CN111047120A - Electric vehicle charging load prediction method under circuit-electric coupling network - Google Patents

Electric vehicle charging load prediction method under circuit-electric coupling network Download PDF

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CN111047120A
CN111047120A CN202010052896.3A CN202010052896A CN111047120A CN 111047120 A CN111047120 A CN 111047120A CN 202010052896 A CN202010052896 A CN 202010052896A CN 111047120 A CN111047120 A CN 111047120A
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袁泉
汤奕
王�琦
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Abstract

The invention discloses a method for predicting charging load of an electric automobile under a road-electric coupling network, which comprises the steps of firstly providing a method for predicting the charging load of the electric automobile, wherein the method comprises a single charging load prediction model of the electric automobile and a polymerization charging load prediction model of the electric automobile, and accurately predicting the time-space distribution characteristic of the charging load of the electric automobile while considering the influence of road and traffic information on the driving behavior of the electric automobile; then, providing a road-electric coupling network framework, performing abstract modeling by using the network topology of an actual road and a power grid, and providing a road-electric coupling network load power reduction method to couple the charging power of the electric automobile in the road network with the load power except the electric automobile in the power grid; finally, the load prediction method is used for accurately predicting the space-time distribution of the large-scale electric vehicle charging load, and providing help for planning and running of a power grid.

Description

Electric vehicle charging load prediction method under circuit-electric coupling network
Technical Field
The invention belongs to the field of planning and operation of power systems, and particularly relates to a method for predicting charging load of an electric vehicle under a circuit-electric coupling network.
Background
With the increasing severity of global fossil energy exhaustion and environmental pollution problems, electric vehicles are receiving attention due to their good energy-saving and emission-reducing effects. Meanwhile, the rapid increase of the electric automobile reserves becomes a necessary trend due to the development of electric automobile technology and the economic subsidy of governments, and the charging load of the electric automobile also gradually becomes an important component of the power grid load. However, under the condition of high reserve of future electric vehicles, a certain burden is brought to a power grid by large-scale disordered charging, and the problems of overlarge voltage deviation, reduced power quality and the like can be caused by large power impact, and even the safe and stable operation of a power system is affected in severe cases. Therefore, solving the problem of disordered charging of large-scale electric vehicles is an indispensable part in planning and running of the power system.
The premise of reasonably regulating and controlling the electric automobile is that the charging load of the electric automobile is accurately predicted, the charging load is influenced by various factors including date, weather, traffic flow and user wishes, certain randomness and uncertainty exist, and how to accurately evaluate the space-time distribution of the charging load of the electric automobile is a major difficulty in research.
At present, most of modeling about electric vehicle load prediction only establishes a static model and lacks consideration on road and traffic information, and actually, information of a road network and a power grid is combined to be really needed for accurately evaluating the space-time distribution of the electric vehicle charging load.
Disclosure of Invention
The invention aims to solve the problems and provides a method for predicting the charging load of the electric automobile under a circuit-electric coupling network, which can accurately predict the space-time distribution of the charging load of the large-scale electric automobile and provide help for planning and running a power grid.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for predicting charging load of an electric vehicle under a circuit-electric coupling network comprises the following steps:
1. the method comprises the following steps of establishing an electric vehicle single body charging load prediction model considering road traffic information, wherein the specific modeling process is as follows:
1) establishing road network topology and road network distance matrix
Main road intersection points in road topology are abstracted into road network nodes s, and the set of the road network nodes s is represented as
Figure BDA0002371828620000021
The distance matrix represents the road distance between two adjacent nodes as follows:
Figure BDA0002371828620000022
in matrix DijThe value of (D) is the road distance between nodes ijijWhen 0 denotes node coincidence, DijAnd when ∞ indicates that the nodes are not connected.
2) Determining departure time and place of electric vehicle based on historical data and Monte Carlo simulation
The probability of a certain vehicle appearing at the s position at the t moment is obtained according to the traffic flow statistical data as follows:
Figure BDA0002371828620000023
the start position of the nth vehicle is determined by Monte Carlo sampling
Figure BDA0002371828620000024
Departure time
Figure BDA0002371828620000025
Where n is the vehicle number.
