CN115292866A - Electric vehicle charging station planning method combined with real-time traffic road network - Google Patents
Electric vehicle charging station planning method combined with real-time traffic road network Download PDFInfo
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Abstract
The invention discloses an Electric Vehicle (EV) charging station planning method combined with a real-time traffic road network, which comprises the following steps: step S1: extracting a topological structure of an urban traffic network; step S2: establishing a real-time vehicle speed-flow model; and step S3: obtaining an OD matrix corresponding to each time interval based on reverse deducing of TransCAD software; and step S4: calculating an electric automobile trip probability matrix; step S5: describing a continuous travel track of the electric automobile in one day based on the travel probability matrix; step S6: constructing a charging model of the single electric vehicle; step S7: constructing a charging load space-time prediction model of the electric automobile; step S8: and constructing a power distribution network planning model containing the electric vehicle charging station based on the load prediction result, and optimizing the site selection and volume determination results of the charging station. The method can realize the demand forecasting of the charging load of the electric automobile and the location and volume fixing scheme of the charging station based on the combination of a real-time traffic network.
Description
Technical Field
The invention relates to the technical field of new energy of electric vehicles, in particular to an electric vehicle charging station planning method combined with a real-time traffic road network.
Background
In an electric power system, an electric automobile is taken as a novel load, and three-dimensional random uncertainty of time, space and behavior, namely uncertain charging time, uncertain charging position and uncertain driving characteristics, can obviously influence the safe operation of a power grid. With the annual increase of the demand of the electric automobile, the scientific and perfect prediction of the charging load of the electric automobile and the formulation of the construction planning strategy of the charging station of the electric automobile are of great importance.
In order to solve the above uncertainty, scholars at home and abroad make a lot of work and research on the electric vehicle charging load prediction and the electric vehicle charging station location and volume fixing. Because different types of electric automobiles are mainly distributed in urban areas, factors such as urban road network topological structures have great influence on the charging characteristics of the electric automobiles. Therefore, the electric vehicle load prediction method which is combined with the real-time traffic road network and considers the space-time distribution is an effective method for analyzing the interaction influence between the electric vehicle and the power grid, selecting the site and fixing the volume of the electric vehicle charging station and formulating the control strategy of ordered charging.
However, the prior art has the following defects:
firstly, data sources of most of the conventional electric vehicle charging load prediction methods are mainly generated randomly by the existing original data based on Monte Carlo, calculation is not carried out according to traffic flow data of a real-time traffic network, and the accuracy and timeliness of the prediction data are lacked; secondly, the type of an electric private car is only considered in the process of predicting the charging load of most electric automobiles, the electric automobiles are widely popularized at present, various types such as electric taxies, urban functional cars, electric buses and the like exist, and the type of the electric automobiles is not comprehensive; finally, most prediction models only predict the total load demand, neglect the influence of the geographical position and the selection of the charging station, and are not in line with the actual situation of the urban road network.
In conclusion, most of the existing electric vehicle charging load prediction methods lack data accuracy and timeliness, are not enough for truly representing EV real-time travel laws, are too comprehensive and random, and have great influence on subsequent EV charging station site selection and volume determination planning models.
Disclosure of Invention
For the blank of the prior art, the method considers that most of the existing electric vehicle charging load prediction methods lack data accuracy and timeliness, are not enough for truly representing the real-time travel rule of the electric vehicle, are too unilateral and random, and have great influence on a subsequent electric vehicle charging station location and volume planning model.
The invention provides an electric vehicle charging station planning method combined with a real-time traffic road network, which is used for establishing an electric vehicle load prediction model based on OD matrix considering space-time distribution, and accordingly, the electric vehicle charging station planning method combined with real-time traffic road network updating is provided, and the travel probability of an electric vehicle and the space-time distribution of charging demands can be predicted, so that a multi-target planning model of an electric vehicle charging station is established, the position and the capacity of a charging station are determined, and the method has important significance on the safety, the reliability and the economy of operation under the condition that a power distribution network is accessed to the electric vehicle in a large scale.
The method for planning an Electric Vehicle (EV) charging station in combination with a real-time traffic road network comprises the following steps: step S1: extracting a topological structure of an urban traffic network; step S2: establishing a real-time vehicle speed-flow model; and step S3: obtaining an OD matrix corresponding to each time interval based on reverse deduction of TransCAD software; and step S4: calculating an electric automobile trip probability matrix; step S5: describing a continuous travel track of the electric automobile in one day based on the travel probability matrix; step S6: constructing a charging model of the single electric vehicle; step S7: constructing a charging load space-time prediction model of the electric automobile; step S8: and constructing a power distribution network planning model containing the electric vehicle charging station based on the load prediction result, and optimizing the site selection and volume determination results of the charging station. The method can realize the demand forecasting of the charging load of the electric automobile and the location and volume fixing scheme of the charging station based on the combination of a real-time traffic network.
