CN112993980A - Electric vehicle charging load space-time probability distribution model calculation method - Google Patents

Electric vehicle charging load space-time probability distribution model calculation method Download PDF

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CN112993980A
CN112993980A CN202110201648.5A CN202110201648A CN112993980A CN 112993980 A CN112993980 A CN 112993980A CN 202110201648 A CN202110201648 A CN 202110201648A CN 112993980 A CN112993980 A CN 112993980A
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vehicle
soc
time
vehicles
travel
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CN112993980B (en
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张杨
吴国诚
倪筹帷
郭力
刘一欣
马瑜涵
张宇轩
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Tianjin University
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Tianjin University
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]

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  • Power Engineering (AREA)
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Abstract

The invention relates to the technical field of a charging load space-time probability distribution model of an electric automobile, in particular to a charging load space-time probability distribution model calculation method of the electric automobile, which comprises the steps of firstly utilizing vehicle traveling statistical data of an OD matrix, combining the population number of road network nodes, the vehicle travel time consumption, the path selection and the vehicle charging process, and calculating the single traveling probability distribution of the same type of vehicles in a road network and the space-time probability distribution of the single traveling charging power of the single vehicle; and then, by combining traffic distribution conditions and travel time consumption and energy consumption characteristics of vehicles, the space-time probability distribution of the electric vehicle charging load in the road network is calculated by utilizing the node comprehensive SOC and the traffic flow group comprehensive SOC probability density function, so that the calculation is more accurate and the calculation speed is high.

Description

Electric vehicle charging load space-time probability distribution model calculation method
Technical Field
The invention relates to the technical field of electric vehicle charging load space-time probability distribution models, in particular to a calculation method of an electric vehicle charging load space-time probability distribution model.
Background
With the large-scale popularization of electric vehicles in the future, the proportion of the charging power of the electric vehicles in a power distribution system is gradually increased, and new challenges are brought to the planning and operation of the power distribution network. The charging power of the electric automobile is accurately predicted, and the method has important significance for the operation safety and the economical efficiency of the power distribution network.
Because the charging characteristics of the electric vehicle are closely related to the traveling characteristics, the topological structure of the traffic network and the traffic level, the influence of the traveling characteristics of the vehicle, the urban road network structure, the traffic jam condition and other factors needs to be comprehensively considered in modeling the charging load of the electric vehicle. Great randomness and uncertainty exist in vehicle traveling, so that the existing load modeling mainly focuses on probability modeling.
In the existing electric vehicle charging load modeling, a large amount of research is carried out probability modeling from the angle of statistics or the angle of simulating vehicle traveling, and a complete probability model is constructed. While the Monte Carlo simulation method or a large number of sample methods similar in principle are generally adopted in the solving process, and the solving method is low in calculation efficiency. The time consumption of the charging process of a large-scale vehicle under the simulated traffic environment is high, only the effective expected charging power value can be obtained, and the calculated amount can be further increased when the variance value is calculated.
In addition, because a large number of sampling parameters exist in the traditional monte carlo simulation, wherein certain coupling relations exist in the time when the vehicle arrives at the destination, the function type of the destination, the parking time and the like in practice, and the coupling relations of all the parameters are difficult to consider in the probability modeling.
In summary, the existing methods still have the following defects and shortcomings:
(1) in the current model, logical modeling is carried out according to the actual specific trip flow of the vehicle, so that a probability model has a large number of coupling parameters, and the probability model is difficult to be considered in actual calculation.
(2) In the existing probability model considering the charging power space-time distribution of traffic conditions, the charging power space-time distribution is solved mainly by a Monte Carlo simulation or software simulation method, and an analysis method capable of high-speed calculation is lacked.
Disclosure of Invention
The present application aims to solve at least to some extent one of the above-mentioned drawbacks and technical problems.
Therefore, the application aims to provide a calculation method of a space-time probability distribution model of the charging load of the electric vehicle, the calculation method can calculate the space-time probability distribution of the charging load of the electric vehicle by constructing the single trip probability distribution of the same type of vehicle, combining the traffic distribution condition and the trip time consumption and energy consumption characteristics of the vehicle and utilizing the node comprehensive SOC and the traffic flow group comprehensive SOC probability density function, and the calculation is more accurate and has high calculation speed.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for calculating a space-time probability distribution model of charging load of an electric automobile,
firstly, calculating single trip probability distribution of vehicles of the same type and space-time probability distribution of single trip charging power of the vehicles in a road network by using vehicle trip statistical data of an OD matrix and combining the population number of road network nodes, vehicle travel time consumption, path selection and vehicle charging process;
and calculating the space-time probability distribution of the charging load of the electric automobile in the road network by utilizing the node comprehensive SOC and the traffic flow group comprehensive SOC probability density function by combining the traffic distribution condition and the travel time consumption and energy consumption characteristics of the vehicle.
