An optimization method for site selection and capacity determination of electric bus charging stations

Abstract

An optimization method for site selection and sizing of electric bus charging stations, which belongs to the technical field of electric bus charging station planning and operation. The present invention solves the problem of poor temperature adaptability of existing electric bus charging station site selection and sizing optimization methods. The present invention provides a two-stage optimization method for site selection and sizing of electric bus charging stations in low-temperature environments in cold regions, constructs a progressive coverage site selection model for charging stations in cold regions to determine the optimal site selection location for charging stations, and based on the charging station site selection results, establishes a charging station sizing planning model to determine the capacity setting plan for each charging station. Since the two-stage optimization method for site selection and sizing of the present invention is still applicable in low-temperature environments in cold regions, the temperature adaptability of the method is improved. The method of the present invention can be applied to site selection and sizing optimization of electric bus charging stations.

Description

Technical Field

[0001] The present invention belongs to the technical field of planning and operating electric bus charging stations, and in particular relates to an optimization method for site selection and capacity determination of electric bus charging stations. Background Art

[0002] At present, many cities in my country are vigorously developing electric buses as an important means to improve the safety and comfort of public transportation services, improve the experience of public transportation, increase the attractiveness of public transportation, and guide the public to travel green. Compared with ordinary fuel buses, the use of electric buses has greatly reduced carbon emissions and environmental pollution, but they also have many problems, such as short driving range on a single charge, too fast decline in vehicle technical performance, and the safety performance of power batteries still needs to be improved. At the same time, the temperature in the cold cities in northern my country is relatively low in winter. The low temperature environment will cause the activity of power batteries to decrease, the battery has power but cannot be released, the capacity will decay, and the energy consumption of electric buses will increase in low temperature conditions. These problems will cause the endurance of electric buses to drop significantly, the driving range on a single charge is short, and it cannot meet the needs of all-day operation, and frequent replenishment of electricity is required. As the main medium for energy replenishment of electric buses, the site selection and capacity optimization of electric bus charging stations in cold areas and low temperature environments have become issues that need to be studied and solved urgently.

[0003] In the aspect of charging station site selection and planning, scholars mainly construct site selection algorithms and models based on service facility site selection models, interception site selection models and multi-objective combined optimization site selection methods; the capacity planning of charging stations is generally based on the site selection planning results to consider meeting the charging needs of electric buses and minimizing the overall social cost. In addition, scholars have begun to widely use queuing theory methods and construct charging station capacity planning models based on parameters such as charging waiting time. However, in the current research on the site selection and capacity planning of electric bus charging stations, the impact of the low temperature environment in cold areas is rarely considered, resulting in poor adaptability of the charging station planning results to cold areas, and the location selection and capacity setting do not match the charging needs of electric buses. Some studies on charging station planning in the context of cold-temperature environments in cold areas simply increase the number of charging vehicles in low-temperature environments, ignoring the actual charging needs of electric buses after the low-temperature capacity of power batteries in cold areas is attenuated, and lack of quantitative analysis of the impact of the low-temperature environment in cold areas, resulting in the loss of reference value of charging station planning results.

[0004] In summary, the existing methods for site selection and sizing of electric bus charging stations have poor temperature adaptability. Therefore, it is necessary to propose an optimization method for site selection and sizing of electric bus charging stations suitable for low temperature environments in cold areas. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of poor temperature adaptability of existing electric bus charging station site selection and capacity optimization methods, and to propose an electric bus charging station site selection and capacity optimization method.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] An optimization method for site selection and capacity determination of an electric bus charging station, the method specifically comprising the following steps:

[0008] Step S1, respectively obtain the starting station, intermediate stop station and terminal station of each bus line in the planning area, then delete the duplicate stations from all the stations obtained, and use the remaining stations to form a charging demand point set; then select the candidate charging stations from the charging demand point set;

[0009] Step S2: Calculate the probability P(d) that the i-th charging demand point selects the j-th candidate charging station according to the distance between the i-th charging demand point and the j-th candidate charging station. ij );

[0010] Step S3: constructing an electric bus power battery capacity attenuation model based on the Arrhenius formula;

[0011] Step S4: Calculate the effective capacity C of the power battery according to the electric bus power battery capacity attenuation model e ;

[0012] Step S5: According to the effective capacity C of the power battery e Calculate the number of times a single electric bus is charged per day K d ;

[0013] Step S6: According to the probability P(d ij ) and the number of daily charging times for a single electric bus K d Establish a model for the progressive coverage of charging stations;

[0014] Step S7: After determining the charging stations according to the established charging station progressive coverage site selection model, calculate the average waiting time of the electric bus at each charging station and the probability of entering each charging station to queue up and wait;

[0015] Step S8: Establish an electric bus charging station capacity planning model and model constraints based on the result of step S7, and then use a genetic algorithm to solve the model to obtain the result of the electric bus charging station capacity planning.

[0016] Furthermore, the step of selecting candidate charging stations from the charging demand point set is specifically as follows:

[0017] For any charging demand point, whether to use it as an alternative charging station is determined based on the traffic convenience, vehicle capacity, charging facility construction and power grid capacity limit of the sub-area where the charging demand point is located.

