CN113379131B - Method for optimizing connection bus network and synchronously optimizing fleet size and number of charging piles in pure electric bus application - Google Patents
Method for optimizing connection bus network and synchronously optimizing fleet size and number of charging piles in pure electric bus application Download PDFInfo
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Abstract
The invention discloses a method for optimizing a connection bus network and synchronously optimizing the size of a fleet and the number of charging piles in the application of pure electric buses, and belongs to the field of planning of pure electric buses. Comprises the following steps: constructing a model; step 2: initializing a public transport network; step 3: evaluating the adaptability; step 4: iterative optimization algorithm design; step 5: a net optimization scheme; the method is suitable for planning the pure electric bus connection network in the research area, and simultaneously optimizes the fleet scale of each line and the number of the deployed charging piles. The invention aims at optimizing the pure electric bus network of the docking subway from the theoretical level, so that the pure electric bus network plays an important role in subway docking, simultaneously optimizes the electric bus team scale and the number of charging piles of each route, reduces the total cost of the bus transportation system, and improves the overall operation benefit and the supply capacity, thereby improving the service level of the docking bus system.
Description
Technical Field
The invention relates to a method for optimizing a connection bus network and synchronously optimizing the size of a fleet and the number of charging piles in pure electric bus application, and belongs to the field of planning of pure electric bus networks. The method is suitable for planning the pure electric bus connection network in the research area, and simultaneously optimizes the fleet scale of each line and the number of the deployed charging piles.
Background
The urban process of China enables subways to rapidly develop in medium and large cities, and subway service is extended to suburban areas with relatively low public transportation density; in order to improve the travel of passengers in these areas, the public transport operators are required to continuously adjust the public transport line network so that they cooperate with subways smoothly. However, many designs of connection lines at present mainly depend on subjective judgment and experience, and lack theoretical basis. In addition, as the public is increasingly concerned about energy consumption and greenhouse gas emission, electric vehicles have been widely used in urban public transportation systems, and are mainly adapted to short-distance lines, such as junction bus lines, etc., due to limited driving range and relatively long charging time of electric vehicles. However, introducing electric vehicles into a connection bus system still brings a series of new problems to bus operators, such as determination of the fleet size of electric buses and the number of charging piles deployed in a charging station.
The invention aims at optimizing the pure electric bus network of the docking subway from the theoretical level, so that the pure electric bus network plays an important role in subway docking, simultaneously optimizes the electric bus team scale and the number of charging piles of each route, reduces the total cost of the bus transportation system, and improves the overall operation benefit and the supply capacity, thereby improving the service level of the docking bus system.
Disclosure of Invention
The technical scheme adopted by the invention is a method for optimizing a connection bus network and synchronously optimizing the fleet size and the number of charging piles under the application of pure electric buses, and the method comprises the following steps:
Step 1: model construction
And (3) taking various constraint conditions based on reality into consideration, and constructing a mixed integer optimization model by taking the total cost minimization of the bus transportation system as an optimization target.
To more fully describe this problem, the constraints set forth by the present invention are as follows:
Constraint 1: route direction constraints. Each route is initiated at a station and terminated at a subway station. No duplicate stations can be present in the line to avoid foldback or local loops in the line.
Constraint 2: line length constraints. The length of each line should be within a reasonable range depending on the scale of the road network.
Constraint 3: the site is fully covered with constraints. In order to improve the accessibility of passengers traveling in the community, the service range of the public transport network needs to cover all public transport stops.
Constraint 4: vehicle capacity limitations. On each line, the OD profile passenger volume cannot exceed the maximum passenger volume of the operating vehicle, which requires that each line must be set to a minimum departure frequency to ensure a corresponding service level.
Constraint 5: and (5) limitation of the charging pile. The total number of charging piles in the station cannot be greater than its highest limit to avoid under-supply of voltage in the station.
Constraint 6: station area limitations. In order to meet the demands of parking all buses at night and arranging charging piles at the stations, the sum of the occupied areas of all buses and the charging piles in the stations cannot be larger than the station area.
The objective function of the invention mainly comprises passenger cost and operator cost; the passenger cost is composed of waiting cost and vehicle-mounted cost, and the operator cost is composed of fleet scale cost and charging pile cost. The method can be concretely expressed as follows:
min CT＝CUW+CUI+CSF+CSC #(1)
Wherein CT is the total cost; CU W is the waiting cost for the passenger; CU I is the on-board cost of the passenger; CS F is the fleet size cost of the operator; CS C is the charging pile cost for the operator.