The further preferable scheme of the invention is as follows: the steps of utilizing the OD matrix for vehicle traveling statistical data are as follows:
dividing the road network nodes into at least N types according to functions, and forming at least N according to the combination of origin orgin and destination2Each OD pair, N is more than or equal to 3;
counting travel data of a certain number of vehicles in a road network node area in one day, wherein the travel data comprise DO pair types of a departure place-destination of each travel of the vehicles in the day and an initial departure time;
fitting is carried out by counting travel data of vehicles to obtain a joint probability distribution matrix P of different types of OD (origin-destination) pairs and timeODT。
The further preferable scheme of the invention is as follows: combining the population number of the road network nodes, the method comprises the following steps:
distributing weight W to the nodes of the road network according to the population number and obtaining the node n in the T type areajProbability P ofn(T,nj)，
Figure BDA0002947828020000021
Binding of PODTGenerating an OD matrix, i.e. the probability P that OD occurs for i at time tOD(i,t)，
POD(i,ts)＝Pn(TOD,i,d,des(i))·PODT(TOD,i,ts)Pn(TOD,i,s,sta(i))
In the formula, TOD,iIs the type of OD to i; t isOD,i,s、TOD,i,dRespectively representing the node types of the starting point and the end point of the OD pair i; sta (-) and des (-) respectively calculate the numbers of the origin node and the focus node of OD pair i.
The further preferable scheme of the invention is as follows: the vehicle travel time is the time consumed by paths of different OD pairs, and the calculation steps are as follows:
calculating the t moment by using a BPR model which is a practical vehicle speed and flow modelOD is used for the time consumed by i in the journey and the expected value matrix N of the distribution of the vehicles on the roadcr；
And calculating the path consumption time of different OD pairs according to the normal distribution characteristic of the median of the matrix.
The further preferable scheme of the invention is as follows: in the routing of the vehicle, the vehicle has PstIs selected to travel in the shortest time path, 1-PstThe probability of the route is traveled by the shortest route, and the travel route is calculated by using a Dijkstra algorithm based on the adjacent matrixes of the travel time and the length of the road section; during the calculation process, the energy consumption of the default vehicle is in a linear relationship with the form mileage.
The further preferable scheme of the invention is as follows: in the vehicle charging process, the SOC probability density function at the end of the vehicle trip of OD to i is fODa,i(s)；
Charging time t after vehicle arrives at destinationcProbability density function fODa,ct,i(tc) From fODa,i(s) is obtained by linear variation of the formula
Figure BDA0002947828020000031
Introducing compensation coefficient c (n) at the same timejT) to compensate for vehicles leaving midway due to travel demand, the physical meaning of which is the node n at time tjThe remaining part of the charging power due to the departure of the vehicle
Figure BDA0002947828020000032
In the formula, NNN(nj,t)，NNL(nj,t)，NNA(njT) are respectively the nodes n at the time tjThe number of parked vehicles, the number of departing vehicles and the number of arriving vehicles;
the probability P of charging at time t in one trip of the vehicle can be obtainedC(njAnd t) is:
Figure BDA0002947828020000033
Figure BDA0002947828020000034
Figure BDA0002947828020000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000041
to represent
Figure BDA0002947828020000042
The probability density function of the travel time of the trip,
Figure BDA0002947828020000043
is a node njThe rated charging power of.
The further preferable scheme of the invention is as follows: the node comprehensive SOC is the total SOC probability distribution of all vehicles of the same type parked under a certain node; the calculation process is as follows:
s21: calculating the number distribution of the vehicles leaving the departure point;
s22: calculating a vehicle group SOC probability distribution for an off-node
S23: calculating the number distribution of vehicles arriving at the destination;
s24: calculating the comprehensive SOC of the arriving destination vehicle group, and weighting the comprehensive SOC probability density functions of the two vehicle groups with different path decisions;
s25: calculating the number of vehicles parked under the nodes;
s26: comprehensive SOC change conditions before and after charging;
s27: and (4) node synthesis SOC probability density function synthesis change.
The further preferable scheme of the invention is as follows: in the calculation process of step S21: vehicles departing from the departure point contain only First and Travel types, at tsStarting at any momentIs leaving node njRespectively is NNL,first(i, t) and NNL,travel(i,t)；
Figure BDA0002947828020000044
Figure BDA0002947828020000045
Figure BDA0002947828020000046
NNL,first(i, t) and NNL,travel(i, t) are both normally distributed;
in the calculation process of step S22: in one OD pair at the same time, the distribution of the initial SOC of all vehicles is completely consistent, and the OD pair ODi,tHas an initial SOC probability density function of fODl,i,t(s)，
Figure BDA0002947828020000047
In the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000048
respectively, a node n at time tjA probability density function of the mid-start SOC and the trip SOC;
in the calculation process of step S23: the vehicles arriving at the destination comprise a Travel type and a Final type in the destination;
arriving at node njNumber of vehicles N of medium Travel and Final typeNA,travel(nj,t)、NNA,final(njT) the calculation formula is as follows
Figure BDA0002947828020000051
Figure BDA0002947828020000052
Figure BDA0002947828020000053
Figure BDA0002947828020000054
Figure BDA0002947828020000055
In the calculation process of step S24: traffic flow comprehensive SOC probability density function f of all OD pairs i arriving at destination at time tODA,i,t(s)，
Figure BDA0002947828020000056
Figure BDA0002947828020000057
Figure BDA0002947828020000058
In the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000059
are each tsThe OD starting at the moment is a travel time probability density function for the i trip decision by the shortest path and the shortest time;
to the destination node njThe SOC probability densities of the middle Travel and Final type vehicles are respectively
Figure BDA0002947828020000061
It is obtained by the accumulation calculation of OD pairs;
