dynamic traffic assignment for mixed traffic flows.

[0127] Perform random dynamic user equilibrium allocation of traffic flow: Based on real-time OD trip volume, road segment status, and charging facility operating conditions, simulate the loading and transmission process of mixed traffic flow and the charging queuing service of electric vehicles in the current time period. The specific steps are as follows:

[0128] 3.1 Adding new traffic loading

[0129] First, the problem of repeated road segments is solved by adding a correction term. This study uses the PSL (Path Size Logit) model of discrete choice model to model the path selection behavior of multiple users, as shown in equations (8)-(9).

[0130] (8)

[0131] (9)

[0132] in, for m Type of vehicle t Choose the path at any time k The probability of; The coefficient of variation reflects the discrepancy in perceived differences among users; for t Time Path k of m Broadly defined travel costs for similar vehicles; For OD pair ( r,s Inter-path k Correction terms; and Each is a road segment a x and path k Length; To determine the path k Does it include road sections? a x 0-1 variables, Representing a path k Including road sections ,otherwise ;

[0133] Then, based on the real-time OD traffic and the real-time generalized travel costs of each path for multiple users obtained in step B, the real-time loading traffic of each path for multiple users is calculated, as shown in equation (10).

[0134] (10)

[0135] in, fort Time OD pair ( r,s )between m The number of vehicles loaded for this type of vehicle. for t Time OD pair ( r,s )between m Vehicle route selection k The number of vehicles loaded.

[0136] 3.2 Road network traffic transmission

[0137] Using a link transmission model, the transmission process of a vehicle entering, driving, and leaving a road segment in the road network is simulated at the micro level, completing the user's location update, remaining battery level update, and timestamp update at the current time step.

[0138] Based on real-time vehicle location, vehicle time, and electric vehicle SOC status, the simulation system has three different vehicle status update scenarios.

[0139] Scenario 1: When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is less than the remaining time in the current time period and the vehicle is not on the last road segment of the route, the vehicle location is updated as follows: the vehicle enters the next road segment or leaves the charging station; the vehicle time is updated as follows: the remaining time in the current time period is updated to the difference between the remaining time in the current time period and the travel time in the current time period, and the travel time of the current road segment is updated to the travel time of the next road segment; the electric vehicle SOC is updated as follows: if the vehicle is in a driving state, the current SOC of the electric vehicle is updated to the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; if the vehicle is in a charging state, the current SOC of the electric vehicle is updated to 100% battery level.

[0140] The second scenario: When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is greater than the remaining time in the current time period, the vehicle location is updated as follows: The vehicle is still in the current road segment or charging station; The vehicle time is updated as follows: The remaining time in the current time period is updated to zero, and the travel time of the current road segment is updated as the difference between the travel time of the current road segment and the remaining time in the current time period; The electric vehicle's SOC is updated as follows: If the vehicle is in a driving state, the current SOC of the electric vehicle is updated as the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; If the vehicle is in a charging state, the current SOC of the electric vehicle is updated as the SOC of the previous moment plus the charging amount in the current time period.

[0141] The third scenario: When the vehicle is located on the last segment of the route and the travel time of the segment is less than or equal to the remaining time in the current time period, the vehicle location is updated as follows: the vehicle leaves the road network; the vehicle time is updated as follows: the remaining time in the current time period and the travel time of the segment are both updated to zero; the electric vehicle SOC is updated as follows: the current electric vehicle SOC is updated to the SOC at the end of the previous segment minus the energy consumption of the current segment.

[0142] Then, as shown in equation (3), the remaining battery power of the electric vehicle after traveling the road segment is calculated based on the initial battery power of the current road network, the average driving speed of the road segment, and the length of the road segment.

[0143] 4. Charging Facility Layout Optimization Model Construction: Considering the reverse construction sequence and highway energy structure, a two-layer site selection optimization model is constructed to optimize the layout of fast charging facilities along highways. Based on step C, the lower-layer model is a mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions. The upper-layer model is a highway charging facility network optimization model considering the reverse construction sequence and green energy structure, which plans the location and capacity of fast charging facilities along the highway.