[0018] Furthermore, the probability P(d ij ) is calculated as:

[0019]

[0020] Among them, d ij is the straight-line distance between the jth alternative charging station and the ith charging demand point within the service range, R min is the minimum service radius of the charging station, R max is the maximum service radius of the charging station.

[0021] Furthermore, the specific process of step S3 is as follows:

[0022] Arrhenius equation is used to express the chemical reaction rate:

[0023]

[0024] Where k is the chemical reaction rate constant, A is the pre-exponential factor, e is the base of the natural logarithm, and E a is the apparent activation energy, R is the molar gas constant, and T is the battery temperature;

[0025] According to formula (2), the capacity attenuation model of the electric bus power battery is constructed as follows:

[0026]

[0027] Among them, Q loss is the battery capacity attenuation rate, A is the pre-exponential factor, t is the battery cycle time, z is the power exponential factor, and ln is the logarithm with the natural logarithm e as the base.

[0028] Furthermore, the specific process of step S4 is as follows:

[0029] C e =C·(1-Q loss -15%) (4)

[0030] Where: C is the initial capacity of the electric bus power battery, C e is the effective capacity of the power battery.

[0031] Furthermore, the specific process of step S5 is as follows:

[0032]

[0033] Among them, Kd The number of times a single electric bus is charged per day, U e is the power consumption per unit mileage of the electric bus, L e The daily operating route mileage of electric buses.

[0034] Furthermore, the charging station progressive coverage site selection model is:

[0035]

[0036] Among them, Z e is the idle cost of the electric bus during its life cycle, I is the set of charging demand points, J is the set of alternative charging stations, x j Indicates whether to build a charging station at the jth candidate charging station. If a charging station is built at the jth candidate charging station, then x j =1, otherwise x j =0; N i represents the number of electric buses with the i-th charging demand point as the station, f e is the unit mileage cost of an electric bus running idle, ξ is the nonlinear coefficient of urban roads, and T′ is the life cycle of an electric bus.

[0037] Furthermore, the constraints of the charging station progressive coverage site selection model are as follows:

[0038]

[0039]

[0040]

[0041] Among them, W i ″ is the demand of the i-th charging demand point; V j is the service capacity of the jth alternative charging station; is the minimum capacity of the charging station; is the maximum capacity of the charging station;

[0042]

[0043] Among them, J max The maximum number of charging stations for electric buses to be built.

[0044] Furthermore, the average waiting time of the electric bus at each charging station and the probability of entering each charging station to queue up are calculated as follows:

[0045] Step S71: Using the electric bus truncated queue theory model, the process of the electric bus entering the jth charging station to queue for charging is described as follows:

[0046]

[0047] Among them, λ j is the average rate at which electric buses arrive at the jth charging station, μ is the average service rate of the charging station, and P 1,j is the probability that an electric bus receives charging service at the jth charging station, P 0,j is the probability that no electric bus receives charging service at the jth charging station, n is the number of electric buses at the jth charging station, s j is the number of chargers in the jth electric bus charging station, P n+1,j is the probability that n+1 electric buses receive charging services at the jth charging station, P n-1,j is the probability that n-1 electric buses accept charging services at the charging station, P n,j is the probability that n electric buses receive charging services at the jth charging station, N j is the maximum number of electric buses that can be accommodated by the jth charging station;

[0048] The average service rate of the charging station is:

[0049]

[0050] Among them, X p is the average charging power of the charger, is the charger efficiency;

[0051] The average arrival rate of the jth charging station is:

[0052]

[0053] Among them, N′ j is the total number of electric buses within the service area of ​​the jth charging station, t e is the daily operating hours of the electric bus;

[0054] Depend on have to:

[0055]

[0056] Among them, ρ j is the service intensity of the jth electric bus charging station;

[0057]

[0058] Step S72: According to P 0,j and λ j Calculate the proportion of late-arriving electric buses that are in a state of being denied service when the vehicle capacity of the jth charging station is full, which is P′ e,j :

[0059]

[0060] The effective arrival rate λ of the jth electric bus charging station e,j for:

[0061] λ e,j =λ j (1-P′ e,j ) (17)

[0062] The effective service intensity ρ′ of the jth electric bus charging station e,j for:

[0063] ρ′ e,j =ρ j (1-P′ e,j ) (18)

[0064] Step S73: Average queue length L at the jth electric bus charging station j for:

[0065]

[0066] The average waiting time W of an electric bus at the jth charging station j for:

[0067]

[0068] The probability of an electric bus entering the jth charging station and waiting in line for:

[0069]

[0070] Furthermore, the electric bus charging station capacity planning model is:

[0071] Z s =Z cc +Z co +Z cl +Z et (twenty two)

[0072] Among them, Z s is the comprehensive social cost during the life cycle; Z cc is the construction cost of the charging station life cycle; Z co is the life cycle operating cost of the charging station; Z cl is the network loss cost of the charging station during its life cycle; Z et The life cycle time cost of an electric bus within the service area of ​​a charging station;

[0073]

[0074] Among them, M s is the purchase cost of a single charger, θ is the equivalent coefficient of the cost of supporting facilities for the charger, O s is the construction cost of arranging the charger, r0 is the discount rate, b is the depreciation period of the charging station; Y is the planned period of the charging station;