To calculate the on-board costs of the passengers, we provide that the passengers all choose the route with the shortest corresponding travel time, so the on-board costs of the passengers can be expressed as:
wherein M ij is the travel demand times of passengers from point i to point j; For the shortest travel time from i to j (because there are multiple subway stations in the study area, the travel path may not pass through the passenger's target subway station, when this happens, it is assumed that the passengers transfer at the subway station to the route of their target subway station; gamma I is the cost per passenger unit of time on board.
The waiting cost of a passenger is mainly related to the departure interval of a line, and can be expressed as:
Wherein H l is the departure interval of the route l; gamma W is the cost per passenger unit waiting time; is a binary variable, if line l is the shortest travel line from i to j points, then Otherwise
The fleet scale cost and the charging pile scale cost of the operators are respectively positively related to the number of the operators, and can be expressed as follows:
Wherein F l is the fleet size of line l; n l is the number of charging piles of the line l; gamma F,γC is the cost of possession (including purchase, investment, maintenance, depreciation costs, etc.) of the converted operator's individual vehicle and individual charging stake, respectively; NW is the line set.
From the above, it can be seen that the total cost of the bus transportation system can be calculated only by determining the number of charging piles, the fleet size and the departure interval of each line. Therefore, the invention introduces a multi-service desk queuing model with limited system capacity to calculate the number of buses (the number of vehicles waiting to be charged and being charged) in each line queuing system, thereby indirectly calculating the departure interval of each line.
In the method, in the process of the invention,The probability of no bus in the queuing system; The probability of n buses in the queuing system; lambda l is the arrival rate of a single bus returning to a station on line l, lambda l＝1/2Tl,Tl is the average single-pass operation time of line l; mu l is the average value of the number of vehicles served by a single charging pile on a line l in unit time, mu l＝1/tcl,tcl is the average charging time of a single bus on the line l, and for convenience of research, the invention provides that a return station is charged to full power every round trip of the bus, so that the relationship tc l＝2αTl·TR exists, and alpha is the power consumption of the bus in unit operation time; t R is the charging rate of the bus.
Based on equations (6) - (7), the average of the number of buses in the queuing system can be expressed as:
Thus, the number of vehicles in transit for line l may be denoted as F l-F′l, and further, the bus departure interval may be denoted as:
based on equations (6) - (8), it can be found that the relationship between the number of vehicles in the queuing system F' l and the total fleet size of the line F l is very complex, and after repeated experiments, it is found that: an increase in F l also causes an increase in F 'l, and F' l increases at a rate lower than F l, given the number of charging posts; the number of vehicles in transit on the line increases as F l increases, and thus the relationship can be obtained: increasing F l reduces the departure interval H l and the passenger waiting costs of the line, whereas the fleet size costs of operators increase linearly with increasing F l. According to the analysis, when the line is provided with the number of charging piles, the purpose of optimizing the line fleet size can be achieved by balancing the relation between the waiting cost of line passengers and the fleet size cost of operators.
Just as increasing fleet size F l, increasing line fill stake number n l also reduces H l and passenger waiting costs for the line, and since the operator's fill stake cost also increases linearly with increasing n l, the overall cost of the bus transportation system can be further minimized by balancing the operator's fill stake cost against the sum of the operator's fleet size cost and the passenger waiting cost.
Based on the analysis, the invention provides a cost optimization-based fleet size and charging pile number synchronous optimization algorithm for simultaneously determining the fleet size, the charging pile number and the departure interval of each line.
Step 2: bus network initialization
Based on the topology network, a network scheme set meeting all constraint conditions in the invention is generated by using a public transportation network initialization algorithm. s 0,S1,S2 respectively represent a bus station, a bus station set and a subway station set; NW denotes the current line plan set; r represents a currently generated line; CS 1 represents a bus stop set covered by the current line scheme set; UCS 1 represents a set of bus stops that are not yet covered by the current set of line plans. The initialization steps of the public transport network are as follows:
Step 3: fitness evaluation
Because the public transportation network can pass through each public transportation station in the area, the invention utilizes the passenger flow distribution method based on the shortest path to determine the passenger flow of each line, and finally, the objective function value of the scheme is calculated.
The shortest path-based passenger flow distribution method is to distribute each pair of passenger flow travel demands (OD) to the shortest one path from O to D.