in the calculation process of step S25: the parking numbers of the First type vehicle, the Travel type vehicle and the Final type 3 vehicle are respectively NNN,first(nj,t)、NNN,travel(nj,t)、NNN,final(njT) are respectively
Figure BDA0002947828020000062
Figure BDA0002947828020000063
Figure BDA0002947828020000064
In the formula, taHas a physical meaning of taThe moment arrives;
in the calculation process of step S26: the comprehensive SOC change conditions before and after charging are reflected through the superposition translation operator;
the calculation method of the translation operator is as follows:
Figure BDA0002947828020000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000066
for translation operators, t is representedaThe process that the SOC probability density function of the newly arrived vehicle at the time OD to i translates to the right along with the charging in the first charging period;
Figure BDA0002947828020000067
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000068
synthesizing a translation operator of the SOC for the node;
in the calculation process of step S27: node comprehensive SOC probability density function changes caused by vehicle arrival and vehicle departure are obtained by weighting probability density functions of the number of vehicles according to the SOC distribution of the arrival and departure vehicle group; superposing translation operators in the charging process; the change formula of the node comprehensive SOC probability density function obtained after simplifying the calculation result is
Figure BDA0002947828020000071
In summary, compared with the existing calculation method, the method has the following main characteristics:
(1) the trip behavior, the charging decision behavior, the vehicle SOC state, the driving process, the charging process and the like of the vehicle can be completely described in a probability manner, and probability distribution functions of all parameters are given;
(2) the parking time-arrival time caused by multiple trips and the coupling error between the arrival time and the parking time can be avoided in the aspect of describing the trip characteristics of the vehicle, so that the trip probability of the vehicle can be more accurately described;
(3) the analytical calculation method is adopted to carry out comprehensive calculation on a large number of existing uncertain parameters, and the calculation speed is greatly higher than that of the common Monte Carlo simulation method and other methods;
(4) the calculation speed is irrelevant to the number of the simulated vehicles, and the calculation advantage is more obvious when the size of the number of the simulated vehicles is large.
Drawings
FIG. 1 is a block diagram of the structure of key variables in the calculation method of the present invention.
Fig. 2 is a schematic diagram of a calculation structure of the integrated SOC distribution in the calculation method of the present invention.
FIG. 3 is a schematic diagram of the transmission situation of the integrated SOC of different nodes in the calculation method of the present invention.
Fig. 4 is a schematic diagram illustrating the influence of charging behavior on the node integrated SOC in the calculation method of the present invention, where the left side is the SOC distribution variation of the vehicle during charging, and the right side is the variation of the node integrated SOC.
FIG. 5 is a road network topology diagram of an embodiment.
FIG. 6 is a comparison graph before and after iterative convergence of traffic distribution in the embodiment.
Fig. 7 is a traffic flow group integrated SOC probability density distribution surface diagram arriving at the node 11 at different times throughout the day according to the embodiment.
Fig. 8 is a graph of the integrated SOC probability density distribution curve of the Travel type vehicle at the node 11 at different times throughout the day according to the embodiment.
Fig. 9 is a Final type vehicle integrated SOC probability density distribution surface map at different time nodes 11 throughout the day according to the embodiment.
Fig. 10 is a power probability distribution thermodynamic diagram for a node 9 all day charge in an embodiment.
Fig. 11 is a comparison graph of the monte carlo simulation solution result of the present invention and the analytic solution result of the present invention at 95% confidence intervals of the charging power probability distribution of 3 different types of nodes 9,11,12 according to the embodiment.
Fig. 12 is a comparison graph of the 95% confidence interval of the charging power probability distribution of the 3 different types of nodes 9,11,12 according to the traditional common MAS method monte carlo simulation solution result and the analytic solution result of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications without inventive contribution to the present embodiment as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
As shown in fig. 1, the invention provides a method for calculating a space-time probability distribution model of an electric vehicle charging load, which includes the steps of firstly calculating single-trip probability distribution of vehicles of the same type and space-time probability distribution of single-trip charging power of the vehicles in a road network by using vehicle trip statistical data of an OD matrix and combining the population number of road network nodes, vehicle travel time consumption, path selection and vehicle charging process. And calculating the space-time probability distribution of the charging load of the electric automobile in the road network by utilizing the node comprehensive SOC and the traffic flow group comprehensive SOC probability density function by combining the traffic distribution condition and the travel time consumption and energy consumption characteristics of the vehicle.
The method comprises the following specific steps:
1. space-time probability distribution calculation of single-vehicle single-trip charging power
S11: generation of OD matrices
Dividing the road network nodes into at least N types according to functions, and forming at least N according to the combination of origin orgin and destination2And N is more than or equal to 3 in OD pairs. In this embodiment, different nodes in the road network are divided into 3 types of areas, i.e., a district citizen area (H), a work area (W), and other areas (O), according to functions. Different types of OD pairs (OD pairs for short) include 9 types (HH, HW, HO, WH, WW, WO, OH, OW, OO).
The OD matrix is used for carrying out statistical calculation through vehicle travel statistical data, wherein the statistical data are travel data of a certain number of vehicles in a day in an analyzed area (road network node area), and the travel data comprise the DO pair type of a departure place-destination of each travel of the vehicles in the day and the starting departure time.
