A method for planning highway traffic energy facilities

Abstract

The present invention relates to a method for planning new transportation energy facilities on highways, including determining basic parameters of electric vehicles and highways; simulating electric vehicle travel on highways through dynamic traffic models; simulating the spatiotemporal distribution of electric vehicle charging loads using the Monte Carlo method; evaluating the utilization potential and complementarity of wind and solar energy in highway service areas; establishing a new transportation energy facility planning model, including: establishing an objective function and constraint conditions; and solving the new transportation energy facility planning scheme using an improved genetic algorithm based on the spatiotemporal distribution of electric vehicle charging loads and the new transportation energy facility planning model. Compared with the prior art, the present invention improves the existing site selection and sizing methods from the aspects of electric vehicle parameter characterization, dynamic traffic simulation, wind and solar energy evaluation, and provides guidance for the site selection and sizing of new transportation energy facilities on highways.

Description

Technical Field

[0001] The invention relates to the field of transportation engineering, and in particular to a method for planning highway transportation energy facilities. Background Art

[0002] With the increase in electric vehicle mileage and the rapid construction of urban transportation energy facilities, the travel demand for electric vehicles has expanded from cities to highways. The rational layout of highway transportation energy facilities is the key to ensuring that the mileage of electric vehicles meets travel needs. Highway transportation energy facilities are transportation energy infrastructure mainly based on fast charging stations. Through the coordinated development of transportation, energy systems, and infrastructure, the deep integration of transportation energy is achieved. In addition, green transportation requires the optimization of transportation energy structure and the promotion of clean and low-carbon transformation of transportation energy. Vigorously developing wind and solar energy applications is a feasible path for transportation energy transformation. Planning the location and configuration of transportation energy facilities and incorporating wind and solar energy into highway charging facilities will encourage more drivers to choose electric vehicles instead of fuel vehicles for long-distance travel. Therefore, studying the planning method of highway transportation energy facilities is of great significance to ensure the sustainable development of electric vehicles and the transformation and upgrading of transportation energy.

[0003] At present, the research on transportation energy facility planning at home and abroad focuses on the location and capacity of charging stations. However, there are the following shortcomings in the research: first, the parameters of electric vehicles are not accurately characterized, and the randomness of electric vehicle travel is not considered; second, static traffic models are often used, and the changes in electric vehicle travel time are not considered; finally, wind and solar energy are rarely considered, and there is a lack of evaluation of the available performance of wind and solar energy. Summary of the invention

[0004] The purpose of the present invention is to provide a method for planning highway transportation energy facilities in order to overcome the defects of the above-mentioned prior art. In view of the technical problems of inaccurate parameter characterization, inaccurate traffic simulation and failure to consider wind and solar energy in the existing planning methods, a method for planning highway transportation energy facilities is proposed. From the aspects of electric vehicle parameter characterization, dynamic traffic simulation, wind and solar energy evaluation, the existing site selection and sizing methods are improved to provide guidance for the site selection and sizing of highway transportation energy facilities.

[0005] The conception process of the present invention is as follows:

[0006] First, the parameters of electric vehicles are not accurately described, and the randomness of electric vehicle travel is not taken into account. More accurate parameters should be used to describe electric vehicle travel. Second, static traffic models are often used, and there is a lack of consideration of changes in electric vehicle travel time. A dynamic traffic model should be used to simulate the spatiotemporal changes in electric vehicle travel. Finally, wind and solar energy are less considered, and there is a lack of evaluation of the availability of wind and solar energy. The availability of wind and solar energy should be evaluated, and wind and solar energy should be included in the factors affecting site selection.

[0007] Considering the shortcomings of the above planning methods, the present invention proposes a highway transportation energy facility planning method, which takes into account the influence of factors such as electric vehicles, service areas, wind and solar energy, and provides a reference for the layout of transportation energy facilities. Reasonable layout of transportation energy facilities is not only conducive to alleviating the anxiety of electric vehicle range, promoting the application of wind and solar energy, but also accelerating the transformation and upgrading of energy structure and promoting the deep integration of transportation energy.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] The present invention provides a method for planning highway traffic energy facilities, comprising the following steps:

[0010] S1: Determine the basic parameters of electric vehicles and highways;

[0011] S2: Based on the basic parameters obtained in S1, the highway electric vehicle travel is simulated through the dynamic traffic model;

[0012] S3: Based on the basic parameters obtained in S1 and the dynamic traffic model established in S2, the Monte Carlo method is used to simulate the spatiotemporal distribution of electric vehicle charging load;

[0013] S4: Evaluate the potential and complementarity of wind and solar energy utilization in highway service areas;

[0014] S5: Establish a transportation energy facility planning model, including: establishing objective functions and constraints;

[0015] S6: Based on the spatiotemporal distribution and planning model of electric vehicle charging load, an improved genetic algorithm is used to solve the transportation energy facility planning scheme.

