CN116702404A - Fuzzy double-target electric vehicle charging station planning optimization method considering target priority - Google Patents
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Abstract
The invention discloses a fuzzy double-target electric vehicle charging station planning optimization method considering target priority, which considers the influence of road congestion on user convenience when considering user convenience; when the waiting time length of charging is counted, a queuing model of the charging facility service system is established, and the waiting time length of each queuing vehicle is solved by using an iteration method. Based on the analysis of the vehicle change characteristics of various functional areas of the city by the user travel chain, the charging requirements of various areas are randomly simulated, the randomness of the charging of the user and the movement characteristics of the charging load are combined, a fuzzy double-target optimization method considering the target priority is provided, the economy of a power grid is considered, the convenience of the user is also considered, the layout of a charging station and the configuration of a charger are more reasonable, and a new thought and method are provided for the planning of the charging station.
Description
Technical Field
The invention belongs to the field of electric vehicles, and particularly relates to a fuzzy double-target electric vehicle charging station planning optimization method considering target priority.
Background
Along with the deterioration of the environment and the exhaustion of fossil fuel, energy conservation and emission reduction are becoming great, and electric vehicles are attracting attention due to the low-carbon and environment-friendly characteristics, and various governments and manufacturers are developing and producing electric vehicles to solve the problems of environmental pollution and the like. At present, the economy of a power grid or the convenience of users are singly considered when the charging station planning is studied, and the traffic characteristics and the travel rules of people are not considered when the charging area is divided, so that the service range of the charging station and the number of service vehicles have no time variability. Charging load prediction is the basis of charging station planning, and unreasonable charging station service range division and inaccurate load prediction will lead to unreasonable charging station site selection and volume determination.
Disclosure of Invention
The invention aims to provide a fuzzy double-target electric vehicle charging station planning optimization method considering target priority, and the method considers the economical efficiency of a power grid and the convenience of a user, so that the layout of a charging station and the configuration of a charger are more reasonable, and a new idea and method are provided for the site selection and the volume setting of the charging station.
The technical scheme for realizing the aim of the invention is a fuzzy double-target electric vehicle charging station planning and optimizing method considering target priority, which comprises the following steps:
step 1, constructing a road traffic network model according to urban road condition information;
step 2, establishing a charging load model based on the concept of a travel chain;
step 3, establishing a charging station planning model taking the maximum power grid benefit and the minimum average charging distance of a user as objective functions;
step 4, distinguishing the priority of the objective function by using a fuzzy double-objective optimization mode;
step 5, optimizing the position of the charging station, the number of charging machines and the access position of the charging station in the power distribution network by adopting a particle swarm optimization algorithm; simulating the charging process of the electric automobile, calculating the charging load and the maximum waiting time of a user in the set time of each charging station, calculating the income and the average charging distance of the charging station after the position, the capacity and the access point of each charging station are generated each time, and calculating the fitness of particles;
step 6, if the position of the charging station, the number of charging machines and the access position of the charging station in the power distribution network are optimized in the step 5 and do not meet the constraint condition of the charging station planning model, punishing the fitness function, and reducing the fitness function to be 1% of the original fitness function; if the constraint is satisfied, the fitness function remains unchanged;
and 7, updating the speed and the position of the particle swarm, decompiling the speed and the position into the number of chargers and access points in the charging station, returning to the step 3 until the iteration times or the preset convergence accuracy are reached, judging whether all possible alternative schemes are traversed, sorting all the possible schemes if so, selecting an optimal scheme, and otherwise returning to the step 2.
Compared with the prior art, the invention has the remarkable advantages that: (1) When considering the convenience of the user, the influence of road congestion on the convenience of the user is considered; when the waiting time length of charging is counted, a queuing model of the charging facility service system is established, and the waiting time length of each queuing vehicle is solved by using an iteration method. (2) According to the invention, based on the analysis of the vehicle change characteristics of various functional areas of the city by the user travel chain, the charging requirements of various areas are randomly simulated, the randomness of the charging of the user and the movement characteristics of the charging load are combined, and finally, the traffic characteristics and the land prices near the alternative station sites, and the economy and the safety of the access to the power grid are considered in the charging station site selection and constant volume research model.