Fitting is carried out through the counted travel data of the vehicles to obtain a joint probability distribution matrix P of different types of OD (origin-destination) to timeODTAnd counting the probability P of the first trip in a day and the probability P of the last trip in a day in any tripBR，PFR。
Distributing weight W to the nodes of the road network according to the population number and obtaining the node n in the T type areajProbability P ofn(T,nj)。
Figure BDA0002947828020000091
Binding of PODTGenerating an OD matrix, i.e. the probability P that OD occurs for i at time tOD(i,t)。
POD(i,ts)＝Pn(TOD,i,d,des(i))·PODT(TOD,i,ts)Pn(TOD,i,s,sta(i))
In the formula, TOD,iIs the type of OD to i; t isOD,i,s、TOD,i,dRespectively representing the node types of the starting point and the end point of the OD pair i; sta (-) and des (-) respectively calculate the numbers of the origin node and the focus node of OD pair i.
Due to different OD to probability POD(i, t) describes the journey starting at time t with OD pairs i starting and ending, since different OD pairs are not coincident in time of departure and in particular journey, and therefore cannot occur simultaneously in time and space, in planning the calculation NTOn the next trip, OD at time t is compared with the number N of vehicles in i according to law of large numbersOD(i, t) is such that obedience expectation value equals variance value and is NTPODNormal distribution of (i, t), i.e.
NOD(i,t)～N(NTPOD(i,t),NTPOD(i,t))。
S22: time consuming vehicle travel
Since the driving speed of the vehicle can change along with the number of road vehicles, a steady-state expected value matrix N of the number of road vehicles is obtained by calculationcr(wherein N iscrRepresenting the expected value of the number of vehicles on each road section in different time periods), and calculating the path consumption time of different OD pairs through the normal distribution characteristic of the values in the matrix.
Firstly, a vehicle speed and flow rate practical model (BPR model) is used for calculating the time consumed by OD to i in a journey at time t and an expected value matrix N of the distribution of a vehicle on a roadcr。
Due to the influence of the power term of the BPR model, the transit time of each road segment in the road network obeys the distribution of logarithm positive power. In the formula, tpFor road section transit time, fP(tp) Is tpProbability density function of tp,minIs tpThe physical meaning of the minimum value is the shortest passing time under the speed limit of the road section.
Figure BDA0002947828020000092
S33: path selection
Consider that the vehicle is in route selection, with PstThe vehicle of (1) selects the route trip in the shortest time, 1-PstThe probability of the distance is traveled by the shortest route, and the travel route is calculated by using a Dijkstra algorithm based on the adjacent matrixes of the travel time and the length of the road section. The calculation assumes that the energy consumption of the vehicle is linearly related to the form mileage.
S44: vehicle charging process
It is assumed here that the OD vs i is known for the SOC probability density function f at the end of vehicle tripODa,i(S), the detailed calculation process is explained in S24. The charging time period t after the vehicle arrives at the destinationcProbability density function fODa,ct,i(tc) Can be formed byODa,iLinear variation of(s) is obtained.
Figure BDA0002947828020000101
Considering that the vehicle being charged may also terminate charging and leave over time, it is assumed that the behavior of the vehicle leaving is mainly determined by the vehicle travel demand, and therefore it is assumed that the current state of charge is not considered when the vehicle leaves. Introducing a compensation coefficient c (n) for compensating the part of the vehicles leaving midwayjT), the physical meaning of which is node n at time tjThe remaining part of the charging power due to the vehicle leaving.
Figure BDA0002947828020000102
In the formula, NNN(nj,t)，NNL(nj,t)，NNA(njT) are respectively the nodes n at the time tjThe number of parked vehicles, the number of departing vehicles, and the number of arriving vehicles, the specific calculation method is given in S24.
The probability P of charging at time t in one trip of the vehicle can be obtainedC(njAnd t) is:
Figure BDA0002947828020000103
Figure BDA0002947828020000104
Figure BDA0002947828020000105
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000106
to represent
Figure BDA0002947828020000107
The probability density function of the travel time of the trip,
Figure BDA0002947828020000108
is a node njThe rated charging power of.
2. Calculation of integrated SOC
The meaning of the node integrated SOC distribution is the total SOC probability distribution of all vehicles of the same type parked under a certain node. The distribution mainly comprises 2 parts, one part is SOC distribution, and the other part is vehicle number distribution.
The node integrated SOC is affected by vehicle departure, vehicle arrival, vehicle charging 3.
Wherein the influence of vehicle charging is the translation process of the comprehensive SOC probability density and function of the vehicle at the charging part; and for the vehicles leaving, the vehicles leaving are considered to leave randomly, and the comprehensive SOC of the nodes is not influenced. The influence structure diagram of the integrated SOC is shown in FIG. 2.
In order to improve the reliability of the calculation result, the types of the vehicles under the nodes are further divided. The vehicle types under the node are divided into 3 types: a vehicle that has not traveled (First type), a vehicle that has traveled and will continue traveling (Travel type), and a vehicle that has ended all trips (Final type). Correspondingly, the node integrated SOC is also divided into 3 independent parts, namely an initial SOC distribution, a trip SOC distribution and an end SOC distribution. The split SOC distribution can avoid the influence of the SOC of the initial SOC of the subsequent trip vehicle on the SOC vehicles which are not trip, and the reliability of the calculation result is improved. Fig. 3 is a "flow" of SOC distributions between different nodes as the vehicle travels.
