CN107392400A - Meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodology - Google Patents
Meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodology Download PDFInfo
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Abstract
The present invention relates to a kind of meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, belong to intelligent grid field.The method comprising the steps of：S1：First all kinds of electric automobiles are classified, analyze its space transfer and charge characteristic；S2：Make abstract simulation to urban traffic network with Graph-theoretical Approach using Trip chain is theoretical；S3：The Time-spatial diversion model of all kinds of electric automobiles is set up using Monte Carlo simulation and the random shortest path method based on Markovian decision；S4：Temperature and traffic energy consumption model are established according to electric automobile actual test data.The present invention has versatility, analyzed suitable for regional distribution, spatial and temporal distributions situation in urban area can conveniently and effectively be calculated, and the change for the load that charged when traffic changes with temperature regime can be predicted, its prediction result provides foundation for researchs such as charging station planning, load schedulings.
Description
Technical field
The invention belongs to intelligent grid field, is related to meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions are predicted
Method.
Background technology
As world energy sources structural adjustment and auto industry technology develop, electric automobile (EV) is the main of new-energy automobile
Developing direction.Extensive electric automobile access, very important influence, including load will be brought to Power System Planning and operation
Growth, operation of power networks optimal control difficulty increase, influence the quality of power supply, new requirement etc. proposed to distribution network planning.Solution
Certainly the basis of problem above is that charging load spatial and temporal distributions are effectively predicted, therefore accurately and effectively load is established in active demand
Forecast model.
The load prediction of foreign scholar's early start charging electric vehicle is studied.Early stage research is in charging set in advance
Between with charging place under, directly using trip survey data complete electric automobile load modeling.The load that existing literature is derived
Demand shows that electric automobile load can increase the peak-valley difference of power network.Because trip survey data are based on conventional truck, mostly
Assume that charging interval and place are fixed value in number existing literature, be generally set to fill in house after last time trip terminates
Electricity.Some researchs afterwards consider the randomness of charging time started, but still set duration of charge as fixed value.It is existing
Region division is industrial area, shopping centre residential quarter etc. by document, and charging load is calculated according to the different charge characteristic of regional
Spatial and temporal distributions, but the space distribution of actually electric automobile is predetermined, does not simulate electric automobile because of random shifting
Dynamic and caused load spatial and temporal distributions.There is scholar to be combined power network with the network of communication lines in recent years, consider the randomness of electric automobile,
A kind of space-time model of mobile electric car load is established by random Trip chain and Markovian decision process (MDP).Should
Method relatively accurately realizes the prediction of spatial and temporal distributions, but mainly for private car, does not consider traffic, temperature etc. to electronic vapour
The environmental factor that car energy consumption has a significant impact.
It is domestic that the random device using probability statistics is concentrated mainly on to load early stage to the research of charging electric vehicle load
Annual distribution is modeled, and existing literature analyzes the charging characteristics of taxi, bus, private car etc., using Monte Carlo
Method predicts the charging total load of China's future electric automobile.Existing literature considers user's daily travel, charging starting
The stochastic variables such as time, establish the probabilistic model of charge requirement.Research afterwards starts to consider load asking in spatial distribution
Topic, existing literature study different zones charging electric vehicle characteristic by way of region division, form spatial distribution.It is existing
Document construction trip chain model, Temporal And Spatial Distribution Model is established to five big trip regions.But the studies above divides more bases to space
In function plot, it is impossible to reach required precision of the analysis space distribution to Electric Power Network Planning operation.Existing literature proposes one kind
Charging load spatial and temporal distributions Forecasting Methodology based on traffic trip matrix and cloud model, this method embody the random of user's decision-making
Property and ambiguity, preferably realize the prediction of spatial and temporal distributions, but be not involved with the different trip of all kinds of electric automobiles with filling
Electrically, it also have ignored influence of the environmental factor to load prediction.