In order to achieve the purpose, the invention specifically adopts the following technical scheme:
an electric vehicle charging station planning method combined with a real-time traffic road network is characterized by comprising the following steps:
step S1: extracting a topological structure of an urban traffic network; step S2: establishing a real-time vehicle speed-flow model; and step S3: obtaining an OD matrix corresponding to each time interval based on reverse deduction of TransCAD software; and step S4: calculating an electric automobile trip probability matrix; step S5: describing a continuous travel track of the electric automobile in one day based on the travel probability matrix; step S6: constructing a charging model of the single electric vehicle; step S7: constructing a charging load space-time prediction model of the electric automobile; step S8: and constructing a power distribution network planning model containing the electric vehicle charging station based on the load prediction result, and optimizing the site selection and volume determination results of the charging station.
Further, step S1 specifically includes: and extracting the topological structure of the urban traffic network, drawing an equivalent road network topological graph according to the actual road network topology, and generating a road matrix.
Further, in step S2: establishing a real-time vehicle speed-flow model, wherein the vehicle speed model at the moment t is shown as (1):
in the formula: v. of ij (t) represents the running speed of the electric vehicle on the road (i, j) at the time t; v. of ij-m (t) denotes a road l ij Zero flow velocity of (d); q. q of ij (t) is the section flow of the road (i, j) at the time t; c ij The traffic capacity of the road (i, j), namely the traffic flow; a. b and n are undetermined parameters and are determined according to fitting of measured values of the road.
Further, in step S3: an OD matrix is adopted to represent the traffic volume between a starting point and an end point in a road network in a certain period; building a road network model based on TransCAD software, wherein the road network model comprises point layers and line layers, the point layers represent road network nodes, and the line layers represent roads among the nodes; and putting the travel impedance at a certain moment, including data of traffic flow, traffic time, traffic speed and road grade, into a road network model, reversely deducing an OD matrix at the moment, and changing the travel impedance data to obtain the OD matrixes at different moments.
Further, in step S4: calculating an electric vehicle traveling probability matrix, wherein the traveling probability of the electric vehicle represents a ratio of the number of electric vehicles from the node i to the node j to the sum of the number of electric vehicles from the node i to any node in a time period from t to t +1, and is shown as a formula (2):
in the formula: p is a radical of ij,t Representing the trip probability of the electric automobile from the node i to the node j in the time period from t to t + 1; n is the number of nodes of the road network; a is ij,t The OD matrix element represents the number of electric vehicles driving from the node i to the node j in the time period from t to t + 1;
represents the sum of the number of electric vehicles traveling from the node i to any other node in the period from t to t + 1.
Further, in step S5: depicting the continuous travel track of the electric automobile in one day based on the OD matrix: with t c Representing simulation time, firstly, utilizing Monte Carlo to sample each electric automobile according to automobile type and assign initial position O i And an initial trip time t s Re-combination of t s The type of the electric vehicle corresponds to an OD matrix at the time interval, and a travel destination d of the electric vehicle is generated through the OD matrix; if the destination and the departure place are the same node, the fact that the electric automobile does not need to go out in the current period is meant; according to the vehicle state parameters, the real-time vehicle speed-flow model is combined to calculate the driving time, and the simulation time t is updated after d is reached c And the state parameter value takes d as a new starting point.
Further, in step S6: the method comprises the following steps of constructing a single electric automobile charging model, counting electric automobile parameters according to various characteristics of the electric automobile running in a road network to obtain a real-time charging model of the electric automobile, wherein the running state of the electric automobile is as follows:
EV＝{T p ,O i ,D i ,t s ,t,Cap r ,Cap 0 ,Cap(t),ΔCap,F p ,L p } (3)
in the formula: t is p Indicates the type of electric vehicle, O i 、D i Indicates the starting and ending points of travel of the electric vehicle, t s Denotes the starting time, t denotes the driving time, cap r Representing the battery capacity, cap, of the electric vehicle 0 Representing the initial electric quantity of the electric automobile, cap (t) representing the electric quantity of the electric automobile at the time t, delta Cap representing the electric consumption of the electric automobile per kilometer, F p 、L p Respectively representing the fast charging power and the slow charging power of the electric automobile;
if the power consumption of the electric vehicle linearly increases along with the driving mileage, the remaining power at a certain moment is calculated by equation (4):
Cap(t+1)＝η(Cap(t)-ΔCap·Δl) (4)
in the formula: cap (t + 1) represents the remaining power at the next sampling time t +1, η represents the power consumption loss coefficient, and Δ l represents the distance traveled from t to t + 1.