And determining the end point of each trip of each vehicle by using a Monte Carlo sampling method
Figure BDA0002371828620000026
Finding the shortest path of the driving path by a Dijkstra algorithm, wherein the path passes through k nodes { S }1,……,SkAnd is provided with
Figure BDA0002371828620000027
3) Calculating the time and the electric quantity when the electric automobile reaches the terminal
vij,tThe average driving speed of the automobile between roads ij at the time t, in the unit of km/h, is closely related to the road grade, the time, the per-capita automobile holding capacity, the road traffic flow and other factors, and the calculation method is as follows:
Figure BDA0002371828620000028
in the formula α1、α2、α3And β is regression parameter and correction coefficient, which varies with road grade, v0Designing the speed of each grade of road in km/h; cij,tIndicating the traffic flow at time t between roads ij, Cij,0The traffic capacity of the road ij is shown in veh/h.
The method for calculating the residual capacity of the electric automobile at the time t comprises the following steps:
SOCt＝η(SOCt-1-Δl·ΔSOC)
in the formula: SOCt-1The electric quantity of the electric automobile at the last electric quantity detection moment is represented, delta l represents the driving distance from t-1 to t, delta SOC is the electric consumption of the electric automobile per kilometer, η is the electric energy efficiency coefficient of the electric automobile driving and is used for representing energy loss generated by starting and braking.
The method for calculating the time to reach the end point comprises the following steps:
Figure BDA0002371828620000031
in the formula: dij,dIndicating the distance from the (d-1) th node to the d-th node on the way,
Figure BDA0002371828620000032
indicating the average speed of travel of the vehicle over the course. t is tchargeIndicating the charging period, twaitThe charging queuing time is represented, Monte Carlo analog sampling is used for obtaining, and when the electric quantity is sufficient and quick charging is not needed, the charging queuing time and the charging queuing time are both 0.
2, establishing an electric vehicle aggregated charging load prediction model considering the behavior characteristics of the electric vehicle, wherein the specific modeling process is as follows:
1) electric vehicle charging demand judgment
Figure BDA0002371828620000033
If the quick charge starting time is tfQuick charge duration Δ tfThen, order:
Figure BDA0002371828620000034
if the slow charging starting time is tgSlow charging duration Δ tgThen, order:
Figure BDA0002371828620000035
2) establishing a fast charging state matrix and a slow charging state matrix of a private car
Figure BDA0002371828620000041
Figure BDA0002371828620000042
Figure BDA0002371828620000043
Is a matrix of S x T, and is,
Figure BDA0002371828620000044
indicating that the nth private car is performing quick charging at the time of the node t. It should be noted that the charging is performed only when the private car is in quick charge
Figure BDA0002371828620000045
Rest time
Figure BDA0002371828620000046
And the electric automobile can be charged only at one position at each time t, namely, the value of 1 appears in at most 1 row of each column.
Figure BDA0002371828620000047
Is a matrix of S x T, and is,
Figure BDA0002371828620000048
the method indicates that the nth private car is slowly charged at the time of the node t and only when the private car is slowly charged
Figure BDA0002371828620000049
Rest time
Figure BDA00023718286200000410
Similarly, replacing all superscripts with 2 indicates the state of charge of the second type of vehicle, i.e.
Figure BDA00023718286200000411
And
Figure BDA00023718286200000412
respectively representing fast charging state matrixes and slow charging state matrixes of the electric taxi; the superscript being replaced by 3 indicates the state of charge of the third vehicle class, i.e.
Figure BDA00023718286200000413
And
Figure BDA00023718286200000414
respectively are the fast charging state matrix and the slow charging state matrix of the electric bus.
3) Establishing large-scale electric automobile polymerization charging model
Figure BDA00023718286200000415
In the formula:
Figure BDA00023718286200000416
power is aggregated for the charging load at the node at time t. N is a radical of1Number of electric private cars, N2For number of electric taxi cars, N3The number of the electric buses. Because the first type and the second type of vehicles are all small-sized vehicles, the charging power is uniformly set,
Figure BDA00023718286200000420
the charging power is used for rapidly charging the electric passenger vehicle,
Figure BDA00023718286200000417
the slow charging power is supplied to the electric passenger vehicle,
Figure BDA00023718286200000418
the charging power when the electric bus is charged rapidly,
Figure BDA00023718286200000419
and slowly charging the electric bus with charging power.
3. The method comprises the following steps of constructing a circuit-electric coupling network and performing power coupling operation:
1) establishing network grid coupling principle
Definition symbol
Figure BDA0002371828620000059
The method is characterized in that a road network node s is coupled with a power grid node z, and | s-z | is defined as the distance between the road network node s and the power grid node z. Defining the road network-power grid coupling nodes by the number r of road network nodes and the number h of power grid nodes
Figure BDA0002371828620000051
The number of coupled nodes, y, is min (r, h).