The content of the invention
In view of this, it is an object of the invention to provide space division when a kind of meter and real-time traffic and the EV of temperature charging loads
Cloth Forecasting Methodology.All kinds of electric automobiles are classified first, analyze its space transfer and charge characteristic, it is theoretical using Trip chain
Make abstract simulation to urban traffic network with Graph-theoretical Approach.Secondly, using Monte Carlo simulation and based on Markovian decision
Random shortest path method sets up the Time-spatial diversion model of all kinds of electric automobiles, and is established according to electric automobile actual test data
Temperature and traffic energy consumption model.Finally, by taking Jincheng City as an example, the validity of this method is demonstrated.
To reach above-mentioned purpose, the present invention provides following technical scheme：
Meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodology, comprise the following steps：
S1：First all kinds of electric automobiles are classified, analyze its space transfer and charge characteristic；
S2：Make abstract simulation to urban traffic network with Graph-theoretical Approach using Trip chain is theoretical；
S3：All kinds of electronic vapour are set up using Monte Carlo simulation and the random shortest path method based on Markovian decision
The Time-spatial diversion model of car；
S4：Traffic and temperature energy consumption model are established according to electric automobile actual test data.
Further, the step S1 is specially：
Charging modes are fixed parking stall trickle charge, and trip mode is come out early and return late for working day, stopped over, and day off goes out
The EV types of amusement are private car A classes；
Charging modes stop over, day off steps out to be come out early and return late by way of ground fast charge, trip mode for working day
EV types be private car B classes；
Charging modes are fixed parking stall trickle charge, and trip mode is that working day traffic path is fixed, what day off did not went on a journey
EV types are officer's car A classes；
Charging modes are by way of ground fast charge, and trip mode is that working day traffic path is fixed, the EV classes that day off does not go on a journey
Type is officer's car B classes；
Charging modes are by way of bus stop quick charge, and trip mode is that traffic path is fixed with the travel time, annual
The EV types of operation are bus；
Charging modes are to be fixed by way of ground fast charge station quick charge, trip mode to set out with the off-running time, and route is random,
The whole year EV types of operation are taxi.
Further, the step S2 is specially：
Trip chain is divided into simple chain and compound catenary；Simple chain only has one for the trip purpose of every Trip chain；It is complicated
Chain is that Trip chain contains multiple trip purposes；It is divided into residence H, place of working W, purchase by resident trip survey data to trip purpose
Thing dining SE, social entertainment SR, other O, while be also charging electric vehicle place；
Wherein, the electrically-charging equipment that residence H is set is fast charge+trickle charge；
The electrically-charging equipment that place of working W is set is fast charge+trickle charge；
The electrically-charging equipment that shopping dining SE is set is fast charge；
The electrically-charging equipment that social entertainment SR is set is fast charge；
The electrically-charging equipment that other O are set is fast charge；
Urban traffic network is abstracted as by digraph using the method for graph theory, each highway in city is arranged to side,
Intersection is arranged to node, road length is arranged to the weight on side, and transportation network is abstracted as to the set of side and node.