Further, in step S7: the method for constructing the electric automobile charging load space-time prediction model specifically comprises the following steps: analyzing and processing regional electric vehicle data to obtain initial parameters of the electric vehicle, wherein the initial parameters comprise: electric vehicle type, quantity, charging power and state of charge;
obtaining a running path of the electric automobile by using an OD trip probability matrix and a Floyd shortest path algorithm before the electric automobile reaches the stop time; when the electric automobile runs in a road network, if the state of charge of the electric automobile reaches a threshold value of 0.2 or below, the electric automobile searches a nearest charging place for quick charging, then continues to run until the running stop time is reached, finally judges whether the SOC of the electric automobile is less than 0.6 when the electric automobile stops, if so, the electric automobile is slowly charged, otherwise, the electric automobile is not charged.
Further, in step S8: based on the load prediction result, constructing a power distribution network planning model containing the electric vehicle charging station, and optimizing the location and volume selection result of the charging station based on an NSGA-III algorithm by taking the minimum planning cost of the charging station, the minimum rapid charging load fluctuation, the highest utilization rate of the charging station and the best voltage quality of a power grid as targets;
the method comprises the following steps of constructing a power distribution network planning model containing the electric vehicle charging station by taking the minimum planning cost of the charging station, the minimum rapid charging load fluctuation, the maximum charging station utilization rate and the best power grid voltage quality as targets, and concretely referring to the formula (7) -10:
charging station investment cost F 1
minF 1 ＝C cdz +C OM (7)
In the formula: c cdz For equivalent annual construction costs of charging stations, C OM Maintenance costs for the charging station;
annual rapid charging load fluctuation F 2
In the formula: CEV k For charging the capacity of the station k, PEV i,j For the fast charging load of j hours on the ith day of the charging station k, PEV i,j,yave The average fast charging load of a charging station k year;
annual charging station utilization rate F 3
In the formula: PEV i,j For the fast charging load of the charging station k at the ith hour, CEV k Capacity of charging station k;
distribution network annual voltage offset F 4
In the formula: m is the number of nodes of the power distribution network; k is the node number of the power distribution network; u shape i,j,k And U i,j,kN Actual voltage and rated voltage at node k at jth hour on day i;
the constraint conditions comprise power flow constraint, node voltage constraint, branch circuit capacity constraint and electric vehicle charging station installation capacity constraint, and are respectively expressed as formulas (11) - (14):
U imin ≤U i ≤U imax (12)
S l ≤S lmax l＝1,2,…,L (13)
in the formula: p is i 、Q i Respectively injecting active power and reactive power into the node i; u shape i 、U j Voltages of nodes i and j, respectively; g ij 、B ij The conductance and susceptance are divided into branches ij; theta ij Is the phase angle difference of the voltages between nodes i, j; u shape imin 、U imax The upper limit and the lower limit of the voltage of the node i are respectively; s. the l 、S lmax The capacity and the maximum limit capacity of the branch l are respectively; l is the total number of branches;
the upper limit and the lower limit of the installation capacity of the electric vehicle charging station of the node k are respectively;
and then solving the power distribution network planning model containing the electric vehicle charging station by adopting an evolutionary multi-objective optimization algorithm NSGA-III based on a non-dominated sorting method of the reference points.
Further, the method also comprises the step S9: in order to verify the influence of uncertainty of the charging mode of an electric vehicle owner on joint planning of a power distribution network, the following 3 schemes are constructed for simulation analysis: scheme A assumes that the owner of the electric vehicle is charged disorderly and does not consider the upper charging limit of the charging station; in the scheme B, the electric vehicle owner is supposed to be guided to charge in order, and the charging upper limit of the charging station is set to control the charging quantity; according to the scheme C, on the basis of the scheme B, idle electric automobile vehicles in a road network are considered to voluntarily participate in the V2G behavior under the ordered guidance strategy;
and comparing and analyzing the rationality of the distribution network planning result according to different charging schemes.
Compared with the prior art, the method and the optimal selection scheme thereof can realize the electric vehicle charging load demand prediction and the location and volume fixing scheme of the charging station combined with the real-time traffic road network. The method comprises the steps of calculating the traveling probability of the electric automobile based on an OD matrix backstepping algorithm, describing the single-day traveling track of each electric automobile, constructing a real-time charging load prediction model of the electric automobile, and accordingly establishing a multi-target planning model of the power distribution network with the electric automobile charging station. The evaluation method has the advantages that uncertainty of charging load space-time distribution is fully considered, the space-time distribution situation of the charging requirements of the electric vehicle in a certain area can be accurately predicted and planned, and optimal configuration of the charging station is carried out.