[0075] Z co =U(Y)·(M s +θ·M s +O s )·s j ·Y (24)

[0076] Among them, U(Y) is the operating cost function of the planned life of the charging station;

[0077] Z cl =(W l ′+W c ′)·s j ·κ·t e ·P·365·Y (25)

[0078] Among them, W l ' is the power loss of the charger line; W c ′ is the charging power loss; κ is the simultaneous use coefficient of the charger; P is the electricity purchase cost of the charging station;

[0079]

[0080] Among them, I j is the number of charging demand points within the service range of the jth charging station; v is the driving speed of the electric bus to the charging station; C d is the unit time cost of the electric bus driver; N ji The number of electric buses at the i-th charging demand point within the service range of the j-th charging station;

[0081] The electric bus charging station capacity planning model needs to satisfy the constraints of equations (27) to (30):

[0082] The effective service intensity of the jth charging station should be less than 1, that is:

[0083] ρ′ e,j <1 (27)

[0084] The service rejection rate of the jth charging station should be less than or equal to 10%, that is:

[0085] P′ e,j ≤10% (28)

[0086] The charging satisfaction of the jth electric bus should be greater than or equal to 90%, that is:

[0087]

[0088] Among them, W max is the maximum waiting time of electric buses; coefficients ω and υ are weight coefficients;

[0089] Limit the number of chargers in the jth charging station:

[0090] 0<s j ≤γ·N′ j (30)

[0091] Among them, γ is the upper limit parameter of the number of chargers.

[0092] The beneficial effects of the present invention are:

[0093] The present invention provides a two-stage optimization method for site selection and capacity determination of electric bus charging stations in cold regions and low temperature environments, constructs a progressive coverage site selection model for charging stations in cold regions to determine the optimal site selection location of charging stations, and establishes a charging station capacity planning model based on the site selection results to determine the capacity setting plan for each charging station. Since the two-stage optimization method for site selection and capacity determination of the present invention is still applicable in cold regions and low temperature environments, the temperature adaptability of the method is improved.

[0094] In the site selection and planning stage of charging stations for electric buses in cold regions, a charging station progressive coverage service function is constructed to calculate the probability of selecting each alternative charging station at the charging demand point, which conforms to the actual situation in cold regions and can maximize the charging needs of electric buses in low temperature environments in cold regions. The charging needs of electric buses are determined based on the capacity attenuation rate of power batteries in low temperature environments in cold regions, and the actual average daily charging times of electric buses in cold regions are obtained. With the goal of minimizing the idle running cost of electric buses throughout their life cycle, a cold region charging station progressive coverage site selection model is constructed to determine the optimal charging station location, reduce the idle running and charging time costs of electric buses, and improve the charging satisfaction of electric buses in cold regions.

[0095] In the capacity planning stage of electric bus charging stations in cold regions, the capacity attenuation rate of power batteries due to low temperatures in cold regions is taken into account in the charging demand of electric buses, which is more in line with the actual operation of electric buses in low temperature environments in cold regions. Based on the truncated queuing theory model with limited capacity, the effective service intensity, refusal of service rate, charging satisfaction and the maximum number of chargers of charging stations are established to improve the effective utilization rate of charging resources and the attractiveness of charging stations. Taking into account the interests of both charging stations and electric buses, with the goal of minimizing the total social cost during the life cycle of the charging station, a capacity planning model for charging stations in cold regions is constructed, and the genetic algorithm is used to solve the model to obtain the optimal capacity setting plan for the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 is a flow chart of the method of the present invention;

[0097] Figure 2 A distribution map of the locations of the candidate electric bus charging stations of the present invention;

[0098] Figure 3 A progressive coverage function diagram of the electric bus charging station site selection planning method of the present invention;

[0099] Figure 4 The daily operation and charging scheduling diagram of the electric bus according to the method of the present invention;

[0100] Figure 5 The results of site selection for electric bus charging stations and the location distribution map of the attached charging demand points according to the method of the present invention are shown. DETAILED DESCRIPTION

[0101] Specific implementation method 1. Combination Figure 1 The present embodiment describes an optimization method for site selection and capacity determination of an electric bus charging station, the method specifically comprising the following steps:

[0102] Step S1, respectively obtain the starting station, intermediate stop station and terminal station of each bus line in the planning area, then delete the duplicate stations from all the stations obtained, and use the remaining stations to form a charging demand point set; then select the candidate charging stations from the charging demand point set;

[0103] Step S2: Calculate the probability P(d) that the i-th charging demand point selects the j-th candidate charging station according to the distance between the i-th charging demand point and the j-th candidate charging station. ij );

[0104] Step S3: constructing an electric bus power battery capacity attenuation model based on the Arrhenius formula;

[0105] Step S4: Calculate the effective capacity C of the power battery according to the electric bus power battery capacity attenuation model e ;

[0106] Step S5: According to the effective capacity C of the power battery e Calculate the number of times a single electric bus is charged per day K d ;

[0107] Step S6: According to the probability P(d ij ) and the number of daily charging times for a single electric bus K d Establish a model for the progressive coverage of charging stations;

[0108] Step S7: After determining the charging stations according to the established charging station progressive coverage site selection model, calculate the average waiting time of the electric bus at each charging station and the probability of entering each charging station to queue up and wait;

[0109] Step S8: Establish an electric bus charging station capacity planning model and model constraints based on the result of step S7, and then use a genetic algorithm to solve the model to obtain the result of the electric bus charging station capacity planning.