[0016] Further, in S1, the process of determining basic parameters of the electric vehicle and the highway includes:

[0017] S-1-1: Get the initial time of electric vehicles on the highway, sample the probability of the time when electric vehicles enter the toll station, and get the initial time when electric vehicles enter the highway;

[0018] S-1-2: Get the starting and ending points of electric vehicles on the highway. The starting and ending points of electric vehicles are determined by the OD probability distribution matrix P n×n OK, OD n×n The distribution matrix is ​​converted into the OD probability distribution matrix P n×n ;

[0019]

[0020] Where: p ij is the travel probability from point i and point j; q ij is the traffic flow from point i to point j;

[0021] S-1-3: Obtain the battery power of the electric vehicle on the highway, including the initial power, change power, and charging power. The battery power is represented by SOC (State of charge), where SOC is the ratio of the current battery power of the electric vehicle to the fully charged battery power.

[0022] Furthermore, in S1, the process of obtaining the initial power, the change power, and the charging power includes:

[0023] S-1-3-1: Get the initial SOC of the electric vehicle:

[0024]

[0025] Where: SOC0 is the initial SOC of the electric vehicle, μ is the expected value of the initial SOC of the electric vehicle entering the highway; σ is the standard deviation of the SOC0 of the electric vehicle;

[0026] S-1-3-2: Get the changing SOC of electric vehicles:

[0027]

[0028] Where: E t is the battery capacity of the electric vehicle; P ev Energy consumption for electric vehicles;

[0029] S-1-3-3: Get the charging SOC of the electric vehicle:

[0030]

[0031] Where: T c Charging time for electric vehicles; SOC e is the desired target SOC; SOC t is the current SOC; sc is the charging efficiency; P sc is the charging power, E ev The battery is fully charged.

[0032] Furthermore, in S2, the process of simulating the travel of electric vehicles on highways through the dynamic traffic model is as follows: the link transmission model (LTM) is used to model the spatiotemporal distribution of electric vehicles on highways and calculate the traffic flow changes on highways, specifically including:

[0033] (1) Constructing the road segment model receiving flow

[0034] This includes obtaining the receiving capacity of the road section R(t). In the road section transmission model, the receiving capacity of the road section refers to the maximum number of vehicles that enter the road section within the period [t, t+Δt]. The receiving flow is affected by the downstream boundary condition x L The constraint of , the receiving capacity of the road section R(t) is as follows:

[0035]

[0036] Among them, x0 is the upstream boundary; x L is the downstream boundary; L is the distance between the upstream and downstream sections; w is the speed corresponding to the vehicle exceeding the maximum capacity of the section; k j is the congestion density; q m is the maximum capacity;

[0037] (2) Constructing the road segment model sending flow

[0038] This includes obtaining the section sending capacity S(t), which refers to the maximum number of vehicles that leave a section within the period [t, t+Δt]. The sending flow is constrained by the boundary condition x0 upstream of the section. The section sending capacity S(t) is:

[0039]

[0040] Among them, v f is the free flow speed.

[0041] Furthermore, in S3, the process of simulating the spatiotemporal distribution of electric vehicle charging load using the Monte Carlo method includes:

[0042] S3-1: Obtain the basic parameters of the determined highway network and electric vehicles;

[0043] S3-2: Based on the OD probability distribution matrix determined in S-1-2 and the probability distribution of the time when electric vehicles enter the highway, the Monte Carlo method is used to assign the starting and ending points and the initial departure time to each electric vehicle, and the driving path and mileage of the electric vehicle on the highway are obtained;

[0044] S3-3: Determine the initial SOC and initial power of the electric vehicle through the SOC probability distribution model constructed in the initial S-1-3-1;

[0045] S3-4: Use the LTM road section model constructed in S2 to simulate the driving of electric vehicles on the highway, calculate the power consumption of electric vehicles during driving, and output the power consumption, remaining power and charging strategy of electric vehicles;

[0046] S3-5: Using the results output by S3-4, select the appropriate charging strategy, determine the charging efficiency, charging power and charging time, output the spatiotemporal coordinates of the charging demand, and predict the spatiotemporal distribution of the electric vehicle charging load.

[0047] Furthermore, in S4, resource richness and correlation analysis are specifically used to evaluate energy utilization potential and complementarity, namely:

[0048] S4-1: Conduct wind energy resource abundance assessment: Use effective wind power density (EWPD) for assessment.

[0049]

[0050] Where: EWPD is the effective wind power density, W / m 2 ; n is the number of hourly wind speeds within the effective wind speed range; ρ is the air density, kg / m 3 ;v i The hourly wind speed is between [3,20] m / s;

[0051] S4-2: Conduct a light energy resource richness assessment, using the ERA5 database to obtain the annual total solar horizontal radiation and evaluate the solar energy resource richness;

[0052] S4-3: Conduct an assessment of the wind-solar complementarity of highways.

[0053] Furthermore, in S4, the Kendall correlation coefficient is specifically used to measure the complementarity of wind and solar energy, where:

[0054] (P w (t1),P s (t1)) and (P w (t2),P s (t2)) are wind energy time series P w (t) and light energy time series P s For two independent pairs in (t), the Kendall correlation coefficient is:

[0055] τ=P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))>0}

[0056] -P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))<0}

[0057] Where: τ is the Kendall correlation coefficient; P is the probability of the event.