Further, the step S3 is specially：
For each EV in region, it is known that after its starting point, using the markov with random chance factor gamma
Decision process obtains the optimal path with randomness in its space；
Wherein, the Markovian decision process is the state observed according to each moment, from available action collection
Middle to be made decisions from an action, the state of system next step is random, and its state transition probability has Ma Er can
Husband's property；Markovian decision process is：
M=(T, S, A (i), p, R)
Wherein, T is decision-making moment collection, represents to choose the time point set of action；S is state set, is represented in each decision-making
Carve, system state space collection；A (i) collects for action, represents, at any one decision-making moment, the state that policymaker observes, that is, to exist
The available action collection that state i chooses；P (j | i, it is a) state transition probability, represents under state i, a that takes action is transferred to shape
State j probability；R (i, it is a) Reward Program, represents under state i, after a that takes action, the return of policymaker's acquisition；
That is Markovian decision dynamic process is as follows：Policymaker state S (0) where T (0) moment, is selected from A (0)
One action a (0) performs, and has arrived next S (1) state by P (S (1) | S (0), a (0)) probability random transferring after execution, then
An action a (1) is performed again, and successively backward, to the last moment T (N), reaches end-state S (N)；
It is the rule that a decision system selects action on each different conditions to define decision rule, is designated as f (S (0)),
f(S(1)),f(S(2))…f(N)；Definition strategy is the sequence of decision rule, is designated as π=(f (S (0)), f (S (1)), f (S
(2)) ... f ((S (N))), the collection of All Policies is collectively referred to as policy class, is designated as Π；From original state S, (0) simultaneously used tactful π
∈ Π so thatMaximum or minimum value is obtained, then tactful π is optimal policy；Define value function VπFor using tactful π when
State S (i) expected returns：
Wherein E is it is expected, StFor t status, t=0 is corresponded to original state S (0), is expressed as with recursive form：
To each tactful π, its corresponding value function VπIt is a series of unique public solution of linear equations, optimal policy passes through
Recursive method obtains expected return value based on Dynamic Programming backward；
All node sets in region are considered as S, each node time instance passed through from origin-to-destination traveling is considered as decision-making
Moment t；Determine that next paths of traveling are considered as action a in each node, the path length that will be travelled is considered as return R；
If EV under steam with part probability transfer mode travel, introduce value be 0~0.5 obedience it is equally distributed with
Machine probability factor γ, represent, when policymaker is often performed with this action, there is probability γ to be transferred to another paths；By Path selection
In shortest path be combined with randomness, do not influenceed by the original state and initial action set out, for next decision-making
Remaining decision rule composition strategy be optimal policy；
(1) for private car：
Working day, private car active strokes share 3 kinds：Active strokes 1：H-W；Active strokes 2：H-W-SR/SE/O；Activity
Stroke 3：H-W+H-SR/SE/O；It is each to obey certain proportion；Working day each car Trip chain C obeys probability distribution：P (C)=pi(i
=1,2,3)
X obeys Normal probability distribution between at the beginning of every section of stroke, and its probability density function is：
Wherein σeFor Variance of Normal Distribution, μeFor normal distribution average；
All electronic private cars are considered as totality, each electric automobile is considered as a two-dimensional random variable, and it, which is combined, divides
Cloth function is：P (C ∩ x)=P (C) P (C | x), when extracting Trip chain type by the method for Monte Carlo simulation and set out
Between；
The random most short used time path computing of meter and real-time traffic：If each link length is Li, the traveling of each road of t
Speed is Vi,t, each side right value is set as by T the time required to each sidei, then havePass through band random chance factor M DP
Algorithm obtains the relevant parameter such as random most short used time path, travel speed of corresponding automobile different sections of highway, obtain private car when
Space division cloth；
(2) for officer's car
Probability distribution between at the beginning of every section of stroke is identical, equal Normal Distribution, using the illiteracy similar with private car
Special Carlow analogy method obtains the spatial and temporal distributions of officer's car；
(3) for taxi
Each taxi is 0:When 00 from company location, destination is randomly selected, using meter and real-time traffic
Obtain most short used time path in most short used time road；It is random between at work to run time return taxi company of being handed over to the next shift until two,
It is corresponding with electric taxi travel speed, obtain the spatial and temporal distributions of each electric taxi；
(4) for bus
Time of departure obedience is uniformly distributed, and for each bus, hair is extracted by the way of Monte Carlo simulation
The car time makes the time, corresponding different sections of highway travel speed, to obtain the spatial and temporal distributions of bus with the parking of each website.