Drawings
Fig. 1 is a schematic diagram of a travel chain.
Fig. 2 is a schematic diagram of a user travel chain structure charged to the charging station CS (charge station).
Fig. 3 is a flow chart of a fuzzy dual target electric vehicle charging station planning optimization method that accounts for target priority.
Fig. 4 is a charge load simulation calculation flow.
Detailed Description
The invention provides a fuzzy double-target electric vehicle charging station planning optimization method considering target priority, which comprises the following steps:
and 1, constructing a road traffic network through attributes capable of reflecting the real condition of the road, such as the road length, the road connection relation, the road grade and the road congestion coefficient. By Q c = (D, U, R, Y, L, T) represents a road traffic network, wherein the meanings each D, U, R, Y, L, T represents are as follows.
(1) D is a set of road intersections and a set of charging stations.
(2) U represents the length between nodes and the node connection relationship, i.e
Wherein: u (U) ij E, U, i and j are nodes respectively; u (u) ij Is the distance between nodes i, j; inf represents infinity and i, j e D.
(3) R is a road grade index.
Wherein r is ij ∈R,r ij Is the road class between nodes i, j.
(4) Y represents the road congestion index, Y ij ∈Y，y ij The road congestion coefficients between the nodes (namely traffic nodes formed after urban roads are intersected) i and j are smaller in value, so that the road is smoother, and the traffic congestion conditions of different periods in the same area are different.
(5) L represents an equivalent road length index. Taking the equivalent road length obtained after the road length, the road grade and the road congestion coefficient are weighted as an objective function, and planning a time-saving and road-saving path by using Dijkstra shortest path algorithm to obtain an equivalent road length L ij The calculation formula is as follows:
wherein: l (L) ij ∈L；W 1 、W 2 、W 3 Weights respectively representing road length, road class, and road congestion factor, W 1 +W 2 +W 3 =1. The equivalent road length between the charging station n and the road node i is:
wherein D is r For road junction set, D c Is a charging station set, and D c ,D r ∈D。
(6) T represents the running time index from node to node, namely:
wherein: t (T) ij For the length of travel between road nodes i, j, T ij E, T; v is the average travel speed.
And 2, establishing a charging load model based on a concept of a travel chain, wherein the travel chain refers to a connection relationship among one or more travel destinations which are sequentially completed by residents in a day according to a time sequence, and the data result of the U.S. family traffic travel survey NHTS (national household travel survey) shows that most of travel starting places in the residents in a day are resident areas H (home), and the travel destinations are mainly a working area W (working area) and a business area C (commercial area). If the travel destinations are classified, the travel chains can be mainly classified into 3 major categories as shown in fig. 1.
The travel chain 1 represents office workers going to and from a residential area and a working area in one day, the travel chain 2 represents people going to and from the residential area and a business area, and the travel chain 3 is people going to the business area for leisure and entertainment after going to and from work.
With the development of electric vehicles, the charging behavior of the electric vehicles is integrated into the travel chain of the user. The user travel chain structure to charge the charging station CS (charge station) is shown in fig. 2.
Travel chain 1-1 indicates that the office workers going to and from the home and work place need to be charged after the vehicles arrive at the work area, travel chain 2-1 indicates that the vehicles going to and from the residence and business area need to be charged at the charging station after arriving at the business area, travel chains 3-1 and 3-2 respectively indicate the vehicles arriving at the business area and the work area in the class 3 travel chain, and travel chain 3-3 indicates that the vehicles can be charged in the two areas of the business area and the work area within 1 day.