[0058] Furthermore, in S5, establishing the transportation energy facility planning model specifically includes constructing an objective function, which is:

[0059] S5-1-1: Construct the maximum interception objective function:

[0060]

[0061] Where: L is the set of all path ODs, l∈L; f l is the charging demand of electric vehicles on road section l; y l Indicates that if the charging demand of electric vehicles on road section l is obtained, it is 1, otherwise it is 0;

[0062] S5-1-2: Construction and operation cost minimization objective function

[0063] minC=C cstr +C opm

[0064] Where: C cstr The construction investment cost of transportation and energy facilities; C opm Operation and maintenance costs of transportation energy facilities;

[0065]

[0066] Where: k is the candidate point for building a charging station; m k is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; The cost of building an AC charging station; The cost of building a DC charging station; The cost of building wind power facilities; is the cost of building photovoltaic power generation facilities; x k ,y k and z k Indicates that the construction is 1 at node k, otherwise it is 0;

[0067]

[0068] In the formula: ζ is the conversion coefficient; r0 is the discount rate; for the proceeds of constructing wind power facilities; For the income from the construction of photovoltaic power generation facilities.

[0069] Furthermore, in S5, establishing the transportation energy facility planning model specifically includes constructing constraint conditions, specifically:

[0070] S5-2-1: Establish a distance constraint for charging stations. The distance between adjacent charging stations should be less than the driving range of the electric vehicle:

[0071] l min ≤d ij ≤l max

[0072] Where: d ij is the distance to transportation and energy facilities; l min is the minimum spacing; l max is the maximum spacing;

[0073] S5-2-2: Establish facility service capacity constraints, and the facility service capacity meets the interception demand:

[0074] f l y l ≤I k x k

[0075] Where: I k is the maximum capacity that candidate site k can bear;

[0076] S5-2-3: The basic constraints for establishing the classic interception site selection model require that there must be a set of site selection points that meet the requirements:

[0077]

[0078] S5-2-4: Establish logical constraints on transportation energy facilities. When no transportation energy facilities are built at k, charging piles m and n are not built at the same time:

[0079] m+n≤x k M

[0080] Where: M is a preset maximum value;

[0081] S5-2-5: Establish capacity constraints on wind and solar energy facilities:

[0082]

[0083] Where: The capacity of the wind power facility; is the maximum capacity of the wind power facility; is the capacity of the photovoltaic power generation facility; is the maximum capacity of the photovoltaic power generation facility;

[0084] S5-2-7: Establish capacity constraints for charging facilities, taking into account the maximum hourly charging demand capacity:

[0085]

[0086] Where: m k is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; ac is the charging power of a single AC charging pile; p dc The charging power of a single DC charging pile; is the maximum hourly charging demand capacity of the electric vehicle at candidate point k.

[0087] Furthermore, in S6, the improved genetic algorithm is used to solve the transportation energy facility planning problem as follows:

[0088] S6-1: Input the road network, travel matrix and vehicle parameters, and set the improved genetic algorithm parameters;

[0089] S6-2: Generate the initial parent planning scheme randomly using genetic algorithm;

[0090] S6-3: Generate new child planning schemes through selection, crossover and mutation operations, each of which includes a set of transportation energy facility sites and facility quantities;

[0091] S6-4: Calculate the charging requirements of electric vehicles in all parent and child planning schemes using the dynamic traffic simulation in S2 and the Monte Carlo simulation in S2, and then calculate the cost of each parent and child planning scheme using the transportation energy facility planning model established in S5;

[0092] S6-5: Compare the benefits of all parent and child planning schemes, and select the planning scheme with the highest benefit as the new parent planning scheme;

[0093] S6-6: Determine whether the current evolutionary generation w has reached the maximum evolutionary generation. If so, output the planning scheme with the lowest cost; otherwise, the current evolutionary generation w = w+1, re-execute S6-4 until the maximum evolutionary generation is reached, and output the optimal transportation energy facility planning scheme.

[0094] Compared with the prior art, the present invention fully considers the influence of factors such as electric vehicles, service areas and wind and solar energy, and has the following technical advantages:

[0095] 1) First, the basic parameters of electric vehicles are accurately described. The LMT dynamic traffic model and Monte Carlo method are used to simulate the travel of multiple electric vehicles on the highway, and the parameters such as travel time, mileage and power consumption are obtained. Then, the spatiotemporal distribution of the charging load of electric vehicles on the highway is obtained, thus ensuring the accuracy and rationality of the planning scheme.