[0144] In this site selection model, on the one hand, from the perspective of charging station investment and operation, the optimization objective is to minimize the sum of the annual average construction cost, grid connection cost, and operation and maintenance cost of fast charging facilities, as shown in equations (11)-(15); on the other hand, from the perspective of government energy and environment, the optimization objective is to maximize the proportion of green electricity in the charging facility network, as shown in equations (16)-(18). The location and capacity of fast charging facilities are optimized and solved using the NSGA-II algorithm. Under the premise of satisfying the proposed forward and reverse construction timing constraints, network accessibility constraints, and charging facility service level constraints, the location and capacity of the facilities are planned.

[0145] Objective function:

[0146] (11)

[0147] (12)

[0148] (13)

[0149] (14)

[0150] (15)

[0151] (16)

[0152] (17)

[0153] (18);

[0154] In the formula, , , They represent During the phase The proposed plan includes the construction cost of charging facilities, grid connection cost, and operation and maintenance cost. This is the capital recovery factor; The interest rate is 6.8%. The capital recovery period; and They represent During the phase Under the proposed plan, the decision is whether to build a station and the size of the facilities. for During the phase, determine the service area exist The plan specifies a 0-1 variable for whether to build a website; if a website is built, then... ,otherwise ; for During the phase, service area exist The number of charging stations to be built under the plan; for During the phase, charging stations The transformer capacity; The construction cost per kilometer of a 10kV overhead line is 200,100 yuan / km. For the site Distance to the substation (1 km); A route adjustment factor of 0.2 is used to avoid redundant investment in the line. The electricity price is 0.76 yuan / kWh; The average load of the charging station's power distribution equipment is 15kW. for During the phase, charging stations Total working hours within the year; The average annual labor cost per charging station is 12,000 yuan. The average annual management cost per unit transformer capacity is 59.2 yuan / kVA; , for During the phase, Electric vehicle traffic allocation results under the scheme, charging stations Total working hours within the year;

[0155] Constraints:

[0156] (19)

[0157] (20)

[0158] (twenty one)

[0159] (twenty two)

[0160] (twenty three)

[0161] In the reverse construction phase, the location of the charging station and the corresponding number of charging piles must be a subset of the layout of the fast charging facilities in the later stage, as shown in equations (19)-(20). The average charging service time for electric vehicle users within the charging station is constrained, as shown in equation (21). for w Phased Plan Below, charging station i Average charging service time for vehicles within the vehicle; for w Under this stage, the vehicle charging service time threshold is defined. Network reachability constraints are proposed to ensure the connectivity of the charging infrastructure network. As shown in equations (22)-(23), a fully charged electric vehicle can meet the energy consumption requirements between any two points (starting point to charging station, charging station to charging station, charging station to destination). Where, for During this phase, a 0-1 variable is used to determine whether a service area has been established; if so, then... ,otherwise ; for During the phase, service area The number of internal charging stations built; and These are the minimum and maximum number of charging piles to be built, respectively. for During the phase, charging stations Average waiting time for vehicles inside the vehicle; The threshold for vehicle waiting time within the station; To determine the path Select charging station Whether a path is reachable is a 0-1 variable, when the path is reachable. choose When charging is available, ,otherwise .

[0162] 5. The lower-level dynamic mixed traffic flow assignment model is solved using a traffic simulation algorithm based on the iterative weighted method to obtain the dynamic charging demand distribution under the equilibrium state of random users, and this distribution is then passed to the upper-level model. In the upper-level site selection optimization model, under the conditions of satisfying construction sequence constraints, facility service level constraints, and network reachability constraints, the NSGA-II algorithm is used to plan the location and capacity of charging stations, and the planned facility layout is then passed to the lower level. Figure 4 This refers to the proposed NSGA-Ⅱ algorithm.