The following describes the travel distribution of the vehicles based on travel chains, and the time-space distribution of the vehicles is related to the travel ratio of each travel chain, the number of vehicles at the starting point of the travel chain and the travel generation rate of the vehicles. For each travel chain, the number of vehicles in the travel chain is generated at the starting point (residential area), and the vehicle generation amounts of other nodes than the starting point do not participate in the exchange of other chains. The change of the number of vehicles in the residential area has certain regularity, and a curve K (t) of the change of the parking quantity of a typical residential area along with time is obtained by fitting, namely:
travel generation amount of electric automobile in a th residential area at time tThe method comprises the following steps:
wherein: a is an integer from 1 to A, A is the total number of residential areas; lambda (lambda) 0 The maintenance rate of the electric automobile is guaranteed; n (N) a The number of vehicles in the residential area a;for the vehicle trip generation rate at time t, +.>Is the absolute value of the derivative of K (t).
In the travel chain 1, the travel yield of the electric automobile in the residential area a going to the b-th working area at the time tThe method comprises the following steps:
wherein: b is an integer from 1 to B, B is the number of working areas; lambda (lambda) 1 The travel duty ratio of the travel chain 1; lambda (lambda) ab The travel ratio from the residential area a to the working area b.
In the travel chain 2, the residential area a travels at the time t to the travel generation amount of the electric vehicle traveling to the h business areaThe method comprises the following steps:
wherein: h is an integer from 1 to H, H being the number of business areas; lambda (lambda) 2 The travel duty ratio of the travel chain 2; lambda (lambda) ah The travel ratio from the residential area a to the commercial area h.
In the travel chain 3, the residential area a generates travel generation amount at the time t, which reaches the business area h after being transferred from the working area bThe method comprises the following steps:
wherein: lambda (lambda) 3 The travel duty ratio of the travel chain 3; lambda (lambda) bh The travel ratio from the working area b to the commercial area h.
The area of the general functional area is smaller, the property is single, the travel generation amount and the arrival amount of each area are evenly distributed to the traffic nodes around the cell, and each type of traffic nodes belonging to multiple types of areas at the same timeThe travel generation amount is superimposed and the travel arrival amount is superimposed. The first travel time and the initial residual electric quantity of the residents obey normal distribution. Whether the user selects charging is related to the next driving range, and when the remaining power is insufficient to support driving to the next destination, the user selects charging, and 30% of the remaining power is considered for ensuring safety, and each charging is charged to a full state. Defining the remaining capacity of the vehicle at the arrival at the destination r asThen there are:
wherein: p (P) B Is the battery capacity; omega is the power consumption per kilometer; l (L) r-1,r For an equivalent link length from the r-1 st destination to the r-th destination. Equation (12) is used to determine whether the remaining power of the vehicle can travel from r to the destination r-1, and if equation (12) is not satisfied, it indicates that charging is required.
Parking duration W of vehicle entering charging station at t minutes t Waiting duration for charging of kth vehicle in queueAnd the charging time length T c And, the waiting time can be obtained by repeatedly iterating the charging station and the remaining charging time of the electric automobile in the charging queue:
T c ＝P B (1-S t )/P c (14)
wherein: x is x t For the number of vehicles being charged in the t-minute charging station, when x t When the charging waiting time is less than the number of charging machines in the charging station, the charging waiting time is 0, and when x is t When s is not less than s, a charging queue is formed in the charging station, and k is the position of the vehicle in the queuing queue; p (P) c The charging power of a single charger is calculated; s is S t The remaining capacity of the vehicle when entering the charging station at t minutes; t is t k The waiting time for the kth vehicle after the kth-1 vehicle in the queue enters the charging station; s is S k-1 Indicating a remaining electric quantity set of all electric vehicles in the charging station, which are being charged, when the (k-1) th vehicle in the queuing enters the charging station; s is S e The remaining power of the vehicle being charged for the e-th vehicle.
Let P (t) be charging power at t min of charging station, x t When < s, P (t) is the product of the charging power of a single charger and the number of the charging machines in operation; when x is t When s is not less than the charging power, the charging load is the power of the simultaneous working of the s charging machines in the charging station, and the charging power has the following expression:
the charging station load simulation calculation flow based on the travel chain is shown in fig. 4. The method comprises the following steps:
(1) And inputting information of each area, road traffic network structures, travel ratios of various travel chains, and randomly generating the position of the charging station and the number of charging machines.