[0096] 2) Taking the maximization of intercepted flow and the minimization of construction and operation costs as the objective function, constraints are established for parameters such as distance, service capacity, capacity, and quantity, and a transportation energy facility planning model is constructed. The investment in planning schemes is reduced by utilizing service areas, thereby reducing the total cost;

[0097] 3) The improved genetic algorithm is used to obtain the planning scheme of transportation energy facilities and give the optimal planning scheme. With the development of electric vehicles and the in-depth utilization of wind and solar energy, this method can provide effective guidance for the construction of highway transportation energy facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 A flowchart of the highway transportation energy facility planning method of the present invention;

[0099] Figure 2 The probability distribution map of electric vehicles entering the highway;

[0100] Figure 3 A schematic diagram of a highway network in an embodiment of the present invention;

[0101] Figure 4 It is a schematic diagram of the planning of transportation energy facilities in an embodiment of the present invention. DETAILED DESCRIPTION

[0102] On the whole, the present invention is a method for planning highway transportation energy facilities, which fully considers the influence of factors such as electric vehicles, service areas, and wind and solar energy. First, the LMT dynamic traffic model and Monte Carlo method are used to simulate the travel of multiple electric vehicles on the highway, and the parameters such as travel time, mileage and power consumption are obtained, and then the spatiotemporal distribution of the charging load of electric vehicles on the highway is obtained; secondly, the energy richness index is used to evaluate the utilization potential of wind and solar energy in the service area, and the energy utilization level is divided. The complementarity of wind and solar energy is evaluated by the Kendall correlation coefficient, which provides a reference for the construction of wind and solar power generation facilities in highway service areas; finally, with the maximization of intercepted flow and the minimization of construction and operation costs as the objective function, constraints are established for parameters such as distance, capacity, quantity and service capacity, and a transportation energy facility planning model is constructed. According to the spatiotemporal distribution of electric vehicle charging load and the transportation energy facility planning model, an improved genetic algorithm is used to obtain the transportation energy facility planning scheme. With the development of electric vehicles and the in-depth utilization of wind and solar energy, this method can provide effective guidance for the construction of highway transportation energy facilities.

[0103] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.

[0104] Example 1

[0105] The highway traffic energy facility planning method in this embodiment is shown in Figure 1 , including the following steps:

[0106] Step 1) The basic parameters for accurately describing electric vehicles on highways are as follows:

[0107] (1) The initial time of the electric vehicle on the highway: Probability sampling of the time when the electric vehicle enters the toll station is used to obtain the initial time when the electric vehicle enters the highway.

[0108] (2) The starting and ending points of electric vehicles on the highway. The starting and ending points of electric vehicles can be represented by the OD probability distribution matrix P n×n OK. n×n The distribution matrix is ​​converted into the OD probability distribution matrix P n×n .

[0109]

[0110] Where: p ij is the travel probability from point i and point j; q ij is the traffic flow from point i to point j.

[0111] (3) The battery capacity of electric vehicles on highways, including initial capacity, change capacity, and charging capacity. The battery capacity is generally represented by SOC (State of charge), which is the ratio of the current battery capacity of the electric vehicle to the fully charged battery capacity.

[0112] (3.1) Initial SOC of electric vehicles

[0113]

[0114] Where: SOC0 is the initial SOC of the electric vehicle, μ is the expected value of the initial SOC of the electric vehicle entering the highway; σ is the standard deviation of the SOC0 of the electric vehicle.

[0115] (3.2) Changing SOC of electric vehicles

[0116]

[0117] Where: E t is the battery capacity of the electric vehicle; Pev Energy consumption for electric vehicles.

[0118] (3.3) Charging SOC of electric vehicles

[0119]

[0120] Where: T c Charging time for electric vehicles; SOC e is the desired target SOC; SOC t is the current SOC; sc is the charging efficiency; P sc is the charging power; E ev The battery is fully charged.

[0121] Step 2) simulates the highway electric vehicle travel through the dynamic traffic model as follows:

[0122] The link transmission model (LTM) is used to model the spatiotemporal distribution of electric vehicles on highways. The link transmission model is used to calculate the changes in traffic flow on highways.

[0123] (1) Section model receiving flow

[0124] In the link transmission model, the receiving capacity of a link refers to the maximum number of vehicles that enter the link within the period [t, t+Δt]. The receiving flow is affected by the downstream boundary condition x L The constraint of , the receiving capacity of the road section R(t) is as follows:

[0125]

[0126] (2) Section model sending flow

[0127] The sending capacity of a road section refers to the maximum number of vehicles that leave a road section within the period [t, t+Δt]. The sending flow is constrained by the boundary condition of the upstream x0 of the road section. The sending capacity of the road section S(t) is expressed as:

[0128]

[0129] Step 3) uses the Monte Carlo method to simulate the spatiotemporal distribution of electric vehicle charging load as follows:

[0130] (1) Obtain the basic parameters of the highway network and electric vehicles. Determine the structure and basic parameters of the highway network, and determine the basic parameters of electric vehicles such as battery capacity and driving energy consumption.

[0131] (2) Based on the OD probability distribution matrix and the probability distribution of the time when electric vehicles enter the highway, the Monte Carlo method is used to assign the starting and ending points and the initial departure time to each electric vehicle, and the driving path and mileage of the electric vehicle on the highway are obtained.