[0110] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that the candidate charging stations are selected from the charging demand point set, specifically:

[0111] For any charging demand point, whether to use it as an alternative charging station is determined based on the traffic convenience, vehicle capacity, charging facility construction and power grid capacity limit of the sub-area where the charging demand point is located.

[0112] The other steps and parameters are the same as those in the first embodiment.

[0113] Investigate whether the traffic around each charging demand point is convenient, whether the grid capacity limit of the area is met, whether there is enough free land to store vehicles, and the construction of charging infrastructure, etc., set the parameter weights of each charging demand point, as shown in Table 1, and select the charging demand points with the sum of weights greater than 2 as candidate charging stations.

[0114] Table 1 Parameter weight settings of alternative charging stations

[0115]

[0116]

[0117] The specific explanation of Table 1 is as follows:

[0118] If the road beside the charging demand point is a branch road, the parameter weight is assigned to 0; if the road beside the charging demand point is a secondary trunk road, the parameter weight is assigned to 0.5; if the road beside the charging demand point is a main trunk road, the parameter weight is assigned to 1. If the number of vehicles that the sub-area where the charging demand point is located can accommodate is [0,10), the parameter weight is assigned to 0; if the number of vehicles that the sub-area where the charging demand point is located can accommodate is [10,30], the parameter weight is assigned to 0.5; if the number of vehicles that the sub-area where the charging demand point is located can accommodate is (30,60), the parameter weight is assigned to 1. If the upper limit of the power grid capacity of the sub-area where the charging demand point is located is [0,500), the parameter weight is assigned to 0; if the upper limit of the power grid capacity of the sub-area where the charging demand point is located is [500,1000), the parameter weight is assigned to 0.5; if the upper limit of the power grid capacity of the sub-area where the charging demand point is located is [1000,2000), the parameter weight is assigned to 1. If there is no energy supplement facility in the sub-area where the charging demand point is located, the parameter weight is assigned to 0. If the capacity of the energy supplement facility in the sub-area where the charging demand point is located is between [0,50)KVA, the parameter weight is assigned to 0.5. If the capacity of the energy supplement facility in the sub-area where the charging demand point is located is greater than or equal to 50KVA, the parameter weight is assigned to 1.

[0119] For a certain charging demand point, the weights of various parameters corresponding to the charging demand point are summed up. If the sum result is greater than 2, the charging demand point can be used as an alternative charging station.

[0120] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the probability P(d ij ) is calculated as:

[0121]

[0122] Among them, d ij is the straight-line distance between the jth alternative charging station and the ith charging demand point within the service range, R min is the minimum service radius of the charging station, R max is the maximum service radius of the charging station.

[0123] The other steps and parameters are the same as those in the first or second embodiment.

[0124] The selection probability of this implementation mode takes into account the impact of the low temperature environment in cold regions on the service range of the charging station.

[0125] Specific implementation method 4: This implementation method is different from any one of the specific implementation methods 1 to 3 in that the specific process of step S3 is as follows:

[0126] Arrhenius equation is used to express the chemical reaction rate:

[0127]

[0128] Where k is the chemical reaction rate constant, A is the pre-exponential factor, e is the base of the natural logarithm, and E a is the apparent activation energy, R is the molar gas constant, and T is the battery temperature;

[0129] According to formula (2), the capacity attenuation model of the electric bus power battery is constructed as follows:

[0130]

[0131] Among them, Q loss is the battery capacity attenuation rate, A is the pre-exponential factor, t is the battery cycle time (regarded as a constant), z is the power exponential factor, and ln is the logarithm with the natural logarithm e as the base.

[0132] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0133] This embodiment takes into account the effect of the ambient temperature in cold regions on the capacity decay of the power battery.

[0134] Specific implementation mode 5: This implementation mode is different from the specific implementation modes 1 to 4 in that the specific process of step S4 is as follows:

[0135] C e =C·(1-Q loss -15%) (4)

[0136] Where: C is the initial capacity of the electric bus power battery, C e is the effective capacity of the power battery.

[0137] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0138] Electric buses return to charging stations at night for centralized charging. Assuming that the power battery state of charge SOC (State of Charge) of the electric bus is 100% before daily operation, in order to ensure the battery technical performance, it is stipulated that the power battery must retain 15% of its capacity.

[0139] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that the specific process of step S5 is as follows:

[0140]

[0141] Among them, K d The number of times a single electric bus is charged per day, U e is the power consumption per unit mileage of the electric bus, L eThe daily operating route mileage of electric buses.

[0142] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0143] Since the power battery has limited capacity and is easily affected by the low temperature environment in cold areas, resulting in capacity attenuation, the driving range on a single charge is short and cannot meet the needs of all-day operation. Additional power is required, so it is necessary to determine the charging needs of electric buses.