S21: calculating a distribution of a number of vehicles leaving a departure point
Vehicles departing from the departure point contain only First and Travel types, tsDeparture node n from timejNumber of vehicles NNL,first(i,t)、NNL,travel(i,t)
Figure BDA0002947828020000111
Figure BDA0002947828020000112
Figure BDA0002947828020000113
From the above linear operation relationship, N is knownNL,first(i,t)、NNL,travelBoth (i, t) follow a normal distribution.
S22: calculating a vehicle group SOC probability distribution for an off-node
Assuming that the starting SOC distributions of all vehicles in an OD pair at the same time are identical, OD vs. ODi,tHas an initial SOC probability density function of fODl,i,t(s)：
Figure BDA0002947828020000114
In the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000115
respectively, a node n at time tjMiddle start SOC and trip SOC.
S23: calculating a distribution of number of vehicles arriving at a destination
The vehicles arriving at the destination can be divided into 2 cases of a Travel type and a Final type in the destination, and arrive at the node njNumber of vehicles N of medium Travel and Final typeNA,travel(nj,t)、NNA,final(njAnd t) is calculated by the following formula.
Figure BDA0002947828020000121
Figure BDA0002947828020000122
Figure BDA0002947828020000123
Figure BDA0002947828020000124
Figure BDA0002947828020000125
S24: calculating an integrated SOC of a group of arriving destination vehicles
Since the same OD pair is assumed
Figure BDA0002947828020000126
The conditions of the vehicles are completely consistent, and the travel energy consumption is all
Figure BDA0002947828020000127
The vehicle SOC distribution probability density function of each OD pair arriving at the destination in the shortest time
Figure BDA0002947828020000128
Is the initial SOC probabilityLeft shift of density function to independent variable SOC
Figure BDA0002947828020000129
And the unit is calculated, and accumulation at the boundary of the SOC value range is ensured. SOC probability density function when arriving at destination in shortest path
Figure BDA00029478280200001210
The same can be obtained.
Weighting and summing the vehicle group comprehensive SOC probability density functions of 2 different path decisions, wherein the time consumption probability distribution of the vehicle in the driving process is considered, and then all OD arriving at the destination at the time t are compared with the traffic flow comprehensive SOC probability density function f of iODA,i,t(s) is:
Figure BDA0002947828020000131
Figure BDA0002947828020000132
Figure BDA0002947828020000133
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000134
are each tsAnd (4) the OD starting at the moment is used for traveling the decision travel time probability density function of the i in the shortest path and the shortest time.
To the destination node njThe SOC probability densities of the middle Travel and Final type vehicles are respectively
Figure BDA0002947828020000135
The calculation method is accumulation of OD pairs. Which is provided with
Figure BDA0002947828020000136
For example, the calculation formula is shown as the formula
Figure BDA0002947828020000137
Figure BDA0002947828020000138
S25: calculating the number of vehicles parked under the node
The parking numbers of the First type vehicle, the Travel type vehicle and the Final type 3 vehicle are respectively NNN,first(nj,t)、NNN,travel(nj,t)、NNN,final(njT), the calculation method is as follows:
Figure BDA0002947828020000139
Figure BDA00029478280200001310
Figure BDA00029478280200001311
in the formula, taHas a physical meaning of taThe moment arrives.
S26: calculating comprehensive SOC change conditions before and after charging
Of the three types of vehicles distinguished in the model, First, Travel and Final, only the latter two consider the charging process of the vehicle. Since there is no difference between the two in calculating the SOC distribution change, the following description does not distinguish between the Travel and Final types of vehicles in order to simplify the description process:
Figure BDA0002947828020000141
NNN(nit) denote nodes n, respectivelyjThe SOC probability density function and the number of vehicles; f. ofODA,i,t(s) denotes OD arriving at node n at moment iiOf a vehicleSOC probability density function.
Assuming that the charging power of the same type of vehicle is the same, and simplifying the assumption that the charging power remains the same during charging, the amount s by which the battery SOC increases per unit time during chargingrAnd the charging power satisfies:
Figure BDA0002947828020000142
in practice, the charging process corresponds to the SOC increasing process of the vehicle, and the integrated SOC corresponding to the node represents the rightward shift of the probability density curve of the SOC. As shown in fig. 4. The method is obtained by adopting a translation operator, and the calculation method of the translation operator comprises the following steps:
Figure BDA0002947828020000143
in the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000144
for translation operators, t is representedaThe process of the SOC probability density function of a newly arriving vehicle at time OD vs. i shifts to the right with charge during the first charging period.
Figure BDA0002947828020000145
In the formula (I), the compound is shown in the specification,
Figure BDA0002947828020000146
and synthesizing a translation operator of the SOC for the node.
S27: comprehensive change of comprehensive SOC probability density function of computing node
Node comprehensive SOC probability density function changes caused by vehicle arrival and vehicle departure mainly represent that the probability density function of the number of vehicles is weighted by the SOC distribution of the arrival and departure vehicle group; and the charging process is a superposition translation operator. In summary, the change of the node comprehensive SOC probability density function obtained after the simplified processing is calculated according to the following formula:
Figure BDA0002947828020000151
after the electric vehicle charging load space-time probability distribution model is obtained by the calculation method, verification is carried out by using the NHTS data of 2009 American national family travel survey.