[0132] (3) Determine the initial SOC and initial power of the electric vehicle through the initial SOC probability distribution model.

[0133] (4) Use the LTM road section model to simulate the driving of electric vehicles on highways, calculate the power consumption of electric vehicles during driving, and output the power consumption, remaining power and charging strategy of electric vehicles.

[0134] (5) Using the results of step (4), the electric vehicle user selects an appropriate charging strategy. It determines whether the electric vehicle needs to be charged, determines the charging efficiency, charging power and charging time, outputs the spatiotemporal coordinates of the charging demand, and predicts the spatiotemporal distribution of the electric vehicle charging load.

[0135] Step 4) evaluates the wind and solar energy potential and complementarity of highway service areas as follows:

[0136] Resource richness and correlation analysis are used to evaluate the utilization potential and complementarity of wind and solar energy.

[0137] (1) Assessment of wind energy resource abundance: Effective wind power density (EWPD) is used for assessment, as EWPD can better reflect the wind energy potential of a site than wind speed itself.

[0138]

[0139] Where: EWPD is the effective wind power density, W / m 2 ; n is the number of hourly wind speeds within the effective wind speed range; ρ is the air density, kg / m 3 ;v i The hourly wind speed is between [3,20]m / s.

[0140] (2) Assessment of the richness of solar energy resources. The ERA5 database is used to obtain the total solar radiation accumulated on the horizontal surface in a year, and combined with the solar radiation intensity, the richness of solar energy resources is evaluated.

[0141] (3) Evaluation of wind-solar complementarity of highways. Currently, the most commonly used method to evaluate the complementarity of wind and solar energy is correlation analysis, which uses the Kendall correlation coefficient to measure the complementarity of wind and solar energy. w (t1),P s (t1)) and (P w (t2),P s (t2)) are wind energy time series P w (t) and light energy time series Ps (t) are two independent pairs. The Kendall correlation coefficient is defined as:

[0142] τ=P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))>0}-P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))<0}

[0143] Where: τ is the Kendall correlation coefficient; P is the probability of the event.

[0144] Step 5) establishes a transportation energy facility planning model as follows:

[0145] (1) Objective function

[0146] The objective function includes two objective functions: maximizing intercepted flow and minimizing construction and operation costs. Combining the operating characteristics, battery characteristics, and charging characteristics of electric vehicles, the dynamic traffic model and Monte Carlo method are used to simulate electric vehicle travel, and the spatiotemporal distribution of electric vehicle charging load is obtained. The maximum interception considering the driving range is taken as the objective function; from the perspective of operators and wind and solar energy, the cost minimization is taken as the objective function.

[0147] (1.1) Maximum interception objective function

[0148]

[0149] Where: L is the set of all path ODs, l∈L; f l is the charging demand of electric vehicles on road section l; y l If the charging demand of electric vehicles on road section l is obtained, it is 1, otherwise it is 0.

[0150] (2.2) Objective function of minimizing construction and operation costs

[0151] minC=C cstr +C opm

[0152] Where: C cstr The construction investment cost of transportation and energy facilities; C opm The operating and maintenance costs of transportation energy facilities.

[0153]

[0154] Where: k is the candidate point for building a charging station, k∈K; mk is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; The cost of building an AC charging station; The cost of building a DC charging station; The cost of building wind power facilities; is the cost of building photovoltaic power generation facilities; x k ,y k and z k It means that the node k is 1, otherwise it is 0.

[0155]

[0156] In the formula: ζ is the discount coefficient, which is 3%; r0 is the discount rate; for the proceeds of constructing wind power facilities; For the income from the construction of photovoltaic power generation facilities.

[0157] (2) Constraints

[0158] (2.1) Charging station distance constraint: the distance between adjacent charging stations is less than the range of electric vehicles.

[0159] l min ≤d ij ≤l max

[0160] Where: d ij is the distance to transportation and energy facilities; l min is the minimum spacing; l max is the maximum spacing.

[0161] (2.2) Facility service capacity constraints, which constrain the number of charging piles and the service capacity of the facilities.

[0162] f l y l ≤I k x k

[0163] Where: I k is the maximum capacity that candidate site k can bear.

[0164] (2.3) The basic constraint of the classic interception site selection model is that there must be a set of site selection points that meet the requirements.

[0165]

[0166] (2.4) The logical constraint of building transportation energy facilities is that if no transportation energy facilities are built at k, charging piles m and n will not be built either.

[0167] m+n≤x k M

[0168] Where: M is a maximum value.

[0169] (2.5) The capacity of wind and solar energy facilities is too large, so restrictions should be imposed on the capacity of wind and solar energy facilities.

[0170]

[0171] Where: The capacity of the wind power facility; is the maximum capacity of the wind power facility; is the capacity of the photovoltaic power generation facility; is the maximum capacity of the photovoltaic power generation facility.

[0172] (2.6) The capacity constraint of the charging facilities takes into account the maximum hourly charging demand capacity.