[0144] Specific implementation method 7: This implementation method is different from any one of specific implementation methods 1 to 6 in that the charging station progressive coverage site selection model is:

[0145]

[0146] Among them, Z e is the idle cost of the electric bus during its life cycle, I is the set of charging demand points, J is the set of alternative charging stations, x j Indicates whether to build a charging station at the jth candidate charging station. If a charging station is built at the jth candidate charging station, then x j =1, otherwise x j =0; N i represents the number of electric buses with the i-th charging demand point as the station, f e is the unit mileage cost of an electric bus running idle, ξ is the nonlinear coefficient of urban roads, and T′ is the life cycle of an electric bus.

[0147] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0148] The model of this embodiment takes the minimum idle cost of the electric bus during its life cycle as the optimization goal, and takes the coverage rate of charging demand points and the capacity limit of charging stations as constraints. The model is solved to obtain the location of the charging station and the attached charging demand points.

[0149] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the constraints of the charging station progressive coverage site selection model are equations (7) to (10):

[0150]

[0151]

[0152]

[0153] Among them, W i ″ is the demand of the i-th charging demand point, calculated according to the number of owned electric buses; V j is the service capacity of the jth alternative charging station; is the minimum capacity of the charging station, calculated based on the number of electric buses owned by the charging station; The maximum capacity of the charging station is calculated based on the number of electric buses that the charging station can accommodate;

[0154]

[0155] Among them, J max The maximum number of charging stations for electric buses to be built.

[0156] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0157] Formula (7) indicates that the charging demand of any charging demand point can be met, that is, the sum of the coverage rates of each charging demand point is greater than or equal to 1. The constraints of formulas (8) and (9) indicate that the total demand of all charging demand points served by each charging station does not exceed the capacity of the charging station. The constraint of formula (10) indicates that in order to reduce the construction cost of charging stations, the number of charging stations to be built is limited.

[0158] Specific implementation method 9: This implementation method is different from any one of specific implementation methods 1 to 8 in that the average waiting time of the electric bus at each charging station and the probability of entering each charging station to queue up are calculated as follows:

[0159] Step S71: The charging station occupies a certain area, and the electric bus is large in size, so the number of electric buses that can be accommodated in each charging station is limited. Using the electric bus truncated queue theory model with limited service capacity, the process of electric buses entering the jth charging station to queue for charging is described as follows:

[0160]

[0161] Among them, λ j is the average rate at which electric buses arrive at the jth charging station, μ is the average service rate of the charging station (the average service rate of each charging station is the same), P 1,j is the probability that an electric bus receives charging service at the jth charging station, P 0,j is the probability that no electric bus receives charging service at the jth charging station, n is the number of electric buses at the jth charging station, s j is the number of chargers in the jth electric bus charging station, P n+1,j is the probability that n+1 electric buses receive charging services at the jth charging station, P n-1,j is the probability that n-1 electric buses accept charging services at the charging station, P n,j is the probability that n electric buses receive charging services at the jth charging station, N j is the maximum number of electric buses that the jth charging station can accommodate;

[0162] The average service rate of the charging station is:

[0163]

[0164] Among them, X p is the average charging power of the charger, is the charger efficiency;

[0165] The average arrival rate of the jth charging station is:

[0166]

[0167] Among them, N′ j is the total number of electric buses within the service area of ​​the jth charging station, t e is the daily operating hours of the electric bus;

[0168] Depend on have to:

[0169]

[0170] Among them, ρ j is the service intensity of the jth electric bus charging station;

[0171]

[0172] Step S72: According to P 0,j and λ j Calculate the proportion of late-arriving electric buses that are in a state of being denied service when the vehicle capacity of the jth charging station is full, which is P′ e,j :

[0173]

[0174] The effective arrival rate λ of the jth electric bus charging station e,j for:

[0175] λ e,j =λ j (1-P′ e,j ) (17)

[0176] The effective service intensity ρ′ of the jth electric bus charging station e ,j is:

[0177] ρ′ e,j =ρ j (1-P′ e,j ) (18)

[0178] Step S73: Average queue length L at the jth electric bus charging station jfor:

[0179]

[0180] The average waiting time W of an electric bus at the jth charging station j for:

[0181]

[0182] The probability of an electric bus entering the jth charging station and waiting in line for:

[0183]

[0184] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0185] Specific implementation method 10: This implementation method is different from any one of specific implementation methods 1 to 9 in that the electric bus charging station capacity planning model is:

[0186] Z s =Z cc +Z co +Z cl +Z et (twenty two)

[0187] Among them, Z s is the comprehensive social cost during the life cycle; Z cc is the construction cost of the charging station life cycle; Z co is the life cycle operating cost of the charging station; Z cl is the network loss cost of the charging station during its life cycle; Z et The life cycle time cost of an electric bus within the service area of ​​a charging station;

[0188] The construction cost of a charging station includes the purchase cost of the charger and its ancillary equipment and the civil construction cost. The calculation formula is as follows:

[0189]

[0190] Among them, M s is the purchase cost of a single charger, θ is the equivalent coefficient of the cost of supporting facilities for the charger, O s is the construction cost of arranging the charger, r0 is the discount rate, b is the depreciation period of the charging station; Y is the planned period of the charging station;

[0191] The operating costs of charging stations include daily operating and maintenance costs of equipment and staff wages. The calculation formula is as follows:

[0192] Z co =U(Y)·(M s +θ·Ms +O s )·s j ·Y (24)

[0193] Among them, U(Y) is the operating cost function of the planned life of the charging station;

[0194] The network loss cost of the charging station includes the charger line loss cost and the charging power loss cost. The calculation formula is as follows:

[0195] Z cl =(W l ′+W c ′)·s j ·κ·t e ·P·365·Y (25)

[0196] Among them, W l ' is the power loss of the charger line; W c ′ is the charging power loss; κ is the simultaneous use coefficient of the charger; P is the electricity purchase cost of the charging station;

[0197] The time cost of electric buses includes the cost of idling time and the cost of waiting in line for charging. The calculation formula is as follows:

[0198]

[0199] Among them, I j is the number of charging demand points within the service range of the jth charging station; v is the driving speed of the electric bus to the charging station; C d is the unit time cost of the electric bus driver, which is equal to the ratio of his salary to working time; N ji The number of electric buses at the i-th charging demand point within the service range of the j-th charging station;

[0200] The electric bus charging station capacity planning model needs to satisfy the constraints of equations (27) to (30):

[0201] To ensure the service quality of the charging station, the effective service intensity of the jth charging station should be less than 1, that is:

[0202] ρ′ e,j <1 (27)

[0203] To ensure the service quality of the charging station, the service rejection rate of the jth charging station should be less than or equal to 10%, that is:

[0204] P′ e,j ≤10% (28)

[0205] To ensure the service quality of the charging station, the charging satisfaction of the jth electric bus should be greater than or equal to 90%, that is:

[0206]

[0207] Among them, W max is the maximum acceptable waiting time for electric buses; coefficients ω and υ are weight coefficients, satisfying ω+υ=1;

[0208] In order to maximize the utilization of charging resources, the number of chargers in the jth charging station is limited:

[0209] 0<s j ≤γ·N′ j (30)

[0210] Among them, γ is the upper limit parameter of the number of chargers, and its value is (0,1), that is, 0<γ<1.

[0211] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0212] This implementation method is based on constraints such as the effective service intensity of electric bus charging stations, service refusal rate, charging satisfaction and the maximum number of chargers, taking into account the interests of both charging stations and electric buses, and aims to minimize the total cost of charging station construction, operation and network loss, and electric bus queuing and idle time during the life cycle of the charging station. A capacity planning model for electric bus charging stations is constructed, and the planning model can be applied to cold regions.

[0213] After establishing a capacity planning model for each charging station using the method of this embodiment, the capacity planning model corresponding to each charging station is solved using a genetic algorithm to obtain the number of chargers that should be installed in each charging station, the four costs corresponding to each charging station, and the minimum total social cost.

[0214] In order to verify the practicability of the two-stage optimization technical method for site selection and capacity determination of electric bus charging stations in cold regions and low temperature environments of the present invention, the present invention conducts tests on electric bus charging stations in the urban area of ​​Harbin as the research object.

[0215] Step 1: A total of 196 bus routes in Harbin were surveyed, duplicate bus stops were removed, charging demand points were screened out, the influencing factors of charging station construction in cold regions were considered, parameter weights of each charging demand point were assigned, and 46 bus terminals or stations with a weight value greater than 2 were screened out as candidate charging stations, such as Figure 2 As shown, the five-pointed star in the figure is the location of the alternative charging station.

[0216] Step 2: Use online map software to measure the straight-line distance between the candidate charging stations and construct the distance matrix D ij Based on the progressive coverage function of charging stations in formula (1), the probability of electric bus choosing each alternative charging station P(d ij ). The function graph is as follows Figure 3 As shown in the figure, when the distance between the charging demand point and the charging station is less than or equal to R min When the charging demand point is completely covered by the charging station, the probability of selecting the charging station is 1; when the distance between the charging demand point and the charging station is R min With R max When the distance between the charging demand point and the charging station is greater than or equal to R, the charging demand point is partially covered by the charging station, and the probability of selecting the charging station is between 0 and 1, and decreases with the increase of the distance; when the distance between the charging demand point and the charging station is greater than or equal to R max When the charging demand point is outside the coverage of the charging station, the probability of selecting the charging station is 0.

[0217] Step 3: Fit the parameters in formula (3) based on the measured power battery capacity attenuation rate and the low temperature environment temperature data in the cold region, and obtain the attenuation model of the power battery capacity changing with temperature in the cold region low temperature environment:

[0218]

[0219] The average minimum temperature in three months of winter in Harbin is investigated, as shown in Table 2. The present invention uses the average value -19.35°C as the typical ambient temperature in Harbin in winter. The capacity attenuation rate Q of the power battery of electric buses in Harbin in winter can be obtained by formula (31): loss =15.67%.

[0220] Table 2 Average minimum temperature in winter in Harbin

[0221]

[0222] Step 4: Based on the daily line operation power consumption, power battery capacity and low-temperature capacity attenuation rate of a single electric bus, the daily charging times of the electric bus in cold regions are calculated by formula (5).