Figure BDA0002371828620000052
In the formula: xiFor the ith coupling node, from the jth grid node ZjAnd the kth road network node SkIs coupled to give, and ZjIs a distance SkThe nearest one of the grid nodes. Meanwhile, since a plurality of network nodes may correspond to one grid node, the limitation of node capacity must be considered.
Figure BDA0002371828620000053
Is all with ZjCoupled road network node SkSum of medium EV maximum aggregate charging power.
Figure BDA0002371828620000054
For grid node ZjThe sum of the maximum power consumption of loads except the medium electric automobile must be less than ZjRated power of
Figure BDA0002371828620000055
If Sk-1Coupled into ZjCan satisfy the node capacity limitation, and SkCoupled into ZjIf the node capacity limit cannot be satisfied, S iskFinding next nearest grid node
Figure BDA0002371828620000056
Namely when:
Figure BDA0002371828620000057
time of flight
Figure BDA0002371828620000058
And if the capacity limit of the node cannot be met, searching the next nearest power grid node, and so on.
2) Providing a calculation formula of the power of the circuit-electric coupling network
The load power of each coupling node is the sum of the total charging power of the electric automobile at the road network node and the total power of the non-electric automobile equipment at the power grid node, and the following formula is satisfied:
Figure BDA0002371828620000061
compared with the prior art, the invention has the following advantages:
the method and the device consider the influence of traffic information on the driving behavior and the charging demand of the electric automobile, can more accurately evaluate the time-space distribution characteristic of the charging load of the electric automobile, are beneficial to reasonably scheduling the large-scale disordered charging load of the electric automobile by the power grid, and have significance for planning and running the power grid.
Drawings
FIG. 1 is a schematic diagram of a circuit-electrical coupling network and its interaction;
FIG. 2 is a simplified topology diagram of an actual circuit-to-electric coupling network;
FIG. 3 shows the prediction result of the temporal-spatial distribution of the charging load of the electric vehicle.
Detailed Description
The invention is described in further detail below with reference to embodiments and with reference to the drawings. It should be noted that in the given embodiment, electric private cars, electric taxis and electric buses are taken as objects of the charging load prediction, but the present invention is not limited to the given embodiment, and setting different electric vehicle types only has an influence on the charging power, and the principle is the same. Any work done in accordance with the line-to-line coupling concept and load prediction method presented herein is within the scope of protection.
In this embodiment, a city is selected as a research object, and a road-electric coupling network (a coupling network topology is shown in fig. 2) is established based on a road topology and a power grid topology, and includes 43 coupling nodes.
The types of electric vehicles are divided into 3 types, and each type of electric vehicle has different behavior rules. The first type is an electric passenger vehicle which is mostly a private car or a working vehicle, the traveling of the electric passenger vehicle has a certain rule, the slow charging power is low, generally 10kW, the fast charging power is low, generally 50kW or so; the second type is an electric taxi, the trip is relatively irregular, and the charging power is almost the same as that of the first type; the third type is an electric bus, although the travel of the electric bus has certain rules, the slow charging power is larger, generally about 60kW, the fast charging power is very large, and some of the fast charging power even exceed 400kW, and the average value is 200 kW.
The number of various types of electric automobiles put into use in 2020 is estimated based on the existing electric automobile holdup and the annual growth rate in the market. 130000 electric private cars, 10000 electric taxis and 7000 electric buses are expected to be reserved in 2020 of the city. The final results of the spatio-temporal prediction of aggregate charging load based on population density and functional conditions (e.g., commercial district, industrial district, etc.) in different regions of the city are shown in fig. 2.
It can be seen that: 1) the aggregate charging load of each node has larger difference, the charging load of No. 10-16 nodes is the most, and the charging load is high because the No. 10-16 nodes are at the position with the most commercial circle density;
2) the shape of the polymeric charge load curve in each node in one day is similar, and the time of the appearance of the valley is close to the time of the peak, and the curve is 18: 00-day 2: 00, charge load peak, and 2: 00-6: 00 is the load trough. Description 2: 00-6: 00 has very high scheduling potential, if 18: 00-day 2: the load part of 00 is transferred to the time period, which is beneficial to peak clipping and valley filling of the power grid, stabilizing the load fluctuation and enhancing the safety and stability of the power grid operation.