[0173]

[0174] Where: m k is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; ac is the charging power of a single AC charging pile; p dc The charging power of a single DC charging pile; is the maximum hourly charging demand capacity of the electric vehicle at candidate point k.

[0175] Step 6) uses an improved genetic algorithm to solve the transportation energy facility planning scheme as follows:

[0176] (1) Input the road network, travel matrix and vehicle parameters, and set the improved genetic algorithm parameters: population size is 100, maximum evolutionary generations is 300, crossover probability is 0.8, and mutation probability is 0.2;

[0177] (2) Generate 100 initial parent planning schemes randomly using genetic algorithm;

[0178] (3) Generate 100 new child planning schemes through operations such as selection, crossover, and mutation. Each planning scheme includes a set of transportation energy facility sites and facility quantities.

[0179] (4) using the dynamic traffic simulation described in step 2) and the Monte Carlo simulation in step 3) to calculate the charging requirements of electric vehicles in all parent and child planning schemes, and then using the transportation energy facility planning model established in step 5) to calculate the cost of each parent and child planning scheme;

[0180] (5) Compare the benefits of all parent and child planning schemes, and select 100 planning schemes with high benefits as new parent planning schemes;

[0181] (6) Determine whether the current evolutionary generation w has reached the maximum evolutionary generation. If so, output the planning scheme with the lowest cost. Otherwise, the current evolutionary generation w = w + 1, and return to step (4) until the maximum evolutionary generation is reached, and output the optimal transportation energy facility planning scheme.

[0182] Application Example 1

[0183] The highway network of the embodiment of the present invention is as follows Figure 3 and Figure 4 As shown in the figure, the highway network consists of 5 major cities, 6 highway sections, and 15 highway service areas. The distances of the highway network nodes are shown in Table 1, the OD traffic probability matrix is ​​shown in Table 2, and the wind energy assessment and solar energy resource assessment are shown in Table 3. The battery capacity of the electric vehicle is taken as 55kWh, the basic power consumption per 100 kilometers is 13kWh, and the SOC0 obeys the normal distribution N(0.80,0.1). The expected SOC of each EV in the simulation is set to 0.85. The power anxiety coefficient of the electric vehicle user is taken as 0.3, and the target power of the electric vehicle is 80%. The probability of the electric vehicle entering the highway is as follows: Figure 2 As shown in Figure 2, the daily traffic volume of electric vehicles on the highway network is 30,000.

[0184] The design service life of charging facilities and wind and solar power generation facilities is 20 years, the discount rate is 0.1, the fixed cost of solar photovoltaic panels is 4 million yuan / MW, the fixed cost of wind turbines is 4.5 million yuan / MW, and the price of wind and solar power for charging electric vehicles is 1.4 yuan / kWh. The fixed cost of charging facilities is 3 million yuan, the cost of AC charging piles is 10,000 yuan / unit, the cost of DC charging piles is 100,000 yuan / unit, the charging power of AC charging piles is 7kW, and the charging power of DC charging piles is 150kW.

[0185] Table 1. Distance between nodes in expressway network

[0186] node node Distance (km) node node Distance (km) node node Distance (km) 1 6 16.53 2 14 32.02 11 12 49.43 6 7 74.32 14 15 40.45 12 13 27.61 7 4 26.93 15 3 6.54 13 2 26.36 2 8 8.97 4 16 5.79 19 20 35.56 8 9 51.72 16 17 52.61 20 3 40.36 9 10 55.57 17 5 45.62 1 11 22.56 10 5 24.54 5 18 10.1 18 19 11.05

[0187] Table 2 OD traffic probability matrix

[0188]

[0189] Table 3 Evaluation of wind and solar energy in highway service areas

[0190] serial number longitude latitude Validity period EWPD grade Solar radiation intensity grade Kendall correlation coefficient 6 117.4188 34.12648 2503 39 IV 5163 II 0.143 7 118.0355 33.93296 2816 42 IV 5132 II 0.007 8 118.5404 34.32984 2715 41 IV 5091 II 0.034 9 118.7999 34.13606 2691 41 IV 5061 II 0.025 10 119.0451 33.79734 2938 42 IV 5045 II 0.029 11 117.5155 34.21662 2524 52 III 5161 II 0.050 12 117.9363 34.2607 2738 41 IV 5127 II 0.048 13 118.2147 34.33037 2851 41 IV 5100 II 0.006 14 118.7499 34.47652 2516 41 IV 5099 II 0.144 15 119.1668 34.52065 2786 43 IV 5089 II 0.059 16 118.3167 33.80049 2915 40 IV 5147 II 0.104 17 118.7304 33.62847 3888 44 IV 5114 II 0.205 18 119.1455 33.7174 2919 43 IV 5024 III 0.145 19 119.1958 33.7799 2928 43 IV 5003 III 0.098 20 119.2696 34.08951 2781 53 III 5015 III 0.047

[0191] According to the optimized genetic algorithm solution, the planning results of transportation energy facilities are shown in Table 4. Charging facilities are set up in highway service areas 7, 14, and 16. The number of DC charging piles and AC charging piles is determined based on interception acquisition and cost. The area is rich in light energy resources and is suitable for setting up solar photovoltaic panels to power electric vehicles. The capacity of solar photovoltaic panels is as follows, while the area is short of wind resources and no wind power generation facilities are set up.