Firstly, a time-probability joint probability distribution matrix of 9 OD types is obtained, and then an OD matrix for a road network is generated. A12-node topological graph of a medium-sized city backbone network is selected as a road network for analysis.
As shown in fig. 5. The key nodes of the traffic system are divided into residential areas, working areas and other areas according to function types, the traffic system totally comprises 12 nodes, and the nodes are connected with the nodes through bidirectional lanes to form 40 unidirectional lanes. The maximum diagonal distance is from node 2 to node 11, and the actual geographic straight-line distance is about 35 km.
In the embodiment, 5000 electric automobiles and 95000 traditional fuel automobiles are simulated to travel, the number of times of vehicle travel in one day is 31.516 ten thousand, the speed limit of each road section vehicle is 40km/h, the battery capacity of each road section vehicle is 40kWh, the maximum charging power of charging facilities of different nodes is 5kW, and the power consumption of the vehicles is 0.3 kWh/km. When the vehicle is fully charged, the SOC of the battery is 95%, and the shortest path and the shortest travel time of the vehicle respectively account for 50%.
Because the gradual soundness of the electric automobile charging facilities and the increase of the vehicle endurance mileage are combined with that the daily average driving mileage of the electric automobile does not exceed 30km, the charging facilities can be obtained according to the actual requirements of the automobile in an urban road network, and the electric quantity of the automobile can be ensured to meet the travel requirement before the automobile goes out due to the limited urban commuting distance.
FIG. 6 compares the road vehicle distribution of the model prior to iteration for steady state traffic flow. Because the road sections between the nodes 10 and 12 belong to the periphery of the road network, the travel demand of vehicles is limited, the traffic flow of the road sections is less, and the traffic jam of the vehicles cannot be caused. On the section from the node 12 to the node 11, since a large number of vehicles leave from the node 12 in the industrial area during the late peak period, the original traveling demand is high, and obvious drop-back can be seen after iteration. On 2 sections of the nodes 12 to 9 and 10, the obvious increase occurs when the peak of the night peak appears, and the sections of the nodes 12 to 11 are shunted. The road section between the nodes 9 and 12 is only 1.5km long, so the carrying capacity of vehicles is limited, and the traffic flow in the middle of the road section between the nodes 9 and 12 is obviously reduced, while the traffic flow of the nodes 12 to 9 is still high in the evening due to the diversion of the nodes 12 to 11. Fig. 6 shows that the model iterative process can reflect the change and adjustment of traffic-related traffic flow of traffic distribution, and can be used for selecting the self optimal path in the case that the road network information can be acquired by a vehicle due to the installation of navigation software and the like in practice.
Fig. 7 is a SOC value probability density distribution curve of a vehicle group arriving at the node 11 at different time intervals throughout the day. At an earlier time, since the trip start SOC probability density of the vehicle is a normal distribution, the SOC distribution of the arriving vehicle group is also close to the normal distribution. The SOC distribution at the position 1 has larger difference in different time, and the main reason is that the probability of vehicles going out in the morning is lower, and the distribution difference is larger because the distances of different OD pairs are different; there are some missing sections in the earlier part of the time, which corresponds to the moment when no vehicle reaches the node 11; the position 2 is the vehicle traveling early peak, which is the arrival destination of a large number of vehicles, and various different OD pairs exist, and the SOC distribution is smooth under the balance of the various OD pairs; the high SOC probability at position 3 drops because the OD pair starting at a starting point closer to node 11 consumes less energy on the journey, so that at position 3 the higher SOC probability increases, while the lower probability is concave, and the vehicles mainly come from the working area 12 and other areas 8.
Fig. 8 is an SOC time-probability density distribution curved surface of a Travel type vehicle in the residential block node 11. The SOC distribution of the vehicles at the starting moment in one day is normal distribution with the expected value of 85%, so the distribution of the Travel type after the vehicles arrive at the destination is also in normal distribution, a large number of newly arrived vehicles approach to be full along with the increase of time in the position 1, and the probability that the SOC reaches the maximum value of 95% gradually rises; the rapid increase in the maximum SOC value at location 2 over time corresponds to the process of filling up a large number of vehicles after reaching the destination in the early rush hour, corresponding to a time between 10 am and 3 pm; the probability of the lower value of SOC at position 3 corresponding to the gradual return to the starting point in the afternoon increases, and the probability value gradually tends to 0 as time increases
Fig. 9 is a SOC time-probability density distribution curve of the Final type vehicle in the residential area node 11. The physical meaning at location 1 is the change in SOC value caused by a vehicle arriving at a destination in the morning and no longer traveling. Wherein, at t-1, the SOC distribution of the arrival destination is close to the normal distribution with the expectation value of 76, which is caused by the first arrival of vehicles, and then the larger probability of the SOC value is also caused by the full charge of the part of vehicles; at position 2, the maximum SOC value is out of a valley, which is caused by the fact that when the vehicle travels in the early peak and the high peak, a large number of vehicles with low SOC values arrive and the probability of high SOC value is reduced; at times greater, t >500 minutes, there is substantial agreement with the trend in Travel, since the charge at the departure of the vehicle tends to be full, whether it is of Travel or Final type, with the same OD being similar to the charge at the destination.