Claims (4)

1. A method for predicting charging load of an electric vehicle under a circuit-electric coupling network is characterized by comprising the following steps:
step 1: the method comprises the steps of establishing a single charging load prediction model of the electric automobile by considering road and traffic information;
step 2: the method comprises the steps of establishing an electric automobile aggregate charging load prediction model by considering behavior characteristics of different types of electric automobiles;
and step 3: and establishing a circuit-electric coupling network and performing power coupling operation based on the actual road network, the power grid topology and the aggregated charging load space-time distribution.
2. The method for predicting the charging load of the electric automobile under the circuit-electric coupling network according to claim 1, wherein the method comprises the following steps: the establishment of the electric vehicle single body charging load prediction model in the step 1 is specifically realized by the following steps:
1-1) establishing road network topology and road network distance matrix
Main road intersection points in road topology are abstracted into road network nodes s, and the set of the road network nodes s is represented as
Figure FDA0002371828610000011
The distance matrix represents the road distance between two adjacent nodes as follows:
Figure FDA0002371828610000012
in matrix DijThe value of (D) is the road distance between nodes ijijWhen 0 denotes node coincidence, DijInfinity means that the nodes are not connected;
1-2) determining departure time and location of electric vehicle based on historical data and Monte Carlo simulation
The probability of a certain vehicle appearing at the s position at the t moment is obtained according to the traffic flow statistical data as follows:
Figure FDA0002371828610000013
the start position of the nth vehicle is determined by Monte Carlo sampling
Figure FDA0002371828610000014
Departure time
Figure FDA0002371828610000015
Wherein n is a vehicle number;
and determining the end point of each trip of each vehicle by using a Monte Carlo sampling method
Figure FDA0002371828610000021
Finding the shortest path of the driving path by a Dijkstra algorithm, wherein the path passes through k nodes { S }1,……,SkAnd is provided with
Figure FDA0002371828610000022
1-3) calculating the time and the electric quantity when the electric automobile reaches the end point
vij,tThe average driving speed of the automobile between roads ij at the time t, in the unit of km/h, is closely related to the road grade, the time, the per-capita automobile holding capacity, the road traffic flow and other factors, and the calculation method is as follows:
Figure FDA0002371828610000023
in the formula α1、α2、α3And β is regression parameter and correction coefficient, which varies with road grade, v0Designing the speed of each grade of road in km/h; cij,tIndicating the traffic flow at time t between roads ij, Cij,0The traffic capacity of the road ij is unit veh/h;
the method for calculating the residual capacity of the electric automobile at the time t comprises the following steps:
SOCt＝η(SOCt-1-Δl·ΔSOC)
in the formula: SOCt-1The method comprises the steps of representing the electric quantity of the electric automobile at the last electric quantity detection moment, representing a driving distance from t-1 to t, representing the electric quantity of the electric automobile per kilometer, representing the electric quantity SOC, representing the energy efficiency coefficient of the electric automobile driving, and representing energy loss generated by starting and braking, judging the electric quantity at certain intervals during the driving of the electric automobile, and rapidly charging the electric automobile in a charging pile at the nearest node when the residual electric quantity is smaller than a certain value;
the method for calculating the time to reach the end point comprises the following steps:
Figure FDA0002371828610000024
in the formula: dij,dIndicating the distance from the (d-1) th node to the d-th node on the way,
Figure FDA0002371828610000025
indicating the average speed of travel of the vehicle over the course. t is tchargeIndicating the charging period, twaitThe charging queuing time is represented, Monte Carlo analog sampling is used for obtaining, and when the electric quantity is sufficient and quick charging is not needed, the charging queuing time and the charging queuing time are both 0.
3. The method for predicting the charging load of the electric automobile under the circuit-electric coupling network according to claim 1, wherein the method comprises the following steps: the specific method of the step 2 comprises the following steps: the method comprises the following steps of dividing different types of electric automobiles into three types with different behavior rules, namely electric private cars, electric taxis and electric buses, judging the electric quantity and the charging requirement of each type of automobile to obtain a fast charging state matrix and a slow charging state matrix, and calculating the aggregate charging load of the electric automobiles through an aggregation model;
(2-1) electric vehicle charging state judgment as follows:
Figure FDA0002371828610000031
if the quick charge starting time is tfQuick charge duration Δ tfThen, order:
Figure FDA0002371828610000032
if the slow charging starting time is tgSlow charging duration Δ tgThen, order:
Figure FDA0002371828610000033
(2-2) establishing a private car rapid charging state matrix
Figure FDA0002371828610000034
And a slow charging state matrix
Figure FDA0002371828610000035
Figure FDA0002371828610000036
Figure FDA0002371828610000037
Figure FDA0002371828610000038
Is a matrix of S x T, and is,
Figure FDA0002371828610000039
the method comprises the steps that the nth private car is rapidly charged at the time of the node t; only when the private car is charged rapidly
Figure FDA00023718286100000310
Rest time
Figure FDA00023718286100000311
At each moment t, the electric automobile can be charged at only one position, namely, at most 1 row of each column has a value of 1;
Figure FDA00023718286100000312
is a matrix of S x T, and is,
Figure FDA00023718286100000313
the method indicates that the nth private car is slowly charged at the time of the node t and only when the private car is slowly charged
Figure FDA00023718286100000314
Rest time
Figure FDA00023718286100000315
Replacing all superscripts with 2 indicates the state of charge of the second type of vehicle, i.e.