[0192] Table 4 Results of highway transportation energy facility planning

[0193]

[0194] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for planning highway traffic energy facilities, characterized in that: The following steps are involved: S1: Determine the basic parameters of electric vehicles and highways; S2: Based on the basic parameters obtained in S1, the highway electric vehicle travel is simulated through the dynamic traffic model; S3: Based on the basic parameters obtained in S1 and the dynamic traffic model established in S2, the Monte Carlo method is used to simulate the spatiotemporal distribution of electric vehicle charging load; S4: Evaluate the potential and complementarity of wind and solar energy utilization in highway service areas; S5: Establish a transportation energy facility planning model, including: establishing objective functions and constraints; S6: Based on the spatiotemporal distribution and planning model of electric vehicle charging load, an improved genetic algorithm is used to solve the transportation energy facility planning scheme; In S6, the improved genetic algorithm is used to solve the transportation energy facility planning problem as follows: S6-1: Input the road network, travel matrix and vehicle parameters, and set the improved genetic algorithm parameters; S6-2: Generate the initial parent planning scheme randomly using genetic algorithm; S6-3: Generate new child planning schemes through selection, crossover and mutation operations, each of which includes a set of transportation energy facility sites and facility quantities; S6-4: Using the dynamic traffic simulation in S2 and the Monte Carlo simulation in S3, calculate the charging demand of electric vehicles in all parent and child planning schemes, and then use the transportation energy facility planning model established in S5 to calculate the cost of each parent and child planning scheme; S6-5: Compare the benefits of all parent and child planning schemes, and select the planning scheme with the highest benefit as the new parent planning scheme; S6-6: Determine whether the current evolutionary generation w has reached the maximum evolutionary generation. If so, output the planning scheme with the lowest cost; otherwise, the current evolutionary generation w = w+1, re-execute S6-4 until the maximum evolutionary generation is reached, and output the optimal transportation energy facility planning scheme.

2. A method for planning highway traffic energy facilities according to claim 1, characterized in that: In S1, the process of determining the basic parameters of electric vehicles and highways includes: S-1-1: Get the initial time of electric vehicles on the highway, sample the probability of the time when electric vehicles enter the toll station, and get the initial time when electric vehicles enter the highway; S-1-2: Get the starting and ending points of electric vehicles on the highway. The starting and ending points of electric vehicles are determined by the OD probability distribution matrix P n×n OK, OD n×n The distribution matrix is ​​converted into the OD probability distribution matrix P n×n ; Where: p ij is the travel probability from point i and point j; q ij is the traffic flow from point i to point j; S-1-3: Obtain the battery power of the electric vehicle on the highway, including the initial power, change power, and charging power. The battery power is represented by SOC (State of charge), where SOC is the ratio of the current battery power of the electric vehicle to the fully charged battery power.

3. A highway traffic energy facility planning method according to claim 2, characterized in that: In S1, the process of obtaining the initial power, the change power, and the charging power includes: S-1-3-1: Get the initial SOC of the electric vehicle: Where: SOC0 is the initial SOC of the electric vehicle, μ is the expected value of the initial SOC of the electric vehicle entering the highway; σ is the standard deviation of the SOC0 of the electric vehicle; S-1-3-2: Get the changing SOC of electric vehicles: Where: E t is the battery capacity of the electric vehicle; P ev Energy consumption for electric vehicles; S-1-3-3: Get the charging SOC of the electric vehicle: Where: T c Charging time for electric vehicles; SOC e is the desired target SOC; SOC t is the current SOC; sc is the charging efficiency; P sc is the charging power, E ev The battery is fully charged.

4. A method for planning highway traffic energy facilities according to claim 3, characterized in that: In S2, the process of simulating the travel of electric vehicles on highways through the dynamic traffic model is as follows: the link transmission model (LTM) is used to model the spatiotemporal distribution of electric vehicles on highways and calculate the changes in traffic flow on highways, including: (1) Constructing the road segment model receiving flow This includes obtaining the receiving capacity of the road section R(t). In the road section transmission model, the receiving capacity of the road section refers to the maximum number of vehicles that enter the road section within the period [t, t+Δt]. The receiving flow is affected by the downstream boundary condition x L The constraint of , the receiving capacity of the road section R(t) is as follows: Among them, x0 is the upstream boundary; x L is the downstream boundary; L is the distance between the upstream and downstream sections; w is the speed corresponding to the vehicle exceeding the maximum capacity of the section; k j is the congestion density; q m is the maximum capacity; (2) Constructing the road segment model sending flow This includes obtaining the section sending capacity S(t), which refers to the maximum number of vehicles that leave a section within the period [t, t+Δt]. The sending flow is constrained by the boundary condition x0 upstream of the section. The section sending capacity S(t) is: Among them, v f is the free flow speed.