Fig. 10 shows the probability density distribution of the charging power at the node 9 all day by taking the node 9 as an example in the form of a thermodynamic diagram.
Fig. 11 is a comparison between the monte carlo simulation solution result of the vehicle charging load calculation model of the present invention and the analysis calculation method result of the present invention, and a 95% confidence interval of the charging power probability distribution is selected as the comparison standard. It can be seen that under the two methods, the charging power trends are basically consistent, and the amplitudes are close. Wherein, the coincidence rate of 95% confidence intervals of the power time sequence curve is 88.95%, the relative error of the electric power expected value of the charging time sequence in the whole day is 6.32%, and the calculation results of the two are very close. It is shown that the assumptions and simplifications in calculating the power distribution in this document can be controlled within a reasonable error range.
FIG. 12 is a comparison between the conventional MAS (Multi-Agent System) model-based Monte Carlo simulation method and the calculation results of the method of the present invention. By comparison, the calculation result trend and amplitude of the calculation method are relatively close to those of the comparison method, wherein the contact ratio of the 95% confidence interval is 79.00%, and the relative error of the expected value is 17.56%. And the error of the expected value mainly comes from the part with lower power value, and the relative error of the expected values of the charging power of the part with the lower limit of the 95% confidence interval exceeding 100kW is 10.82%, and the relative error of the expected values of the charging power of the part with the higher limit of 200kW is only 4.83%. The calculation method results are closer to the vehicle travel simulation process which is completely significant in the traditional model, and the reliability is high.
Table 1 shows the comparison of the calculation characteristics and the calculation performance of the conventional method according to the present invention
Figure BDA0002947828020000171
As shown in table 1, comparing the monte carlo simulation calculation method of the comparison method, the monte carlo simulation calculation method of the model of the present invention, and the analytic calculation method of the present invention, 3 are comparison of the calculation characteristics and the calculation efficiency. The result of the method is different from that of a contrast method, so that the calculation efficiency can be greatly improved, and the complete probability distribution condition can be given.

Claims (9)

1. A calculation method of a space-time probability distribution model of an electric vehicle charging load is characterized by comprising the following steps of;
firstly, calculating single trip probability distribution of vehicles of the same type and space-time probability distribution of single trip charging power of the vehicles in a road network by using vehicle trip statistical data of an OD matrix and combining the population number of road network nodes, vehicle travel time consumption, path selection and vehicle charging process;
and calculating the space-time probability distribution of the charging load of the electric automobile in the road network by utilizing the node comprehensive SOC and the traffic flow group comprehensive SOC probability density function by combining the traffic distribution condition and the travel time consumption and energy consumption characteristics of the vehicle.
2. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric vehicle as claimed in claim 1, wherein the vehicle travel statistical data using the OD matrix comprises the following steps:
dividing the road network nodes into at least N types according to functions, and forming at least N according to the combination of origin orgin and destination2Each OD pair, N is more than or equal to 3;
counting travel data of a certain number of vehicles in a road network node area in one day, wherein the travel data comprise DO pair types of a departure place-destination of each travel of the vehicles in the day and an initial departure time;
fitting is carried out by counting travel data of vehicles to obtain a joint probability distribution matrix P of different types of OD (origin-destination) pairs and timeODT。
3. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric automobile according to claim 2, wherein the method is characterized by combining the population number of road network nodes and comprises the following steps:
distributing weight W to the nodes of the road network according to the population number and obtaining the node n in the T type areajProbability P ofn(T,nj)，
Figure FDA0002947828010000011
Binding of PODTGenerating an OD matrix, i.e. the probability P that OD occurs for i at time tOD(i,t)，
POD(i,ts)＝Pn(TOD,i,d,des(i))·PODT(TOD,i,ts)Pn(TOD,i,s,sta(i))
In the formula, TOD,iIs the type of OD to i; t isOD,i,s、TOD,i,dRespectively representing the node types of the starting point and the end point of the OD pair i;
sta (-) and des (-) respectively calculate the numbers of the origin node and the focus node of OD pair i.
4. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric automobile according to claim 3, wherein the vehicle travel time is the path consumption time of different OD pairs, and the calculation steps are as follows:
calculating the time consumed by the OD to i in the distance at the time t and an expected value matrix N of the vehicle on the road distribution by using a vehicle speed and flow practical model-BPR modelcr；
And calculating the path consumption time of different OD pairs according to the normal distribution characteristic of the median of the matrix.
5. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric automobile according to claim 4, wherein in the path selection of the vehicle, the vehicle has PstIs selected to travel in the shortest time path, 1-PstThe probability of the route is traveled by the shortest route, and the travel route is calculated by using a Dijkstra algorithm based on the adjacent matrixes of the travel time and the length of the road section; during the calculation process, the energy consumption of the default vehicle is in a linear relationship with the form mileage.
6. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric automobile as claimed in claim 5, wherein the SOC probability density function at the end of the vehicle trip of OD to i is f during the charging process of the vehicleODa,i(s)；
Charging time t after vehicle arrives at destinationcProbability density function fODa,ct,i(tc) From fODa,i(s) is obtained by linear variation of the formula
Figure FDA0002947828010000021
Introducing compensation coefficient c (n) at the same timejT) to compensate for vehicles leaving midway due to travel demand, the physical meaning of which is the node n at time tjThe remaining part of the charging power due to the departure of the vehicle
Figure FDA0002947828010000022
In the formula, NNN(nj,t)，NNL(nj,t)，NNA(njT) are respectively the nodes n at the time tjThe number of parked vehicles, the number of departing vehicles and the number of arriving vehicles;
the probability P of charging at time t in one trip of the vehicle can be obtainedC(njAnd t) is:
Figure FDA0002947828010000023
Figure FDA0002947828010000024
Figure FDA0002947828010000025
in the formula (I), the compound is shown in the specification,
Figure FDA0002947828010000026
to represent
Figure FDA0002947828010000027
The probability density function of the travel time of the trip,
Figure FDA0002947828010000028
is a node njThe rated charging power of.
7. The method for calculating the space-time probability distribution model of the charging load of the electric automobile as claimed in claim 6, wherein the node comprehensive SOC is the total SOC probability distribution of all vehicles of the same type parked under a certain node; the calculation process is as follows:
s21: calculating the number distribution of the vehicles leaving the departure point;
s22: calculating a vehicle group SOC probability distribution for an off-node
S23: calculating the number distribution of vehicles arriving at the destination;
s24: calculating the comprehensive SOC of the arriving destination vehicle group, and weighting the comprehensive SOC probability density functions of the two vehicle groups with different path decisions;
s25: calculating the number of vehicles parked under the nodes;
s26: comprehensive SOC change conditions before and after charging;
s27: and (4) node synthesis SOC probability density function synthesis change.
8. The electric vehicle charging load space-time probability distribution model calculation method according to claim 7, wherein the node comprehensive SOC is influenced by aspects of vehicle departure, vehicle arrival and vehicle charging 3;
before calculation, dividing the vehicle types under the nodes into 3 types, namely a vehicle-First type which is not traveled, a vehicle-Travel type which is traveled and is to be traveled continuously and a vehicle-Final type which is to finish all travels;
correspondingly, the node integrated SOC is also divided into 3 independent parts, namely an initial SOC distribution, a trip SOC distribution and an end SOC distribution.
9. The method for calculating the spatio-temporal probability distribution model of the charging load of the electric vehicle according to claim 8,
in the calculation process of step S21: vehicles departing from the departure point contain only First and Travel types, at tsDeparture node n from timejRespectively is NNL,first(i, t) and NNL,travel(i,t)；
Figure FDA0002947828010000031
Figure FDA0002947828010000032
Figure FDA0002947828010000033
NNL,first(i, t) and NNL,travel(i, t) are both normally distributed;
in the calculation process of step S22: in one OD pair at the same time, the distribution of the initial SOC of all vehicles is completely consistent, and the OD pair ODi,tHas an initial SOC probability density function of fODl,i,t(s)，
Figure FDA0002947828010000034
In the formula (I), the compound is shown in the specification,
Figure FDA0002947828010000035
respectively, a node n at time tjA probability density function of the mid-start SOC and the trip SOC;
in the calculation process of step S23: the vehicles arriving at the destination comprise a Travel type and a Final type in the destination;
arriving at node njNumber of vehicles N of medium Travel and Final typeNA,travel(nj,t)、NNA,final(njT) the calculation formula is as follows
Figure FDA0002947828010000041
Figure FDA0002947828010000042
Figure FDA0002947828010000043
Figure FDA0002947828010000044
Figure FDA0002947828010000045
In the calculation process of step S24: traffic flow comprehensive SOC probability density function f of all OD pairs i arriving at destination at time tODA,i,t(s)，
Figure FDA0002947828010000046
Figure FDA0002947828010000047
Figure FDA0002947828010000048
In the formula (I), the compound is shown in the specification,
Figure FDA0002947828010000049
are each tsThe OD starting at the moment is a travel time probability density function for the i trip decision by the shortest path and the shortest time;
to the destination node njThe SOC probability densities of the middle Travel and Final type vehicles are respectively
Figure FDA0002947828010000051
Figure FDA0002947828010000052
It is obtained by the accumulation calculation of OD pairs;
in the calculation process of step S25: first, Travel,The number of parked Final 3 type vehicles is NNN,first(nj,t)、NNN,travel(nj,t)、NNN,final(njT) are respectively
Figure FDA0002947828010000053
Figure FDA0002947828010000054
Figure FDA0002947828010000055
In the formula, taHas a physical meaning of taThe moment arrives;
in the calculation process of step S26: the comprehensive SOC change conditions before and after charging are reflected through the superposition translation operator;
the calculation method of the translation operator is as follows:
Figure FDA0002947828010000056
in the formula (I), the compound is shown in the specification,
Figure FDA0002947828010000057
for translation operators, t is representedaThe process that the SOC probability density function of the newly arrived vehicle at the time OD to i translates to the right along with the charging in the first charging period;
Figure FDA0002947828010000058
in the formula (I), the compound is shown in the specification,
Figure FDA0002947828010000059
synthesizing a translation operator of the SOC for the node;
in the calculation process of step S27: node comprehensive SOC probability density function changes caused by vehicle arrival and vehicle departure are obtained by weighting probability density functions of the number of vehicles according to the SOC distribution of the arrival and departure vehicle group; superposing translation operators in the charging process; the change formula of the node comprehensive SOC probability density function obtained after simplifying the calculation result is
Figure FDA0002947828010000061
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