[0163] In summary, this invention provides a method for planning the layout of charging facilities along highways. First, it constructs an intercity highway travel sub-network for both gasoline and electric vehicle users, and, combined with an electric vehicle driving energy consumption model, builds a set of effective paths that meet driving energy consumption requirements. Second, it analyzes electric vehicle travel behavior and uses the PSL model to model the path selection behavior of different users. Then, it employs a link-based dynamic traffic flow model to simulate the dynamic process of traffic flow loading, transmission, and departure in the road network; simultaneously, it constructs a charging behavior simulation model to simulate the dynamic process of vehicles receiving services at charging stations and updating the status of charging facilities. Finally, based on the spatiotemporal distribution of electric vehicle charging demand obtained from the lower-level simulation, the upper-level NSGA-II algorithm is used to optimize the layout of charging facilities. The method provided by this invention has the following advantages:

[0164] (1) By analyzing the charging demand of electric vehicles on the intercity road network and combining the network energy structure, a dynamic traffic flow simulation algorithm for the road network was constructed by introducing multi-user path selection behavior. While realizing the dynamic estimation of charging demand throughout the day in the mixed traffic network, it can also reflect the impact of charging costs, charging facility type, location and capacity, electric vehicle proportion and other factors on the distribution of charging demand. This provides quantitative support for adjusting the distribution of charging demand and stabilizing the power grid load, and provides decision support for the operation planning of charging facilities.

[0165] (2) To meet the intercity travel needs of electric vehicles, this paper proposes a two-layer optimization model for the deployment of fast charging facilities on highways, taking into account the energy supply structure of the transportation network and the dynamic charging needs of users. The model considers the dynamic changes in the proportion of EVs and the coverage area of ​​green energy at different development stages, and proposes a dynamic optimization scheme for the allocation of charging facility resources, providing decision support for the orderly planning of charging facilities.

[0166] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0167] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0168] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0169] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for planning the layout of charging facilities along highways, characterized in that, include: S1 models and calculates the travel route selection behavior of multiple users based on the factors influencing user travel, and obtains the route travel time, route travel energy consumption, and generalized travel cost of multiple users; the factors influencing user travel include the difference in behavior between fuel vehicle and electric vehicle travelers, road congestion status, vehicle energy consumption, highway energy distribution, charging facility layout, and whether there is a charging demand. S2 analyzes the charging demand of electric vehicles on the highway network, and combines the highway network topology, the layout of charging facilities along the highway, the highway energy network structure, the driving energy consumption of different types of vehicles and the range anxiety of electric vehicle drivers to construct a spatiotemporal extended network for fuel vehicles and electric vehicles, and depict the service process of fast charging of electric vehicles on the way. S3 achieves dynamic information interaction between road network operation status, vehicle driving characteristics and charging facility status by constructing a dynamic traffic flow allocation model that includes the fast charging behavior of electric vehicle users during the journey, a stochastic dynamic user equilibrium model under mixed traffic flow, and a dynamic traffic flow iterative algorithm that integrates charging queuing service simulation, thereby obtaining the charging demand of electric vehicles on highways under time-varying networks. S4 Based on the path travel time, path travel energy consumption, multi-user path generalized travel cost, electric vehicle fast charging service process and highway electric vehicle charging demand under time-varying network, a two-layer location optimization model is constructed, and the layout of highway fast charging facilities is optimized by the two-layer location optimization model to plan the location and capacity of fast charging facilities along the highway. The upper-layer model of the two-layer site selection optimization model is a highway charging facility network optimization model considering reverse construction sequence and green energy structure, while the lower-layer model is a mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions. The mixed traffic flow allocation model based on stochastic dynamic user equilibrium conditions is solved using a traffic simulation algorithm based on an iterative weighted method to obtain the dynamic charging demand distribution under stochastic user equilibrium conditions, which is then passed to the highway charging facility network optimization model considering reverse construction sequence and green energy structure. Finally, the highway charging facility network optimization model considering reverse construction sequence and green energy structure is solved using a multi-objective heuristic algorithm to obtain the planning results for the location and capacity of fast charging facilities along the highway. The planning results for the location and capacity of fast charging facilities along highways include: the optimization results obtained through calculations to maximize the proportion of green electricity in the charging facility network, and the planning of the location and capacity of charging facilities along highways.