5. A method for planning highway traffic energy facilities according to claim 4, characterized in that: In S3, the process of simulating the spatiotemporal distribution of electric vehicle charging load using the Monte Carlo method includes: S3-1: Obtain the basic parameters of the determined highway network and electric vehicles; S3-2: Based on the OD probability distribution matrix determined in S-1-2 and the probability distribution of the time when electric vehicles enter the highway, the Monte Carlo method is used to assign the starting and ending points and the initial departure time to each electric vehicle, and the driving path and mileage of the electric vehicle on the highway are obtained; S3-3: Determine the initial SOC and initial power of the electric vehicle through the SOC probability distribution model constructed in the initial S-1-3-1; S3-4: Use the LTM road section model constructed in S2 to simulate the driving of electric vehicles on the highway, calculate the power consumption of electric vehicles during driving, and output the power consumption, remaining power and charging strategy of electric vehicles; S3-5: Using the results output by S3-4, select the appropriate charging strategy, determine the charging efficiency, charging power and charging time, output the spatiotemporal coordinates of the charging demand, and predict the spatiotemporal distribution of the electric vehicle charging load.

6. A method for planning highway traffic energy facilities according to claim 1, characterized in that: In S4, resource richness and correlation analysis are used to assess energy utilization potential and complementarity, namely: S4-1: Conduct wind energy resource abundance assessment: Use effective wind power density (EWPD) for assessment. Where: EWPD is the effective wind power density, W / m 2 ; n is the number of hourly wind speeds within the effective wind speed range; ρ is the air density, kg / m 3 ;v i The hourly wind speed is between [3,20] m / s; S4-2: Conduct a light energy resource richness assessment, using the ERA5 database to obtain the annual total solar horizontal radiation and evaluate the solar energy resource richness; S4-3: Conduct an assessment of the wind-solar complementarity of highways.

7. A method for planning highway traffic energy facilities according to claim 1, characterized in that: In S4, the Kendall correlation coefficient is used to measure the complementarity of wind and solar energy, where: (P w (t1),P s (t1)) and (P w (t2),P s (t2)) are wind energy time series P w (t) and light energy time series P s For two independent pairs in (t), the Kendall correlation coefficient is: τ=P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))>0} -P{(P w (t1)-P w (t2))(P s (t1)-P s (t2))<0} Where: τ is the Kendall correlation coefficient; P is the probability of the event.

8. A method for planning highway traffic energy facilities according to claim 1, characterized in that: In S5, establishing the transportation energy facility planning model specifically includes constructing an objective function, which is: S5-1-1: Construct the maximum interception objective function: Where: L is the set of all path ODs, l∈L; f l is the charging demand of electric vehicles on road section l; y l Indicates that if the charging demand of electric vehicles on road section l is obtained, it is 1, otherwise it is 0; S5-1-2: Construction and operation cost minimization objective function minC=C cstr +C opm Where: C cstr The construction investment cost of transportation and energy facilities; C opm Operation and maintenance costs of transportation energy facilities; Where: k is the candidate point for building a charging station; m k is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; The cost of building an AC charging station; The cost of building a DC charging station; The cost of building wind power facilities; is the cost of building photovoltaic power generation facilities; x k ,y k and z k It is 1 if it is built at node k, otherwise it is 0; In the formula: ζ is the conversion coefficient; r0 is the discount rate; for the proceeds of constructing wind power facilities; For the income from the construction of photovoltaic power generation facilities.

9. A method for planning highway traffic energy facilities according to claim 8, characterized in that: In S5, establishing the transportation energy facility planning model also includes constructing constraints, which are: S5-2-1: Establish a distance constraint for charging stations. The distance between adjacent charging stations should be less than the driving range of the electric vehicle: l min ≤d ij ≤l max Where: d ij is the distance to transportation and energy facilities; l min is the minimum spacing; l max is the maximum spacing; S5-2-2: Establish facility service capacity constraints, and the facility service capacity meets the interception demand: f l y l ≤I k x k Where: I k is the maximum capacity that candidate site k can bear; S5-2-3: The basic constraints for establishing the classic interception site selection model require that there must be a set of site selection points that meet the requirements: S5-2-4: Establish logical constraints on transportation energy facilities. When no transportation energy facilities are built at k, charging piles m and n are not built at the same time: m+n≤x k M Where: M is a preset maximum value; S5-2-5: Establish capacity constraints on wind and solar energy facilities: Where: The capacity of the wind power facility; is the maximum capacity of the wind power facility; is the capacity of the photovoltaic power generation facility; is the maximum capacity of the photovoltaic power generation facility; S5-2-7: Establish capacity constraints for charging facilities, taking into account the maximum hourly charging demand capacity: Where: m k is the number of AC charging piles built at candidate point k; n k is the number of DC charging piles built at candidate point k; ac is the charging power of a single AC charging pile; p dc The charging power of a single DC charging pile; is the maximum hourly charging demand capacity of the electric vehicle at candidate point k.

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