(2) Calculating the travel quantity of each time period through the number of vehicles in each residential area and the formulas (7) to (10), and extracting the first travel time and the initial electric quantity of each electric automobile.
(3) Extracting the travel destination of each vehicle, selecting a time-saving and road-saving path through the formula (3) and the formula (4), calculating the arrival time of each vehicle, and calculating the residual electric quantity when reaching the destination by using the formula (11). And extracting the next destination according to the travel proportion. Whether the remaining power can reach the next destination is judged by the equation (12).
(4) And if the charging is not needed, extracting the parking time, otherwise, selecting an optimal path to reach the charging station through the formula (3) and the formula (4), and calculating the time of reaching the charging station and the residual electric quantity. And (3) calculating charge load in the charging station when the vehicle is charged by using the formulas (13) to (16), calculating the return arrival time, and extracting the returned parking time.
(5) And (3) calculating the departure time of each vehicle, judging whether each vehicle returns to the original place, if so, outputting the charging load curve of each charging station, and otherwise, returning to the step (3).
And 3, establishing a charging station planning model with the maximum power grid benefit and the minimum average charging distance of users. After the number of vehicles in each area, the road traffic network and the position and number of charging stations are randomly generated, the analysis content of the step can be known, and the traffic nodes which can be served by the charging stations at each moment and the charging load in the charging stations can be determined. The final charging station position, the number of charging stations and the access position in the distribution network are optimized, and the specific mathematical description is as follows:
max F 1 ＝I-(C 1 +C 2 +C 3 ) (18)
C 3 ＝λ g P 1 (23)
wherein: f (F) 1 To plan forAnnual operating returns within the zone; i is annual income of the charging station; c (C) 1 、C 2 、C 3 Respectively representing construction and maintenance costs of the charging station, line construction costs and increased network loss costs after the charging station is introduced; i n To establish the state variables of charging station n, I n =0 or 1; f (F) 2 The average charging driving distance for the user; dr, n represents a set of road traffic points selected to be charged at charging station n; z is Z i The number of electric vehicles is the ith traffic node; p is p i The average charging probability of the ith traffic node; lambda (lambda) c The charging electricity price is; lambda (lambda) b The price is electricity purchasing price; 1440 in formula (20) represents the number of minutes per day; p (P) n (t) represents the charging power at t minutes in the nth charging station; t(s) n ) The cost for configuring the charging machines (related to the number of charging machines in the charging station); alpha n The ground price at charging station n (related to the position of the charging station); z(s) n ) The occupied area of s chargers in the charging station n is set; y(s) n ) Annual running cost (related to the number of chargers) of the charging station; r is (r) 0 Is the discount rate; z y The depreciated years of the charging station; omega 1 Representing the cost of a single-circuit line per unit length; l (L) nw The length of the line from the charging station n to the network node w; w is a set of network nodes connected with the charging station; p (P) 1 Network loss after the charging station is connected to the power distribution network; lambda (lambda) g And the electricity selling price is represented.
Constraint conditions
(1) The maximum waiting time of the user is as follows:
t n,max ≤t max (24)
wherein: t is t n,max Representing a maximum waiting period of the vehicle in the charging station n; t is t max Indicating the maximum waiting time that the user can withstand.
(2) The number of the charging machines is constrained as follows:
N n,min ≤N n ≤N n,max (25)
wherein: n (N) n,min 、N n,max The minimum number and the maximum number of the chargers in the charging station n are respectively; n (N) n The number of charging machines of charging station n.
(3) The maximum distance constraint of the charging station to the served traffic node is:
L ni ≤L max n∈D c ,i∈D r,n (26)
wherein L is max Is the maximum allowed distance of the traffic node to the charging station.
(4) The minimum distance constraint between charging stations is:
L mn ≤l min m,n∈D c ,m≠n (27)
wherein: l (L) min The minimum allowable distance between charging stations; l (L) mn Is the distance between charging station n and charging station m.