The main design points comprise:
(1) A traffic network topology model and a real-time speed-geographic database model are established;
(2) Adopting an OD matrix back-stepping algorithm based on traffic planning software TransCAD to calculate an EV travel probability matrix so as to predict the EV charging demand space-time distribution;
(3) By adopting the EV charging load prediction result, an EV charging station multi-objective optimization configuration model considering space-time distribution is established, the site selection and the volume fixing of EV charging stations in a distribution network are realized, technical support is provided for a power distribution network planning model after a large number of electric vehicles are connected into the network, and the comprehensive performance of a configuration scheme is also improved by the combined planning of the electric vehicles and a real-time traffic road network.
Drawings
FIG. 1 is a flow chart of the electric vehicle load space-time prediction in the embodiment of the invention;
FIG. 2 is a flow chart of an electric vehicle charging station planning process based on NSGA-III algorithm according to an embodiment of the present invention;
fig. 3 is a topological structure diagram of a traffic network in a specific example provided by the embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
it should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
As shown in fig. 1 and fig. 2, the present embodiment provides a method for planning an electric vehicle charging station in combination with a real-time traffic road network, including the following steps:
step S1: extracting a topological structure of an urban traffic network, and drawing an equivalent road network topological graph according to the actual road network topology;
step S2: obtaining the real-time speed of the vehicle running according to the real-time traffic flow, and introducing a real-time vehicle speed-flow model to calculate the running speed v of the vehicle on the road section between the end points i and j at the time t ij (t), establishing a real-time vehicle speed-flow model, wherein the vehicle speed model at a certain moment is shown as (1):
in the formula: v. of ij (t) represents the running speed of the electric vehicle on the road (i, j) at the time t; v. of ij-m (t) denotes a road l ij A zero flow speed of (a), typically a road-specified maximum speed; q. q of ij (t) is the section flow of the road (i, j) at the time t; c ij Is the traffic capacity of the road (i, j), i.e. the traffic flow; a. b and n are undetermined parameters and are determined by fitting according to measured values of roads;
urban areas are generally divided into work areas, living areas and business areas by functional location.
And step S3: the OD matrix can represent the traffic volume between the starting point (Origin) and the end point (Destination) in a road network at a certain time segment. Building a road network model based on TransCAD software, wherein the road network model comprises point layers and line layers, the point layers represent road network nodes, and the line layers represent roads among the nodes; putting the travel impedance (including data such as traffic flow, traffic time, traffic speed, road grade and the like) at a certain moment into a road network model, reversely deducing an OD matrix at the moment, and changing the travel impedance data to obtain OD matrixes at different moments.
And step S4: calculating an electric vehicle traveling probability matrix, wherein the traveling probability of the electric vehicle represents the ratio of the number of electric vehicles from the node i to the node j to the sum of the number of electric vehicles from the node i to any node in a time period from t to t +1, and is expressed as formula (2):
in the formula: p is a radical of formula ij,t Representing the trip probability of the electric automobile from the node i to the node j in the time period from t to t + 1; n is the number of nodes of the road network; a is ij,t The OD matrix element represents the number of electric vehicles driving from the node i to the node j in the time period from t to t + 1;
representing the sum of the number of electric vehicles driving from the node i to any other node in the time period from t to t + 1;
step S5: describing a continuous travel track of the electric automobile in one day based on the OD matrix: with t c Representing simulation time, firstly, utilizing Monte Carlo to sample each electric automobile according to automobile type and assign initial position O i And an initial trip time t s Re-combination of t s The type of the electric vehicle corresponds to an OD matrix at the time interval, and a travel destination d of the electric vehicle is generated through the OD matrix; if the destination and the departure place are the same node, the fact that the electric automobile does not need to go out in the current period is meant; according to the vehicle state parameters, the real-time vehicle speed-flow model is combined to calculate the driving time, and the simulation time t is updated after d is reached c And the state parameter value takes d as a new starting point;
by repeatedly calling the OD matrixes of various types of electric vehicles at all time intervals, the continuous travel track of the EV in one day can be described.
Step S6: the method comprises the following steps of constructing a single electric automobile charging model, counting electric automobile parameters according to various characteristics of the electric automobile running in a road network to obtain a real-time charging model of the electric automobile, wherein the running state of the electric automobile is as follows:
EV＝{T p ,O i ,D i ,t s ,t,Cap r ,Cap 0 ,Cap(t),ΔCap,F p ,L p } (3)
in the formula: t is p Indicates the type of electric vehicle, O i 、D i Indicates the starting and ending points of the electric vehicle, t s Denotes the start time, t denotes the travel time, cap r Representing the battery capacity, cap, of the electric vehicle 0 Representing the initial electric quantity of the electric automobile, cap (t) representing the electric quantity of the electric automobile at the time t, delta Cap representing the electric consumption of the electric automobile per kilometer, F p 、L p Respectively representing the fast charging power and the slow charging power of the electric automobile.