2. The method according to claim 1, characterized in that, The differences in the behavior of fuel-powered vehicle and electric vehicle users in step S1 include the factors considered by fuel-powered vehicle users when choosing routes and the factors considered by electric vehicle users when choosing routes. The factors considered by fuel-powered vehicle users when choosing routes include route travel time and route travel energy consumption. The factors considered by electric vehicle users when choosing routes include route travel time, energy structure of charging stations, charging time and queuing time. Step S1 includes: S11 Calculation Formula Based on Point Queuing Model (1) (2) Calculate the real-time travel time for each road segment; where, In order to be in t time m Sections of road for vehicles of this type a x The travel time is determined by the road segment. a x Free-flow travel time and road section delay time Together they form; for t Time and Section a x Queue length; S12 Based on the highway network topology and the real-time travel time of each road segment, calculate the real-time average speed of each road segment, and then use the formula... (3) The driving energy consumption of gasoline-powered vehicles and electric vehicles is modeled and calculated to obtain their real-time energy consumption; where, for t time m Sections of road for vehicles of this type a x Energy consumption during driving; and Each is a road segment a x The average speed and length; S13, based on factors influencing user travel, and combining the real-time travel time of each road segment, the real-time energy consumption of fuel-powered and electric vehicles, and the real-time operating conditions of charging facilities, calculates the formula using the PSL model. (4) Calculate the real-time generalized travel costs for users of gasoline-powered and electric vehicles; where, and They are respectively m Type of vehicle via OD ( r,s The time value of driving and the time value of charging services; For charging stations c i Energy prices; a m for m Equivalent car coefficient for vehicle class; For electric vehicles At the charging station c i The energy replenishment demand; for t Always ready to set off m Sections of road for vehicles of this type a x Travel time; and Electric vehicles At the charging station c i Queuing time and charging time; To determine the charging station c i Is it in OD pair ( r,s Inter-path k The 0-1 variable on the charging station c i In the path k hour, ,otherwise .

3. The method according to claim 1, characterized in that, S2 includes: S21, based on the shortest path obtained using Dijkstra's algorithm for any OD pair, and all paths obtained through depth-first search of OD pairs, takes the highway topology as input and uses the formula... (5) The effective path set for gasoline-powered vehicles is calculated; where, For OD Between, Path Length; For OD The shortest path length between; This is the magnification factor; S22 takes the layout of charging facilities along the highway topology, the highway energy network structure and the electric vehicle driving energy consumption model as inputs to obtain a set of travel routes under hybrid energy that can meet the range anxiety of electric vehicle users. The range anxiety of electric vehicle users is a battery constraint, including battery constraints during the journey and battery constraints at the destination, expressed by the formula... (6) Obtain; where, , and Electric vehicles The vehicle's SOC (State of Charge) is measured from the starting point to the charging station, from charging station 1 to charging station 2, and when leaving the highway terminus. S 1 and S 2 These represent the minimum SOC required for electric vehicles while driving and when leaving the road network, respectively. To determine the path k Select charging station c i Is it feasible to use 0-1 variables when the path is... k choose c i When charging is feasible, ,otherwise ; S23 Based on queuing theory, a fast charging behavior model of electric vehicles during driving is constructed to simulate the dynamic process of electric vehicles arriving at charging stations, queuing, receiving services, and leaving charging stations during dynamic traffic flow. The queuing simulation process is based on the waiting system principle and the first-come, first-served principle. Arriving users select the charging station with the shortest remaining service time at the current moment to queue for charging. The charging formula is as follows. (7) As shown; in the formula, For electric vehicles At the charging station Required charging time, Total battery capacity ( ), For charging stations Charging energy consumption factor For electric vehicles At the charging station Vehicle SOC at that time.