Further, the step S4 is specially：
S401：Traffic energy consumption model is established according to electric automobile actual test data：
The measured data modeling of the energy consumed based on the identical mileage of different category of roads downward drivings, obtains corresponding to often
Its different periods and the specific energy consumption under the real-time jam situation of different road conditions, through street unit mileage power consumption are Eksl=0.247
+1.520/V-0.004*V+2.992*10(-5)*V；
Trunk roads unit mileage power consumption is Ezgl=-0.179+0.004*V+5.492/V；
Secondary distributor road unit mileage power consumption is Ecgl=0.21-0.001*V+1.531/V；
Branch road unit mileage power consumption is Ezl=0.208-0.002*V+1.553/V；
Wherein Eksl、Ezgl、Ecgl、EzlThrough street, trunk roads, secondary distributor road, the electric automobile unit mileage of branch road are represented respectively
Power consumption, V represent electric automobile road-section average travel speed；
For each electric automobile in region, certain position is obtained according to its Temporal And Spatial Distribution Model at moment, it is corresponding real
When traffic congestion after obtain moment EV units mileage power consumption；
S402：Temperature energy consumption model is established according to electric automobile actual test data：
The calculation formula of least square air-conditioning unlatching rate is：
Kpect=7 × 10-5·TEMP4-0.0887·TEMP3+3.673·TEMP2-56.302·TEMP+310.7681
Wherein TEMP represents temperature, KpectAir-conditioning unlatching rate is represented, draws out its functional image；
Temperature energy consumption proportionality coefficient is defined, i.e., unit distance power consumption corresponding to different temperatures consumes with benchmark after air-conditioning is opened
The ratio between electricity, the fitting formula of least square temperature proportional coefficient are：
Ktemp=10-3·(TEMP-10)2+1.2
Wherein KtempFor temperature proportional coefficient, TEMP is temperature, draws out its functional image；
According to temperatures above energy consumption model, the real time temperature at corresponding estimation range moment, electric automobile in region is obtained
To the influence situation of electric automobile unit mileage power consumption after the ratio that air-conditioning is opened, and air-conditioning unlatching.
Further, the charging Load Calculation Method of all kinds of electric automobiles：
The charging matrix of loadings PE of m rows n row is defined, each time interval is represented per a line, each row represent each node,
Matrix element represents the charging load at certain place corresponding moment, i.e. PEi,jRepresent the charge power at i-node j moment；Time interval
Quantity m, i.e. row in matrix quantity, depending on temporal resolution and calculate requirement；
(1) A classes private car and officer's car：According to the traveling spatial and temporal distributions of each electric automobile, charging modes, obtain its
The charging load of place parking site；For A classes private car, officer's car, because its place of charging is fixed, each electronic vapour
Charging load corresponds to a certain row of PE matrixes caused by car；
(2) B classes private car, officer's car：Consider that environment temperature calculates unit mileage power consumption with traffic, correspond to this
Section path length obtains this section of distance power consumption ER1；If dump energy EC is not enough to be unsatisfactory for reaching next destination, i.e., not
Meet EC>ER1, then electric automobile current location carry out a quick charge, charging duration tcr taken out using Monte Carlo mode
Take, and be limited within 30min；If the charging time started is TS, the charging end time is TE, then has：TE=TS+tcr；
It is by needing to judge that can dump energy expire during next place with A class private car calculation process difference
Sufficient distance needs；For B classes private car, officer's car, corresponded to and PE matrixes by way of the load that charged caused by charging place at it
Each row；
(3) taxi：Taxi is A class private cars for a long time, without fixed destination traveling, and taxi is by way of charging
The corresponding each row with PE matrixes of charging load caused by place；
(4) bus：Bus is the A class private cars that route is fixed, battery capacity and unit mileage power consumption are bigger, public
Car is handed in the corresponding each row with PE matrixes of load that charged caused by each website.