Figure FDA00023718286100000316
And
Figure FDA00023718286100000317
respectively representing fast charging state matrixes and slow charging state matrixes of the electric taxi; the superscript being replaced by 3 indicates the state of charge of the third vehicle class, i.e.
Figure FDA0002371828610000041
And
Figure FDA0002371828610000042
respectively are fast charging state matrixes and slow charging state matrixes of the electric buses;
(2-3) establishing a large-scale electric automobile polymerization charging model:
Figure FDA0002371828610000043
in the formula:
Figure FDA0002371828610000044
power is aggregated for the charging load at the node at time t. N is a radical of1Number of electric private cars, N2For number of electric taxi cars, N3The number of the electric buses. Because the first type and the second type of vehicles are all small-sized vehicles, the charging power is uniformly set,
Figure FDA0002371828610000045
the charging power is used for rapidly charging the electric passenger vehicle,
Figure FDA0002371828610000046
the slow charging power is supplied to the electric passenger vehicle,
Figure FDA0002371828610000047
the charging power when the electric bus is charged rapidly,
Figure FDA0002371828610000048
and slowly charging the electric bus with charging power.
4. The method for calculating the power coupling of the circuit-electric coupling network according to claim 1, wherein: the specific method of the step 3 comprises the following steps:
110kV bus in regional power grid is used as power grid node
Figure FDA0002371828610000049
Coupling the power grid nodes and the road network nodes according to a principle of simultaneously considering the geographic position and the node capacity;
definition symbol
Figure FDA00023718286100000410
The method is characterized in that a road network node s is coupled with a power grid node z, and | s-z | is defined as the distance between the road network node s and the power grid node z. Defining the road network-power grid coupling nodes by the number r of road network nodes and the number h of power grid nodes
Figure FDA00023718286100000411
The number of coupled nodes, y, is min (r, h);
Figure FDA00023718286100000412
Figure FDA00023718286100000413
Figure FDA00023718286100000414
in the formula: xiFor the ith coupling node, from the jth grid node ZjAnd the kth road network node SkIs coupled to give, and ZjIs a distance SkThe nearest one of the grid nodes. Meanwhile, a plurality of road network nodes may appearIn the case of a point corresponding to a grid node, the limitation of node capacity must be considered.
Figure FDA00023718286100000415
Is all with ZjCoupled road network node SkSum of medium EV maximum aggregate charging power.
Figure FDA00023718286100000416
For grid node ZjThe sum of the maximum power consumption of loads except the medium electric automobile must be less than ZjRated power of
Figure FDA0002371828610000051
If Sk-1Coupled into ZjCan satisfy the node capacity limitation, and SkCoupled into ZjIf the node capacity limit cannot be satisfied, S iskFinding next nearest grid node
Figure FDA0002371828610000052
Namely when:
Figure FDA0002371828610000053
time of flight
Figure FDA0002371828610000054
Figure FDA0002371828610000055
Figure FDA0002371828610000056
If the capacity limit of the node cannot be met, searching the next nearest power grid node, and so on;
the load power of each coupling node is the sum of the total charging power of the electric automobile at the road network node and the total power of the non-electric automobile equipment at the power grid node, and the following formula is satisfied:
Figure FDA0002371828610000057
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CN112508364A (en) * 2020-11-26 2021-03-16 东南大学 Weight quantification method for electric vehicle charging decision
CN112736945A (en) * 2020-12-17 2021-04-30 国网浙江省电力有限公司嘉兴供电公司 Electric vehicle charging regulation and control method and terminal based on dynamic electricity price
CN114707292A (en) * 2022-01-21 2022-07-05 东北电力大学 Voltage stability analysis method for power distribution network containing electric automobile
CN114707292B (en) * 2022-01-21 2024-05-24 东北电力大学 Analysis method for voltage stability of distribution network containing electric automobile

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