Step 4: the design of the iterative optimization algorithm is carried out,
The invention uses genetic algorithm to carry out iterative optimization on the public transportation network, the genetic algorithm framework is shown in figure 1, when each iteration starts, a group of individuals can be randomly selected by a selection operator, the individuals with higher adaptability are considered to be better individuals, and the probability of being selected is higher. The invention takes the reciprocal of the objective function as the fitness function: f=1 CT. The probability that each individual is selected can be expressed as:
Wherein P i is the probability that individual i is selected; f i is the fitness function value of individual i. Then, NIND' (= NIND × GGAP) individuals were selected to make up the initial population using roulette, in preparation for crossover and mutation.
The invention proposes a customized crossover operator, which is shown in fig. 2, and performs two steps in total: 1) Exchanging lines from different parents; 2) The intermediate site sequence of two lines selected from different parents is changed. In order to find a better site sequence, the present invention proposes two single-point crossing methods in step 2 (named single-point crossing 1 and single-point crossing 2, respectively, see fig. 3 (a) and (b), respectively for detailed operation); and two single-point crossing methods are selected according to the coincidence degree c R1,R2 of the two lines.
Single point crossover is the search for a more optimal sequence of sites by changing the intermediate sequence of sites of two lines selected from different parents, aiming at optimizing the lines from a microscopic perspective. As shown in fig. 3 (a), after the single-point crossover 1 is performed on the line, connectivity between stations may change, and the crossover position may not have connectivity in the topology network, and at this time, the lines are connected through the shortest path by using Dijkstra's algorithm.
The crossover operation algorithm is described in detail as follows:
the invention provides a mutation operator. The operator first deletes the randomly selected line from the current line set, and then reinserts the node on the line into the line set after deleting the line.
While embedding line repair mechanisms into crossover and mutation operations may ensure that each generated line is viable, from a line set constraint, the newly generated line set may still have the following problems: 1) The newly generated line set has two or more identical lines; 2) The newly generated route set does not meet the constraints of covering all bus stops. To this end, the invention proposes a repair operator. For the situation that the problem 1 possibly occurs, the repair operator firstly checks whether the repeated lines exist in the whole line set, if so, the repeated lines in the line set are deleted, and only one line is left; for the case where problem 2 may occur, the repair operator is to generate one or more new routes to pass through bus stops not covered by the route set (see, for practice, steps 5-21 in the bus network initialization algorithm)
Step 5: net optimization scheme
Carrying out iterative optimization on a public transportation network through a genetic algorithm, obtaining an optimized public transportation network scheme, and giving out data information such as the trend, departure interval, number of charging piles, fleet size and the like of each line in the network scheme.
Drawings
FIG. 1 is a flowchart of a genetic algorithm according to the present invention
FIG. 2 is a schematic diagram of a customized crossover operator of the present invention
FIG. 3 is a schematic illustration of a single point crossover according to the present invention
FIG. 4 is a topology of a research area road network of the present invention
FIG. 5 is a diagram of an iterative process of the genetic algorithm of the present invention
FIG. 6 is a diagram of an optimized version of the present invention
Detailed Description
An example is given in which there are 51 nodes in the road network including 1 station (node 0), 47 bus stops (nodes 1-47) and 3 subway stations (nodes 48-50), and the topology network is as shown in fig. 4.
The invention utilizes the proposed optimization method to optimally design a bus network, related parameters in a model are set to ：γF＝300yuan/h;γW＝15yuan/h;γI＝10yuan/h;γC＝150yuan/h;TR＝0.7h;α＝0.5％/min; bus running speed V b =30 km/h, the maximum passenger capacity P=60 pass of the bus, LL max＝12km,LLmin =5 km: NIND = 20; MAXGEN = 500; p C＝0.9;PM＝0.1;fs＝0.3;fe = 0.8.
The iterative process of the genetic algorithm is shown in fig. 5, the optimization result of the public transportation network is shown in fig. 6, and the cost data information of each line after optimization is shown in table 1. The total cost of the whole bus transportation system of the optimized wire network is 38041.3yuan/h, wherein the fleet size cost of an operator is 10800yuan/h, the charging pile cost of the operator is 1650yuan/h, the waiting cost of passengers is 107023.0yuan/h, and the vehicle-mounted cost of passengers is 14868.3yuan/h.