(5) The node voltage amplitude constraint of the power distribution network is as follows:
wherein: v (V) b The voltage of the b node of the power distribution network;and->Respectively the minimum value and the maximum value of the voltage amplitude of the b node; b is a node set of the power distribution network.
(6) The active power balance constraint of the load node is as follows:
wherein: p (P) ab Active transmission power for line ab; p (P) b,L Is the original load at node b; p (P) b,S Power charging station at node b; n (N) node The number of nodes of the power distribution network is counted.
(7) The power line transmission capacity constraints are:
in the method, in the process of the invention,the minimum and maximum transmission power of the line ab, respectively.
And step 4, providing a fuzzy double-objective optimization mode, and distinguishing the priorities of the two objective functions. In fuzzy double-target optimization without considering target priority, a fuzzy membership function is adopted for processing because the attributes between targets cannot be compared. F, optimizing calculation with maximum annual income of charging station construction 1m The charging average travel distance F at this time is obtained 2M . Distance F calculated when optimizing with minimum average charging distance 2m Obtaining annual income F of charging station at the moment 1M . Fuzzifying the objective function to establish a single objective function F 1 、F 2 Mapping to membership. The membership of economic targets is:
the membership of the average charging distance is:
finally, optimizing a target maxF, wherein the calculation formula of the fitness F is as follows:
F＝min{μ(F 1 ),μ(F 2 )} (33)
in fuzzy double-target optimization considering target priority, if relative priority exists between two target functions, the two target functions can be distinguished by selecting different membership functions, and the membership function selection rule of the counted priority is as follows:
in the formula, q is the importance degree of the objective function, and when the objective function 1 is more important than the objective function 2, the q value in the membership function of the objective function 1 is larger than the q value of the membership function of the objective function 2.
And 5, solving the model established in the fourth step by using a particle swarm optimization algorithm, and optimizing the position of the charging station, the number of charging machines and the access position of the charging station in the power distribution network by using the particle swarm optimization algorithm. The number of charging stations is related to the possession of the electric vehicles, and according to national regulations, one charging station is required to be built for every 2000 electric vehicles, so the number of charging stationsN t For planning the maintenance of annual electric vehicles, < >>Representing an upward rounding.
Numbering the standby site selection scheme, setting a circulation variable, randomly selecting N sites in the standby site selection scheme, randomly generating the number x of charging machines within the constraint range of the number x of the charging machines, and setting the charging machines as initial positions of particles by encoding the charging machines and the charging station access point y, wherein the initial numbers and the access positions of the charging machines are as follows
If the population number of the particle group isThen the compiled population is
And simulating the charging process of the electric automobile, and calculating the charging load and the maximum waiting time of the user in each charging station for 1 day and the traffic node served by each charging station in each period. After each generation of the position, capacity, and access point of the charging station, the charging station profit and average charging distance are calculated by the formulas (18) to (23), and the fitness F of the particles is calculated by the formulas (31) to (33).
Step 6, if the constraint formulas (24) to (30) are not satisfied, punishing the fitness function, and reducing the fitness function F to 1% of the original fitness function; the fitness function remains unchanged if the constraint is satisfied.
And 7, updating the speed and the position of the particle swarm, decompiling the speed and the position into the number of chargers and access points in the charging station, and returning to the step five until the iteration times or the preset convergence accuracy are reached. And judging whether all possible alternatives are traversed, if so, entering the next step, otherwise, returning to the step two. And sequencing all possible schemes, and selecting an optimal scheme.