Assuming that the electric power consumption of the electric vehicle increases linearly with the mileage, the remaining power at a certain time can be calculated by equation (4):
Cap(t+1)＝η(Cap(t)-ΔCap·Δl) (4)
in the formula: cap (t + 1) represents the remaining power at the next sampling time t + 1, η represents the power consumption loss coefficient, and Δ l represents the distance traveled from t to t + 1.
Step S7:
because the electric bus has a fixed driving path, charging time and charging place, and the space-time distribution of the charging demand is relatively fixed, the embodiment mainly analyzes the electric taxi, the urban functional vehicle and the electric private car when performing joint optimization configuration of the DG charging station and the EV charging station.
The travel characteristics of the EV are closely related to the urban area where the EV is located, a private owner is mainly used for the user to go to and from work and to and from a living area and a working area in daily life, and the initial position of the private owner is mainly concentrated in the living area; most taxis finish the shift change in the living area, so the initial position of the taxis is more likely to be in the living area; most of the urban functional vehicles are used by enterprises and government departments for performing official business in the daytime, and the probability that the initial position of the urban functional vehicle is in a working area is high.
Firstly, analyzing and processing electric vehicle data in a certain area to obtain initial parameters of the electric vehicle, such as electric vehicleType of car (private car, taxi and city functional car), number, charging power and state of charge, etc., wherein the initial state of charge of each electric car satisfies the normal distribution N (0.5,0.1) 2 ) The starting and stopping running time of the private car and the urban functional car meet the normal distribution N (8.92,3.24) 2 ) And N (17.6,3.4) 2 ) The starting and stopping running time of the taxi satisfies the normal distribution N (7.92,3.24) 2 ) And N (18.6,3.4) 2 )。
Therefore, an electric vehicle charging load space-time prediction model can be constructed: analyzing and processing electric vehicle data in a certain area to obtain initial parameters of the electric vehicle, such as the type (private car, taxi and city functional vehicle), quantity, charging power, state of charge and the like of the electric vehicle, as shown in table 1:
TABLE 1 electric vehicle parameter settings
| Type of electric vehicle | Number/vehicle | Initial driving time/h | End of travel time/h | Stop time/min |
| Private car | 240 | N(8.92,3.24 2 ) | N(17.6,3.4 2 ) | N(10,3.5 2 ) |
| Taxi | 500 | N(7.92,3.24 2 ) | N(18.6,3.4 2 ) | N(120,42 2 ) |
| Urban functional vehicle | 160 | N(6.00,1.50 2 ) | N(17.6,3.4 2 ) | N(20,6 2 ) |
And obtaining the driving path of the electric automobile by the OD trip probability matrix and the Floyd shortest path algorithm before the electric automobile reaches the stop time. When the electric automobile runs in a road network, if the state of charge of the electric automobile reaches a threshold value of 0.2 or below, the electric automobile searches a nearest charging place for quick charging, then continues to run until the running stop time is reached, finally judges whether the SOC of the electric automobile is less than 0.6 when the electric automobile stops, if so, the electric automobile is slowly charged, otherwise, the electric automobile is not charged. The load space-time prediction process is shown in figure 1;
step S8: based on a load prediction result, a power distribution network planning model containing an electric vehicle charging station is constructed, the charging station planning takes into consideration both the economy of the charging station and the technical factors of a power grid, so that the optimization of the location and volume determination result of the charging station is carried out based on an NSGA-III algorithm with the goals of minimum planning cost of the charging station, minimum quick charging load fluctuation, highest utilization rate of the charging station and best voltage quality of the power grid as the targets, and a specific flow chart is shown in figure 2;
step S9: in order to verify the influence of uncertainty of the charging mode of an electric vehicle owner on the joint planning of the power distribution network, the following 3 schemes are constructed for simulation analysis: scheme A assumes that the owner of the electric vehicle is charged disorderly and does not consider the upper charging limit of the charging station; in the scheme B, the electric vehicle owner is supposed to be guided to charge in order, and the charging upper limit of the charging station is set to control the charging quantity; scheme C considers on the basis of scheme B that idle electric automobile vehicles in the road network voluntarily participate in the V2G action under the orderly guiding strategy, and this embodiment assumes that 100 idle electric automobile vehicles are willing to participate in, and compares and analyzes the rationality of the distribution network planning result according to different charging schemes.
In this embodiment, step S1 specifically includes the following contents:
the traffic network model consists of a road topological structure, a real-time vehicle speed-flow model and a functional area. And drawing an equivalent road network topological graph according to the actual road network topology, as shown in FIG. 3.
The road matrix generated according to the road network topological graph is shown as (5) - (6):
in the formula: v (G) is a road section set in a road network; l ij Is the road length of nodes i to j; inf indicates that there is no link between two nodes.