Table 1 line costs in the optimization scheme of the present invention
Claims (1)
1. A method for optimizing a connection bus network and synchronously optimizing the size of a fleet and the number of charging piles under the application of pure electric buses is characterized by comprising the following steps of: comprises the steps of,
Step 1: constructing a model, taking constraint conditions into consideration, and constructing a mixed integer optimization model by taking the total cost minimization of the bus transportation system as an optimization target;
The function of the optimization objective includes passenger cost and operator cost; the passenger cost consists of waiting cost and vehicle-mounted cost, and the operator cost consists of fleet scale cost and charging pile cost; in order to calculate the vehicle-mounted cost of passengers, the passengers select one route with the shortest corresponding travel time, and the total cost of the bus transportation system can be calculated by determining the number of charging piles, the fleet scale and the departure interval of each route; determining the fleet size, the number of charging piles and the departure interval of each line simultaneously based on a cost-optimized fleet size and number of charging piles synchronous optimization algorithm;
Step 2: initializing a public transportation network, and generating a network scheme set meeting all constraint conditions by using a public transportation network initialization algorithm based on a topology network; s 0,S1,S2 respectively represent a bus station, a bus station set and a subway station set; NW denotes the current line plan set; r represents a currently generated line; CS 1 represents a bus stop set covered by the current line scheme set; UCS 1 represents a bus stop set which is not covered by the current line scheme set;
Step 3: the adaptability evaluation, wherein the passenger flow of each line is determined by using a passenger flow distribution method based on the shortest path as the public transport network passes through each public transport station in the area, and the objective function value of the scheme is calculated;
The passenger flow distribution method based on the shortest path distributes each pair of passenger flow travel demands OD to the shortest path from O to D;
Step 4: iterative optimization algorithm design, namely carrying out iterative optimization on a public transportation network by using a genetic algorithm, randomly selecting a group of individuals by a selection operator when each iteration starts, and taking the reciprocal of an objective function as an fitness function: f=1/CT;
Step 5: the network optimization scheme is used for carrying out iterative optimization on a public transportation network through a genetic algorithm, obtaining an optimized public transportation network scheme, and giving out trend, departure interval, number of charging piles and fleet scale data information of each line in the public transportation network scheme;
the function of the optimization objective in step 1 is specifically expressed as:
min CT＝CUW+CUI+CSF+CSc (1)
Wherein CT is the total cost; CU W is the waiting cost for the passenger; CU i is the on-board cost of the passenger; CS F is the fleet size cost of the operator; CS C is the charging pile cost for the operator;
In the function of the optimization objective in step 1, the on-vehicle cost of the passenger is expressed as:
wherein M ij is the travel demand times of passengers from point i to point j; the shortest travel time from i to j; gamma I is the cost per passenger unit of time on board;
in the function of the optimization objective in step 1, the waiting cost of the passenger is related to the departure interval of the route, expressed as:
Wherein H l is the departure interval of the route l; gamma W is the cost per passenger unit waiting time; is a binary variable, if line l is the shortest travel line from i to j points, then Otherwise
In the function of the optimization objective in step 1, the fleet scale cost and the charging pile scale cost of the operator are respectively positively correlated with the number of the operators, and are expressed as follows:
Wherein F l is the fleet size of line l; n l is the number of charging piles of the line l; gamma F,γC is the cost of possession of the converted operator's single vehicle and single charging pile, respectively; NW is line set;
In the function of the optimization target in the step 1, a multi-service desk queuing model with limited system capacity is introduced to calculate the number of buses in each line queuing system, so that the departure interval of each line is indirectly calculated;
In the method, in the process of the invention, The probability of no bus in the queuing system; The probability of n buses in the queuing system; lambda l is the arrival rate of a single bus returning to a station on line l, lambda l＝1/2Tl,Tl is the average single-pass operation time of line l; mu l is the average value of the number of vehicles which can be served by a single charging pile on a line l in unit time, mu l＝1/tcl,tcl is the average charging time of a single bus on the line l, and the bus charges a return station to full power every round trip, so tc l＝2αTl·TR and alpha are the power consumption of the bus in unit operation time; t R is the charging rate of the bus;
in the function of the optimization objective in step 1, based on formulas (6) - (7), the average value of the number of buses in the queuing system is expressed as:
Thus, the number of vehicles in transit on line l is denoted as F l-F′l, and the bus departure interval is denoted as:
Based on equations (6) - (8), the relationship between the number of vehicles in the queuing system F' l and the line total fleet size F l is found to be: when the line is given with the number of charging piles, the line fleet size is optimized by balancing the relation between the waiting cost of line passengers and the fleet size cost of operators;
in step 4, the probability that each individual is selected is expressed as:
Wherein P i is the probability that individual i is selected; f i is the fitness function value of individual i; then, NIND' (= NIND × GGAP) individuals were selected to make up the initial population using roulette, in preparation for crossover and mutation.
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