Claims (10)
1. The fuzzy double-target electric vehicle charging station planning optimization method considering target priority is characterized by comprising the following steps of:
step 1, constructing a road traffic network model according to urban road condition information;
step 2, establishing a charging load model based on the concept of a travel chain;
step 3, establishing a charging station planning model taking the maximum power grid benefit and the minimum average charging distance of a user as objective functions;
step 4, distinguishing the priority of the objective function by using a fuzzy double-objective optimization mode;
step 5, optimizing the position of the charging station, the number of charging machines and the access position of the charging station in the power distribution network by adopting a particle swarm optimization algorithm; simulating the charging process of the electric automobile, calculating the charging load and the maximum waiting time of a user in the set time of each charging station, calculating the income and the average charging distance of the charging station after the position, the capacity and the access point of each charging station are generated each time, and calculating the fitness of particles;
step 6, if the position of the charging station, the number of charging machines and the access position of the charging station in the power distribution network are optimized in the step 5 and do not meet the constraint condition of the charging station planning model, punishing the fitness function, and reducing the fitness function to be 1% of the original fitness function; if the constraint is satisfied, the fitness function remains unchanged;
and 7, updating the speed and the position of the particle swarm, decompiling the speed and the position into the number of chargers and access points in the charging station, returning to the step 3 until the iteration times or the preset convergence accuracy are reached, judging whether all possible alternative schemes are traversed, sorting all the possible schemes if so, selecting an optimal scheme, and otherwise returning to the step 2.
2. The method for optimizing the planning of a fuzzy dual-objective electric vehicle charging station taking into account objective priority as recited in claim 1, wherein step 1 constructs a road traffic network Q by road length, road connection, road class, road congestion factor c = (D, U, R, Y, L, T), wherein D, U, R, Y, L, T each represents the meaning as follows:
(1) D is a road intersection set and a charging station set;
(2) U represents the length between nodes and the node connection relationship, i.e
Wherein: u (U) ij E, U, i and j are nodes respectively; u (u) ij Is the distance between nodes i, j; inf represents infinity, and i, j e D;
(3) R is road grade index:
wherein r is ij ∈R,r ij Is the road class between nodes i, j.
Y represents the road congestion index, Y ij ∈Y，y ij The road congestion coefficient between the nodes i and j;
l represents an equivalent road length index, and the equivalent road length obtained by weighting the road length, the road class and the road congestion coefficient is used as a target functionThe number of the links is that a Dijkstra shortest path algorithm is used for planning a time-saving and road-saving path to obtain the equivalent road length L of the road node i and the road node j ij The calculation formula is as follows:
wherein: l (L) ij ∈L；W 1 、W 2 、W 3 Weights respectively representing road length, road class, and road congestion factor, W 1 +W 2 +W 3 =1; the equivalent road length between the charging station n and the road node i is:
wherein D is r For road junction set, D c Is a charging station set, and D c ,D r ∈D；
T represents the running time index from node to node, namely:
wherein: t (T) ij For the length of travel between road nodes i, j, T ij E, T; v is the average travel speed.
3. The fuzzy double-target electric vehicle charging station planning optimization method taking into account target priority according to claim 1, wherein the travel chain means that residents sequentially complete connection relations among one or more travel destinations in time sequence in a day, and the travel destinations are classified into 3 categories:
the travel chain 1 represents office workers going to and from a residential area and a working area in one day, the travel chain 2 represents people going to and from the residential area and a business area, and the travel chain 3 is people going to the business area for leisure and entertainment after going to and from work.
4. The fuzzy dual objective electric vehicle charging station programming optimization method of claim 3, wherein the charging behavior of the electric vehicle is integrated into the user's travel chain to obtain the user travel chain structure to charge the charging station CS as:
travel chain 1-1 indicates that the office workers going to and from the home and work place need to be charged after the vehicles arrive at the work area, travel chain 2-1 indicates that the vehicles going to and from the residence and business area need to be charged at the charging station after arriving at the business area, travel chains 3-1 and 3-2 respectively indicate the vehicles arriving at the business area and the work area in the class 3 travel chain, and travel chain 3-3 indicates that the vehicles can be charged in the two areas of the business area and the work area within 1 day.