In this embodiment, step S8 specifically includes the following contents:
the charging station planning takes into account both the economy of the charging station and the grid engineering, so that the aim is to minimize the charging station planning cost, minimize rapid charging fluctuations, maximize the charging station utilization and optimize the grid voltage quality, as shown in (7) - (10).
1) Charging station investment cost F 1
minF 1 ＝C cdz +C OM (7)
In the formula: c cdz For equivalent annual construction costs of charging stations, C OM Is the maintenance cost of the charging station.
2) Annual rapid charging load fluctuation F 2
In the formula: CEV k For charging the capacity of the station k, PEV i,j For the fast charging load of j hours on the ith day of the charging station k, PEV i,j,yave The average fast charge of the charging station k for one year.
3) Annual charging station utilization rate F 3
In the formula: PEV i,j For the fast charging load of the charging station k at the ith hour, CEV k Is the capacity of the charging station k.
4) Distribution network annual voltage offset F 4
In the formula: m is the number of nodes of the power distribution network; k is the node number of the power distribution network; u shape i,j,k And U i,j,kN Actual voltage and rated voltage at node k at j hour of day i.
The constraint conditions comprise power flow constraint, node voltage constraint, branch circuit capacity constraint and electric vehicle charging station installation capacity constraint, which are respectively expressed as formulas (11) - (14).
U imin ≤U i ≤U imax (12)
S l ≤S lmax l＝1,2,…,L (13)
In the formula: p i 、Q i Respectively injecting active power and reactive power into the node i; u shape i 、U j Voltages of nodes i and j, respectively; g ij 、B ij The conductance and susceptance are divided into branches ij; theta ij Is the phase angle difference of the voltages between nodes i, j; u shape imin 、U imax The upper limit and the lower limit of the voltage of the node i are respectively; s l 、S lmax The capacity and the maximum limit capacity of the branch l are respectively; l is the total number of branches;
and the upper limit and the lower limit of the installation capacity of the electric vehicle charging station of the node k are respectively.
Compared with the traditional multi-target genetic algorithm, the non-dominated sorting genetic (NSGA-II) algorithm with the elite strategy is improved in the aspects of improving the running speed, optimizing the convergence precision and the like, but is easy to fall into local optimization when facing three or more multi-target optimization problems. Therefore, the present embodiment adopts an evolutionary multi-objective optimization (NSGA-III) algorithm based on a non-dominated ranking method of reference points to solve the model, and the specific steps are shown in fig. 2.
In this embodiment, an IEEE 33 node power distribution network system is combined with a 29 node network, functional areas are divided into a living area 1 (nodes 1 to 11), a living area 2 (nodes 12 to 16), an industrial area (nodes 17 to 20), and a business area (nodes 21 to 29), and by taking 3 schemes described in S9 as an example, different schemes are adopted to perform planning and configuration of electric vehicle charging stations. The technical effect of this embodiment is shown in table 2 with reference to specific embodiments.
Table 2 comparison of results of planning models of electric vehicle charging stations
As can be seen from table 2, the owner of the electric vehicle may select a charging station in the vicinity of the disordered charging behavior, so that the charging needs are intensively concentrated in the business area; after the guide car owner carries out charging in order, except commercial district, the demand evenly distributed that charges to living area and industrial area has effectively balanced the distribution of electric automobile charging station in the distribution network. The results of the power distribution network planning models of the 3 schemes corresponding to the objective function are compared and shown in table 3.
TABLE 3 comparison of objective function results for power distribution network planning models
As can be seen from table 3, compared with the disordered charging strategy of the scheme a, the scheme B guides the electric vehicle owner to perform ordered charging, so that the construction and operation and maintenance costs of the electric vehicle charging station are effectively reduced, and the utilization rate of the charging station is improved, and because the ordered charging behavior brings about the change of the travel will of the vehicle owner, reasonable small-range load fluctuation is generated, so that the scheme B is more reasonable than the planning scheme a; compared with the scheme B, the scheme C combines orderly charging and orderly participation in the V2G behaviors, the utilization rate of the electric vehicle charging station is greatly increased, the situation that the vehicle owner voluntarily goes to the charging station to participate in discharging is considered, the power grid side needs to sell power to the vehicle owner participating in the V2G, the construction cost of the charging station is slightly improved, the rapid charging load fluctuation is increased by 15% in comparison with the scheme B, and the rapid charging load fluctuation all belong to a normal fluctuation range.