The beneficial effects of the present invention are：The present invention has versatility, is analyzed suitable for regional distribution, can facilitate, have
Spatial and temporal distributions situation in urban area is calculated to effect, and the load that charged when traffic changes with temperature regime can be predicted
Change, its prediction result provides foundation for researchs such as charging station planning, load schedulings.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carried out
Explanation：
Fig. 1 is Trip chain type；
Fig. 2 is Markovian decision schematic diagram；
Fig. 3 is electric automobile Time-spatial diversion schematic diagram；
Fig. 4 is taxi space transfer figure；
Fig. 5 is air-conditioning unlatching rate curve；
Fig. 6 is temperature proportional coefficient；
Fig. 7 is charging matrix of loadings；
Fig. 8 is simulation contact surface；
Fig. 9 is Jincheng City transportation network figure；
Figure 10 is each node charging load of typical case's week lower Jincheng City A, B class car；
Figure 11 is each node charging load of typical case's week lower Jincheng City bus；
Figure 12 is each node charging load of typical case's week lower Jincheng City taxi；
Figure 13 is typical case's week lower Jincheng City charging electric vehicle total load；
Figure 14 is the typical case's week of node 10, high temperature week charging load curve；
Figure 15 is the typical case's week of node 15, congestion week charging load curve；
Figure 16 is two methods load prediction comparison curves.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
1. electric automobile is classified and traveling network
1.1 electric automobile types
Electric automobile type is more in city, and the charging modes that same type electric automobile uses also are not quite similar,
For the spatial and temporal distributions of analysis charging load, the present invention from automobile charging modes and trip feature, by electric automobile be divided into
Lower several types, as shown in table 1：
The electric automobile type of table 1
1.2 Trip chain
Trip chain can be divided into simple chain and compound catenary.Simple chain is that the trip purpose of every Trip chain only has one, more
It is fixed；Compound catenary then refers to Trip chain and contains multiple trip purposes, and flexibility is larger with randomness.It can go out to trip purpose by resident
Row survey data is divided into the H that goes home (Home), work W (Work), shopping dining SE (Shopping and Eating), social joy
Five classes of happy SR (Social and Recreational) and other affairs O (Other family/personal errands)
Type, it is charging electric vehicle place to be also simultaneously these trip purposes.The common Trip chain such as Fig. 1 institutes being related in the present invention
Show, wherein (a) (b) (c) (d) is simple chain, (e) is compound catenary.
With charging modes it is trip purpose to be mutually related, is printed and distributed according to National Development and Reform Committee《Charging electric vehicle base
Infrastructure development guide (2015-2020)》, to trip purpose charging equipment do following setting：
The electrically-charging equipment of table 2 is set
The 1.3 random walk simulations based on Markov decision theory
Urban traffic network is abstracted as by digraph using the method for graph theory.Each highway in city is arranged to side,
Intersection is arranged to node, road length is arranged to the weight on side, and with this, transportation network can be abstracted as side and node
Set.
Markovian decision process refers to that policymaker periodically or continuously observes the stochastic and dynamic with Markov property
System, sequentially make decisions.The state observed according to each moment, from an action from available action collection
Make decisions, the state of system next step is random, and its state transition probability has Markov property.One Ma Er can
Husband's decision process is made up of a five-tuple
M=(T, S, A (i), p, R)：
T：Decision-making moment collection, represent to choose the time point set of action.
S：State set, represent in each decision-making moment, system state space collection.
A(i)：Action collection, represents at any one decision-making moment, the state that policymaker observes, can be chosen in state i
Available action collection.
p(j|i,a)：State transition probability.Represent under state i, a that takes action is transferred to state j probability.
R(i,a)：Reward Program.Under expression state i, after a that takes action, the return of policymaker's acquisition.
Markovian decision dynamic process is as follows：Policymaker state S (0) where T (0) moment, the selection one from A (0)
Individual action a (0) is performed, and next S (0) state has been arrived by P (S (1) | S (0), a (0)) probability random transferring after execution.Then again
An action a (1) is performed, successively backward, to the last moment T (N), reaches end-state S (N), as shown in Figure 2.