5. The fuzzy dual objective electric vehicle charging station programming optimization method of claim 1, wherein the travel chain based charging station load model workflow is:
(1) Inputting information of each area, a road traffic network structure and travel ratios of various travel chains into a travel chain-based charging station load model, and randomly generating a charging station position and the number of charging machines;
(2) According to the number of vehicles in each residential area, calculating the travel quantity in each period, and extracting the first travel time and the initial electric quantity of each electric automobile;
(3) Extracting the travel destination of each vehicle, selecting a time-saving and road-saving path according to the equivalent road length, calculating the arrival time of each vehicle, calculating the residual electric quantity when reaching the destination, extracting the next destination according to the travel proportion, and judging whether the residual electric quantity can reach the next destination; the specific calculation formula of the residual electric quantity when the destination is reached is as follows:
in the method, in the process of the invention,to the remaining capacity of the vehicle at the destination r, P B Is the battery capacity; omega is the power consumption per kilometer; l (L) r-1,r For the equivalent road length from the (r-1) th destination to the (r) th destination +.>The current residual electric quantity of the vehicle;
the specific method for judging whether the residual electric quantity can reach the next destination is as follows: when the residual electric quantity is satisfiedIndicating that the charge can reach the next destination;
if the next destination can be reached, extracting parking time, otherwise, selecting an optimal path to reach a charging station through the equivalent road length, and calculating the time of reaching the charging station and the residual electric quantity; calculating charging loads in the charging station and the charging time when the vehicle is charged, calculating the return arrival time, and extracting the returned parking time;
(4) And (3) calculating the departure time of each vehicle, judging whether each vehicle returns to the original place, if so, outputting the charging load curve of each charging station, and otherwise, returning to the step (3).
6. The fuzzy bi-target electric vehicle charging station programming optimization method of claim 5, wherein the per-period travel measure calculation specific method is:
travel quantity of electric automobile in a th residential area at time tThe method comprises the following steps:
wherein a is an integer from 1 to A, A is the total number of residential areas; lambda (lambda) 0 The maintenance rate of the electric automobile is guaranteed; n (N) a The number of vehicles in the residential area a;for the vehicle trip generation rate at time t, +.>Is the absolute value of the derivative of K (t),
in the travel chain 1, the travel amount of the electric vehicle in the residential area a, which goes to the b-th working area at the time tThe method comprises the following steps:
wherein B is an integer from 1 to B, and B is the number of working areas; lambda (lambda) 1 The travel duty ratio of the travel chain 1; lambda (lambda) ab The travel duty ratio from the residential area a to the working area b is set;
in the travel chain 2, the residential area a travels at the time t for the travel amount of the electric vehicle going to the h business areaThe method comprises the following steps:
wherein: h is an integer from 1 to H, H being the number of business areas; lambda (lambda) 2 The travel duty ratio of the travel chain 2; lambda (lambda) ah The travel ratio from the residential area a to the commercial area h is set;
in the travel chain 3, the residential area a generates the travel quantity at the time t, which reaches the business area h after being transferred from the working area bThe method comprises the following steps:
wherein: lambda (lambda) 3 The travel duty ratio of the travel chain 3; lambda (lambda) bh The travel ratio from the working area b to the commercial area h.
7. The fuzzy double-target electric vehicle charging station programming optimization method taking into account target priorities of claim 5, wherein the specific method for calculating charging loads in charging stations during charging is as follows:
T c ＝P B (1-S t )/P c
wherein x is t For the number of vehicles being charged in the t-minute charging station, when x t When the charging waiting time is less than the number of charging machines in the charging station, the charging waiting time is 0, and when x is t When s is not less than s, a charging queue is formed in the charging station, and k is the position of the vehicle in the queuing queue; p (P) c The charging power of a single charger is calculated; s is S t The remaining capacity of the vehicle when entering the charging station at t minutes; t is t k The waiting time for the kth vehicle after the kth-1 vehicle in the queue enters the charging station; s is S k-1 Indicating a remaining electric quantity set of all electric vehicles in the charging station, which are being charged, when the (k-1) th vehicle in the queuing enters the charging station; s is S e The remaining power of the vehicle being charged for the e-th vehicle.
8. The fuzzy double-objective electric vehicle charging station programming optimization method taking into account objective priorities of claim 1.