4. The method according to claim 1, characterized in that, Step S3 includes: S31 Through-type (8) (9) Construct a dynamic traffic flow assignment model that incorporates the fast-charging behavior of electric vehicle users during their journey, and a stochastic dynamic user equilibrium model under mixed traffic flow; where, for m Type of vehicle t Choose the path at any time k The probability of; The coefficient of variation reflects the differences in perceived perception among users; for t Time Path k of m Broadly defined travel costs for similar vehicles; For OD pair ( r,s Inter-path k Correction terms; and Each is a road segment a x and path k Length; To determine the path k Does it include road sections? a x 0-1 variables, Representing a path k Including road sections ,otherwise ; S32 Through-type (10) A dynamic traffic flow iterative algorithm integrating charging queuing service simulation is constructed; where, for t Time OD pair ( r,s )between m The number of vehicles loaded for this type of vehicle. for t Time OD pair ( r,s )between m Vehicle route selection k The number of vehicles loaded; S33 uses a link transmission mechanism to simulate the transmission process of vehicles entering, traveling, and leaving road segments in the road network, obtaining the charging demand distribution that satisfies the equilibrium state of random users at different time periods; specifically, it includes any one of the following three cases: When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is less than the remaining time in the current time period and the vehicle is not on the last road segment of the route, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. The vehicle location update is when the vehicle enters the next road segment or leaves the charging station. The vehicle time update is when the remaining time in the current time period is updated to the difference between the remaining time in the current time period and the travel time in the current time period, and the travel time of the current road segment is updated to the travel time of the next road segment. The electric vehicle SOC update includes: if the vehicle is in a driving state, the current SOC of the electric vehicle is updated to the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; if the vehicle is in a charging state, the current SOC of the electric vehicle is updated to 100% battery level. When the travel time of the current road segment or the charging service time of the electric vehicle at the charging station is greater than the remaining time in the current time period, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. The vehicle location update indicates that the vehicle is still in the current road segment or charging station. The vehicle time update indicates that the remaining time in the current time period is updated to zero, and the travel time of the current road segment is updated to the difference between the travel time of the current road segment and the remaining time in the current time period. The electric vehicle SOC update includes: if the vehicle is in a driving state, the current SOC of the electric vehicle is updated to the SOC at the end of the previous road segment minus the driving energy consumption of the current road segment; if the vehicle is in a charging state, the current SOC of the electric vehicle is updated to the SOC of the previous moment plus the charging amount in the current time period. When a vehicle is located on the last segment of the path, and the travel time of the segment is less than or equal to the remaining time in the current time period, vehicle location update, vehicle time update, and electric vehicle SOC update are performed. The vehicle location update is when the vehicle leaves the road network. The vehicle time update is when both the remaining time in the current time period and the travel time of the segment are updated to zero. The electric vehicle SOC update includes: the current electric vehicle's SOC is updated to the SOC at the end of the previous segment minus the energy consumption of the current segment.

5. The method according to claim 1, characterized in that, The solution process for the lower-level model of the two-level location optimization model includes: Through the first objective function (11) (12) (13) (14) (15) The optimization calculation aims to minimize the sum of the annual average construction cost, grid connection cost, and operation and maintenance cost of fast charging facilities; where, , , They represent During the phase The proposed plan includes the construction cost of charging facilities, grid connection cost, and operation and maintenance cost. This is the capital recovery factor; For interest rates; The capital recovery period; and They represent During the phase Under the proposed plan, the decision is whether to build a station and the size of the facilities. for During the phase, determine the service area exist The plan specifies a 0-1 variable for whether to build a website; if a website is built, then... ,otherwise ; for During the phase, service area exist The number of charging stations to be built under the plan; for During the phase, charging stations The transformer capacity; Cost per kilometer of 10kV overhead line; For the site Distance from the substation; Route adjustment coefficient to avoid redundant investment in lines; Electricity price; The average load of the power distribution equipment in the charging station; for During the phase, charging stations Total working hours within the year; The average annual labor cost per charging station; The average annual management cost per unit transformer capacity; , for During the phase, Electric vehicle traffic allocation results under the scheme, charging stations Total working hours within the year; Through the second objective function (16) (17) (18) Construct an optimized calculation based on the proportion of green electricity in the charging infrastructure network; S33 Based on the calculation results of sub-step S32, the NSGA-II algorithm is used to calculate the planning results of the location and capacity of fast charging facilities along the highway; the calculation process has the following constraints: Reverse construction constraints (19) (20)； Constraints on the average charging service time of electric vehicle users at charging stations (21)， In the formula, for w Phased Plan Below, charging station i Average charging service time for vehicles within the vehicle. for w Under this phase, a network reachability constraint is proposed based on the vehicle charging service time threshold to ensure the connectivity of the charging infrastructure network. A fully charged electric vehicle can meet the energy consumption constraints between any two points. (22) (23)； In the formula, for During this phase, a 0-1 variable is used to determine whether a service area has been established; if so, then... ,otherwise ; for During the phase, service area The number of internal charging stations built; and These are the minimum and maximum number of charging piles to be built, respectively. for During the phase, charging stations Average waiting time for vehicles inside the vehicle; The threshold for vehicle waiting time within the station; To determine the path Select charging station Whether a path is reachable is a 0-1 variable, when the path is reachable. choose When charging is available, ,otherwise .

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