[0223] Step 5: Electric buses run on fixed routes every day according to a fixed schedule, and their operation is very regular. The operation and charging scheduling process of electric buses is as follows: Figure 4 As shown. Electric buses are charged at charging stations at night. Before the start of daytime operation, they run empty to the departure station or terminal station before the first departure task of the day according to the driving plan of each line to ensure on-time departure (the terminal station is the departure station of the opposite operating line). During operation, electric buses travel between the departure station and the terminal station through the operating line to carry passengers. When the power battery is not enough to support the next round-trip operating line, the electric bus needs to run empty from the departure station through the empty line to the corresponding charging station to replenish the power, and then drive empty back to the departure station to re-enter operation. For lines where the charging station and the departure station coincide, the empty driving distance between the two is 0.

[0224] Under the premise that each charging demand point is fully covered and the charging capacity of each charging station is limited, in order to improve the charging experience of electric buses, the cost of the idle time of electric buses going back and forth to the charging station during their life cycle is minimized as the optimization goal, and a gradual coverage site selection model for electric bus charging stations in cold regions is constructed. min =2.0km; R max =4.0km; U e =100kW·h; L e =160km; C = 150kW·h; f e =0.54 yuan / km;ξ=1.6;T′=5 years;J max = 10. The model is solved to obtain the location of 9 cold-region charging stations and the locations of charging demand points covered by each charging station, such as Figure 5 shown.

[0225] Step 6: Construct a truncated queueing theory model for electric buses with limited capacity, and improve the model-related parameters based on the electric bus refusal rate to obtain parameters such as the effective arrival rate of electric buses, the effective service intensity of charging stations, the average queue length, the average waiting time, and the probability of waiting in line. Model-related parameter settings: t e =14h;X p =170kW;

[0226] Step 7: Further establish the constraints and objective function of the cold region charging station capacity planning model. The constraints include the effective service intensity constraint of the charging station, the service refusal rate constraint of the electric bus, the charging satisfaction constraint of the electric bus, and the maximum number of chargers constraint; the objective function is to minimize the total social cost during the life cycle of the charging station, where the cost of the charging station side includes the construction, operation and network loss cost of the charging station, and the cost of the electric bus side includes the time cost of driving to the charging station empty and queuing for charging. Model related parameter settings: M s = 200,000 yuan; θ = 0.1; O s = 15,000 yuan; r0 = 0.08; b = 15 years; Y = 8 years; U(Y) = 0.15; W l =3.50kW; W c =2.30kW; P = 6.0×10 -5 10,000 yuan·(kW·h) -1 ;κ=0.8;C d =2.5×10 -3 10,000 yuan / h; W max =0.5h; ω=υ=0.5; γ=0.5. A genetic algorithm is designed according to the characteristics of the model, and the capacity planning model of each charging station in the cold region is solved in turn, and the number of chargers and cost results planned for each charging station are obtained, as shown in Table 3.

[0227] Table 3 Planned number and cost of chargers for each charging station in cold regions

[0228]

[0229] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. An optimization method for site selection and capacity determination of electric bus charging stations, It is characterized in that The method specifically comprises the following steps: Step S1, respectively obtain the starting station, intermediate stop station and terminal station of each bus line in the planning area, then delete the duplicate stations from all the stations obtained, and use the remaining stations to form a charging demand point set; then select the candidate charging stations from the charging demand point set; Step S2: Calculate the probability P(d) that the i-th charging demand point selects the j-th candidate charging station according to the distance between the i-th charging demand point and the j-th candidate charging station. ij ); The probability P(d ij ) is calculated as: Among them, d ij is the straight-line distance between the jth alternative charging station and the ith charging demand point within the service range, R min is the minimum service radius of the charging station, R max is the maximum service radius of the charging station; Step S3: constructing an electric bus power battery capacity attenuation model based on the Arrhenius formula; Step S4: Calculate the effective capacity C of the power battery according to the electric bus power battery capacity attenuation model e ; Step S5: According to the effective capacity C of the power battery e Calculate the number of daily charging times K for a single electric bus d ; The specific process of step S5 is as follows: Among them, K d The number of times a single electric bus is charged per day, U e is the power consumption per unit mileage of the electric bus, L e Daily operating route mileage for electric buses; Step S6: According to the probability P(d ij ) and the number of daily charging times for a single electric bus K d Establish a model for the progressive coverage of charging stations; The charging station progressive coverage site selection model is: Among them, Z e is the idle cost of the electric bus during its life cycle, I is the set of charging demand points, J is the set of alternative charging stations, x j Indicates whether to build a charging station at the jth candidate charging station. If a charging station is built at the jth candidate charging station, then x j =1, otherwise x j =0; N i represents the number of electric buses with the i-th charging demand point as the station, f e is the unit mileage cost of an electric bus running idle, ξ is the nonlinear coefficient of urban roads, and T′ is the life cycle of an electric bus; Step S7: After determining the charging stations according to the established charging station progressive coverage site selection model, calculate the average waiting time of the electric bus at each charging station and the probability of entering each charging station to queue up and wait; Step S8: Establish an electric bus charging station capacity planning model and model constraints based on the result of step S7, and then use a genetic algorithm to solve the model to obtain the result of the electric bus charging station capacity planning.

2. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 1, It is characterized in that The step of selecting candidate charging stations from the charging demand point set is as follows: For any charging demand point, whether to use it as an alternative charging station is determined based on the traffic convenience, vehicle capacity, charging facility construction and power grid capacity limit of the sub-area where the charging demand point is located.

3. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 2, It is characterized in that The specific process of step S3 is: Arrhenius equation is used to express the chemical reaction rate: Where k is the chemical reaction rate constant, A is the pre-exponential factor, e is the base of the natural logarithm, and E a is the apparent activation energy, R is the molar gas constant, and T is the battery temperature; According to formula (2), the capacity attenuation model of the electric bus power battery is constructed as follows: Among them, Q loss is the battery capacity attenuation rate, A is the pre-exponential factor, t is the battery cycle time, z is the power exponential factor, and ln is the logarithm with the natural logarithm e as the base.

4. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 3, It is characterized in that The specific process of step S4 is as follows: C e =C·(1-Q loss -15%) (4) Where: C is the initial capacity of the electric bus power battery, C e is the effective capacity of the power battery.

5. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 4, It is characterized in that The constraints of the charging station progressive coverage site selection model are as follows: Among them, W i ″ is the demand of the i-th charging demand point; V j is the service capacity of the jth alternative charging station; is the minimum capacity of the charging station; is the maximum capacity of the charging station; Among them, J max The maximum number of charging stations for electric buses to be built.

6. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 5, It is characterized in that The calculation of the average waiting time of electric buses at each charging station and the probability of entering each charging station to queue up is specifically: Step S71: Using the electric bus truncated queue theory model, the process of the electric bus entering the jth charging station to queue for charging is described as follows: Among them, λ j is the average rate at which electric buses arrive at the jth charging station, μ is the average service rate of the charging station, and P 1,j is the probability that an electric bus receives charging service at the jth charging station, P 0,j is the probability that no electric bus receives charging service at the jth charging station, n is the number of electric buses at the jth charging station, s j is the number of chargers in the jth electric bus charging station, P n+1,j is the probability that n+1 electric buses receive charging services at the jth charging station, P n-1,j is the probability that n-1 electric buses accept charging services at the charging station, P n,j is the probability that n electric buses receive charging services at the jth charging station, N j is the maximum number of electric buses that can be accommodated by the jth charging station; The average service rate of the charging station is: Among them, X p is the average charging power of the charger, θ is the efficiency of the charger; The average arrival rate of the jth charging station is: Among them, N j ′ is the total number of electric buses within the service range of the jth charging station, t e is the daily operating hours of the electric bus; Depend on have to: Among them, ρ j is the service intensity of the jth electric bus charging station; Step S72: According to P 0,j and λ j Calculate the proportion of late-arriving electric buses that are in a state of being denied service when the vehicle capacity of the jth charging station is full. e ' ,j : The effective arrival rate λ of the jth electric bus charging station e,j for: l e,j =λ j (1-P e ′ ,j ) (17) The effective service intensity ρ′ of the jth electric bus charging station e,j for: r e ′ ,j =ρ j (1-P e ′ ,j ) (18) Step S73: Average queue length L at the jth electric bus charging station j for: The average waiting time W of an electric bus at the jth charging station j for: The probability of an electric bus entering the jth charging station and waiting in line for:

7. The method for optimizing the site selection and capacity of an electric bus charging station according to claim 6, It is characterized in that The electric bus charging station capacity planning model is: minZ s =Z cc +Z co +Z cl +Z et (22) Among them, Z s is the comprehensive social cost during the life cycle; Z cc is the construction cost of the charging station life cycle; Z co is the life cycle operating cost of the charging station; Z cl is the network loss cost of the charging station during its life cycle; Z et The life cycle time cost of an electric bus within the service area of ​​a charging station; Among them, M s is the purchase cost of a single charger, θ is the equivalent coefficient of the cost of supporting facilities for the charger, O s The construction cost for arranging the charger is 0 is the discount rate, b is the depreciation period of the charging station; Y is the planned life of the charging station; Z co =U(Y)·(M s +θ·M s +O s )·s j ·Y (24) Among them, U(Y) is the operating cost function of the planned life of the charging station; Z cl =(W l ′+W c ′)·s j ·κ·t e ·P·365·Y (25) Among them, W l ' is the power loss of the charger line; W c ′ is the charging power loss; κ is the simultaneous use coefficient of the charger; P is the electricity purchase cost of the charging station; Among them, I j is the number of charging demand points within the service range of the jth charging station; v is the driving speed of the electric bus to the charging station; C d is the unit time cost of the electric bus driver; N ji The number of electric buses at the i-th charging demand point within the service range of the j-th charging station; The electric bus charging station capacity planning model needs to satisfy the constraints of equations (27) to (30): The effective service intensity of the jth charging station should be less than 1, that is: r e ′ ,j <1 (27) The service rejection rate of the jth charging station should be less than or equal to 10%, that is: P′ e,j ≤10％ (28) The charging satisfaction of the jth electric bus should be greater than or equal to 90%, that is: Among them, W max is the maximum waiting time of electric buses; coefficients ω and υ are weight coefficients; Limit the number of chargers in the jth charging station: 0<s j ≤γ·N j ′ (30) Where γ is the upper limit parameter of the number of chargers.

Images