9. The fuzzy double-target electric vehicle charging station programming optimization method taking into account target priorities of claim 1, wherein step 3 establishes a charging station programming model with maximum grid gain and minimum average charging distance of users as follows:
max F 1 ＝I-(C 1 +C 2 +C 3 )
C 3 ＝λ g P 1
wherein: f (F) 1 Annual operating benefits within the planning zone; i is annual income of the charging station; c (C) 1 、C 2 、C 3 Respectively representing construction and maintenance costs of the charging station, line construction costs and increased network loss costs after the charging station is introduced; i n To establish the state variables of charging station n, I n =0 or 1; f (F) 2 The average charging driving distance for the user; dr, n represents a set of road traffic points selected to be charged at charging station n; z is Z i The number of electric vehicles is the ith traffic node; p is p i The average charging probability of the ith traffic node; lambda (lambda) c The charging electricity price is; lambda (lambda) b The price is electricity purchasing price; p (P) n (t) represents the charging power at t minutes in the nth charging station; t(s) n ) The cost for configuring the charger; alpha n The land price at the charging station n; z(s) n ) The occupied area of s chargers in the charging station n is set; y(s) n ) Annual operation cost of the charging station; r is (r) 0 Is the discount rate; z y The depreciated years of the charging station; omega 1 Representing the cost of a single-circuit line per unit length; l (L) nw The length of the line from the charging station n to the network node w; w is a set of network nodes connected with the charging station; p (P) 1 Network loss after the charging station is connected to the power distribution network; lambda (lambda) g The electricity selling price is represented;
the constraint conditions are as follows:
(1) The maximum waiting time of the user is as follows:
t n,max ≤t max
wherein: t is t n,max Representing a maximum waiting period of the vehicle in the charging station n; t is t max Representing the maximum waiting time which can be born by the user;
(2) The number of the charging machines is constrained as follows:
N n，min ≤N n ≤N n，max
wherein: n (N) n,min 、N n,max The minimum number and the maximum number of the chargers in the charging station n are respectively; n (N) n The number of charging machines is charging station n;
(3) The maximum distance constraint of the charging station to the served traffic node is:
L ni ≤L max n∈D c ，i∈D r，n
wherein L is max The maximum allowable distance from the traffic node to the charging station is set;
(4) The minimum distance constraint between charging stations is:
L mn ≤l min m，n∈D c ，m≠n
wherein: l (L) min The minimum allowable distance between charging stations; l (L) mn Is the distance between charging station n and charging station m;
(5) The node voltage amplitude constraint of the power distribution network is as follows:
wherein: v (V) b The voltage of the b node of the power distribution network; v (V) b min And V b max Respectively the minimum value and the maximum value of the voltage amplitude of the b node; b is a node set of the power distribution network;
(6) The active power balance constraint of the load node is as follows:
wherein: p (P) ab Active transmission power for line ab; p (P) b,L Is the original load at node b; p (P) b,S Power charging station at node b; n (N) node The number of nodes of the power distribution network is counted;
(7) The power line transmission capacity constraints are:
in the method, in the process of the invention,the minimum and maximum transmission power of the line ab, respectively.
10. The fuzzy double-objective electric vehicle charging station programming optimization method taking into account objective priorities of claim 1, wherein the specific method of prioritizing two objective functions in step 4 is:
f, optimizing calculation with maximum annual income of charging station construction 1m The charging average travel distance F at this time is obtained 2M Distance F calculated when optimizing with minimum average charging distance 2m Obtaining annual income F of charging station at the moment 1M The method comprises the steps of carrying out a first treatment on the surface of the Fuzzifying the objective function to establish a single objective functionF 1 、F 2 Mapping to membership degree, wherein the membership degree of the economic target is as follows:
the membership of the average charging distance is:
finally, optimizing a target maxF, wherein the calculation formula of the fitness F is as follows:
F＝min{μ(F 1 )，μ(F 2 )}
in fuzzy double-target optimization considering target priority, if relative priority exists between two target functions, the two target functions can be distinguished by selecting different membership functions, and the membership function selection rule of the counted priority is as follows:
in the formula, q is the importance degree of the objective function, and when the objective function 1 is more important than the objective function 2, the q value in the membership function of the objective function 1 is larger than the q value of the membership function of the objective function 2.
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