This embodiment has realized:
(1) A traffic network topology model and a real-time speed-geographic database model are established;
(2) Adopting an OD matrix back-stepping algorithm based on traffic planning software TransCAD to calculate an electric vehicle travel probability matrix so as to predict the electric vehicle charging demand space-time distribution;
(3) By adopting the electric vehicle charging load prediction result, a multi-objective optimization configuration model of the electric vehicle charging station considering space-time distribution is established, the site selection and the volume fixing of the electric vehicle charging stations in the distribution network are realized, technical support is provided for a power distribution network planning model after a large number of electric vehicles are connected into the network, and the comprehensive performance of the configuration scheme is also improved by the combined planning of the electric vehicle charging load prediction result and a real-time traffic road network.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
The present invention is not limited to the above preferred embodiments, and other various types of electric vehicle charging station planning methods combined with real-time traffic network can be obtained according to the teaching of the present invention.
Claims (10)
1. An electric vehicle charging station planning method combined with a real-time traffic road network is characterized by comprising the following steps of:
step S1: extracting a topological structure of an urban traffic network; step S2: establishing a real-time vehicle speed-flow model; and step S3: obtaining an OD matrix corresponding to each time interval based on reverse deducing of TransCAD software; and step S4: calculating an electric automobile trip probability matrix; step S5: depicting a continuous travel track of the electric automobile in one day based on the travel probability matrix; step S6: constructing a charging model of the single electric vehicle; step S7: constructing a charging load space-time prediction model of the electric automobile; step S8: and constructing a power distribution network planning model containing the electric vehicle charging station based on the load prediction result, and optimizing the site selection and volume determination results of the charging station.
2. The method of claim 1, wherein the method comprises the steps of: the step S1 specifically includes: and extracting the topological structure of the urban traffic network, drawing an equivalent road network topological graph according to the actual road network topology, and generating a road matrix.
3. The method of claim 1, wherein the method comprises the steps of:
in step S2: establishing a real-time vehicle speed-flow model, wherein the vehicle speed model at the moment t is shown as (1):
in the formula: v. of ij (t) represents the running speed of the electric automobile on the road (i, j) at the time t; v. of ij-m (t) denotes a road l ij Zero flow velocity of (d); q. q.s ij (t) is the section flow of the road (i, j) at the time t; c ij The traffic capacity of the road (i, j), namely the traffic flow; a. b and n are undetermined parameters and are determined according to fitting of measured values of the road.
4. The method of claim 1, wherein the method comprises the steps of:
in step S3: an OD matrix is adopted to represent the traffic volume between a starting point and a terminal point in a road network in a certain period; building a road network model based on TransCAD software, wherein the road network model comprises point layers and line layers, the point layers represent road network nodes, and the line layers represent roads among the nodes; and putting the trip impedance at a certain moment, including data of traffic flow, traffic time, traffic speed and road grade, into a road network model, reversely deducing the OD matrix at the moment, and obtaining the OD matrixes at different moments by changing the trip impedance data.
5. The method of claim 1, wherein the method comprises the steps of:
in step S4: calculating an electric vehicle traveling probability matrix, wherein the traveling probability of the electric vehicle represents a ratio of the number of electric vehicles from the node i to the node j to the sum of the number of electric vehicles from the node i to any node in a time period from t to t +1, and is shown as a formula (2):
in the formula: p is a radical of ij,t Represents that the electric automobile is in the period from t to t +1The trip probability of points i to j; n is the number of nodes of the road network; a is ij,t The OD matrix element represents the number of the electric vehicles driving from the node i to the node j in the time period from t to t + 1;
represents the sum of the number of electric vehicles traveling from the node i to any other node in the period from t to t + 1.
6. The method of claim 1, wherein the method comprises the steps of:
in step S5: describing a continuous travel track of the electric automobile in one day based on the OD matrix: with t c Representing simulation time, firstly, utilizing Monte Carlo to sample each electric automobile according to automobile type and assign initial position O i And an initial trip time t s Re-combination of t s The type of the electric vehicle corresponds to an OD matrix at the time interval, and a travel destination d of the electric vehicle is generated through the OD matrix; if the destination and the departure place are the same node, the fact that the electric automobile does not need to go out in the current period is meant; according to the vehicle state parameters, the real-time vehicle speed-flow model is combined to calculate the driving time, and the simulation time t is updated after d is reached c And the state parameter value takes d as a new starting point.
7. The method of claim 1, wherein the method comprises the steps of:
in step S6: the method comprises the following steps of constructing a single electric automobile charging model, counting electric automobile parameters according to various characteristics of the electric automobile running in a road network to obtain a real-time charging model of the electric automobile, wherein the running state of the electric automobile is as follows:
EV＝{T p ,O i ,D i ,t s ,t,Cap r ,Cap 0 ,Cap(t),ΔCap,F p ,L p } (3)
in the formula: t is p Indicates the type of electric vehicle, O i 、D i Indicates the starting and ending points of the electric vehicle, t s Denotes the start time, t denotes the travel time, cap r Representing the battery capacity, cap, of an electric vehicle 0 Representing the initial electric quantity of the electric automobile, cap (t) representing the electric quantity of the electric automobile at the time t, delta Cap representing the electric consumption of the electric automobile per kilometer, F p 、L p Respectively representing the fast charging power and the slow charging power of the electric automobile;
if the power consumption of the electric vehicle linearly increases along with the driving mileage, the remaining power at a certain moment is calculated by equation (4):
Cap(t+1)＝η(Cap(t)-ΔCap·Δl) (4)
in the formula: cap (t + 1) represents the remaining power at the next sampling time t +1, η represents the power consumption loss coefficient, and Δ l represents the distance traveled from t to t + 1.
8. The method of claim 1, wherein the method comprises the steps of:
in step S7: the method for constructing the electric automobile charging load space-time prediction model specifically comprises the following steps: analyzing and processing regional electric vehicle data to obtain initial parameters of the electric vehicle, wherein the initial parameters comprise: electric vehicle type, quantity, charging power and state of charge;
obtaining a driving path of the electric automobile by using an OD (origin-destination) trip probability matrix and a Floyd shortest path algorithm before the electric automobile reaches the stop time; when the electric automobile runs in a road network, if the state of charge of the electric automobile reaches a threshold value of 0.2 or below, the electric automobile searches a nearest charging place for quick charging, then continues to run until the running stop time is reached, finally judges whether the SOC of the electric automobile is less than 0.6 when the electric automobile stops, if so, the electric automobile is slowly charged, otherwise, the electric automobile is not charged.
9. The method of claim 1, wherein the method comprises the steps of:
in step S8: based on a load prediction result, constructing a power distribution network planning model containing an electric vehicle charging station, and optimizing a location and volume result of the charging station based on an NSGA-III algorithm by taking the minimum planning cost of the charging station, the minimum rapid charging load fluctuation, the highest charging station utilization rate and the best power grid voltage quality as targets;
the method comprises the following steps of constructing a power distribution network planning model containing the electric vehicle charging station by taking the minimum planning cost of the charging station, the minimum rapid charging load fluctuation, the maximum charging station utilization rate and the best power grid voltage quality as targets, and concretely referring to the formula (7) -10:
charging station investment cost F 1
min F 1 ＝C cdz +C OM (7)
In the formula: c cdz For equivalent annual construction costs of charging stations, C OM Maintenance costs for the charging station;
annual rapid charge fluctuation F 2
In the formula: CEV k For charging the capacity of the station k, PEV i,j For the fast charging load of j hours on the ith day of the charging station k, PEV i,j,yave K is the average fast charge of the charging station for one year;
annual charging station utilization rate F 3
In the formula: PEV i,j For the fast charging load, CEV, of charging station k at day i and hour j k Is the capacity of the charging station k;
distribution network annual voltage offset F 4
In the formula: m is the number of nodes of the power distribution network; k is the node number of the power distribution network; u shape i,j,k And U i,j,kN Actual voltage and rated voltage at node k at j hour of day i;
the constraint conditions comprise power flow constraint, node voltage constraint, branch circuit capacity constraint and electric vehicle charging station installation capacity constraint, and are respectively expressed as formulas (11) - (14):
U imin ≤U i ≤U imax (12)
S l ≤S lmax l＝1,2,…,L (13)
in the formula: p i 、Q i Respectively injecting active power and reactive power into the node i; u shape i 、U j Voltages of nodes i and j, respectively; g ij 、B ij The conductance and susceptance are divided into branches ij; theta ij Is the phase angle difference of the voltages between nodes i, j; u shape imin 、U imax The upper limit and the lower limit of the voltage of the node i are respectively; s l 、S lmax The capacity and the maximum limit capacity of the branch l are respectively; l is the total number of branches;
the upper limit and the lower limit of the installation capacity of the electric vehicle charging station of the node k are respectively;
and then solving the power distribution network planning model containing the electric vehicle charging station by adopting an evolutionary multi-objective optimization algorithm NSGA-III based on a non-dominated sorting method of the reference points.
10. The method of claim 1, wherein the method comprises the steps of:
further comprising step S9: in order to verify the influence of uncertainty of the charging mode of an electric vehicle owner on joint planning of a power distribution network, the following 3 schemes are constructed for simulation analysis: scheme A assumes that the owner of the electric vehicle is charged disorderly and does not consider the upper charging limit of the charging station; scheme B assumes that the owners of the electric vehicles are guided to charge in order, and the charging upper limit of the charging station is set to control the charging quantity; according to the scheme C, on the basis of the scheme B, idle electric automobile vehicles in a road network are considered to voluntarily participate in the V2G behavior under the ordered guidance strategy;
and comparing and analyzing the rationality of the distribution network planning result according to different charging schemes.
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