It is the rule that a decision system selects action on each different conditions to define decision rule, is designated as f (S (0)),
f(S(1)),f(S(2))…f(N).Definition strategy is the sequence of decision rule, is designated as π=(f (S (0)), f (S (1)), f (S
(2)) ... f ((S (N))), the collection of All Policies is collectively referred to as policy class, is designated as Π.If from original state S, (0) simultaneously used
Tactful π ∈ Π so thatMaximum or minimum value is obtained, then tactful π is referred to as optimal policy.Define value function VπFor using strategy
In state S (i) expected returns during π：
Wherein St is t status, and t=0 is corresponded to original state S (0), is expressed as with recursive form：
To each tactful π, its corresponding value function VπIt is a series of unique public solution of linear equations.Above formula is referred to as
Bellman formula, optimal policy can recursive method obtains backward by the expected return value based on Dynamic Programming.
All node sets in region are considered as S, each node time instance passed through from origin-to-destination traveling is considered as decision-making
Moment t；Determine that next paths of traveling are considered as action a in each node, the path length that will be travelled is considered as return R.
By taking 4 node regions as an example：State set S={ 1,2,3,4 }, action collection A={ a, b, c, d }；R (a)=65m, R (b)
=95m, R (c)=90m, R (d)=97m, π 1={ a, b }, π 2={ c, d }, if 1 is starting point, 2 be terminal, then：
R(π1)=r (a)+r (b)=160m
R(π2)=r (c)+r (d)=187m (3)
Definition action state-transition matrix P, represent to take certain take action after the probability that is shifted between each state of policymaker.If
Take action a, b, c, and d is full probability transfer, i.e., is transferred to NextState with probability 1, by taking a that takes action as an example, then P matrixes are expressed as：
When policymaker is located at node 1, execution action a will be transferred to node 2 with probability 1, when being in other nodes then with
The holding position of probability 1 is constant.Now optimal policy is that shortest path path is π 1={ a, b }.
Due to the actual generally non-shortest path of electric automobile driver trip route, it is to the current region residing for oneself
State can not possibly have a complete understanding.Therefore, the method for probability factor is introduced to handle the uncertainty of this category information.
Assuming that EV is travelled with part probability transfer mode under steam, obeyed between introducing 0~0.5 equally distributed random
Probability factor γ, when policymaker is often performed with this action, there is probability γ to be transferred to another paths.To action a in upper example
Speech, its probability transfer matrix of taking action are：
There is γ probability to be transferred to node 3 after performing action a.It is corresponding random in the case of transferable path is more than 2
Probability factor γ1~γk, and meetRandom chance factor gamma be introduced into by the shortest path in Path selection with
Randomness is combined, because the recursive algorithm backward of MDP optimizing decision processes obeys principle of optimality, i.e., no matter at the beginning of which
Which initial action is beginning state set out and employ, and the strategy of remaining decision rule composition is optimal for next decision-making
Strategy.Therefore for each EV in region, it is known that optimal using the MDP with random chance factor gamma after its starting point
Process approach can obtain the optimal path with randomness in its space.
2. electric automobile Time-spatial diversion model
2.1 private car
2.1.1 Trip chain and departure time
On weekdays, the drive active strokes of working clan share 3 kinds：Active strokes 1 are to be gone from before house on job site
Class, After Hours goes home, does not go to other places, represent to be H-W with Trip chain；Active strokes 2 are gone to work from before house, are come off duty
SR/SE/O places are gone to before in way home once, then are gone home, Trip chain is expressed as H-W-SR/SE/O；Active strokes 3 are in oneself
Before go to work, After Hours go home, then go to SR/SE/O places one to be gone home after plowing from family again, this active strokes contain two
Individual Trip chain, i.e. H-W and H-SR/SE/O.Three kinds of active strokes proportions of working day and as shown in table 3.
The Trip chain ratio of table 3
That is working day each car Trip chain C obeys following probability distribution：
P (C)=pi(i=1,2,3) (6)
X obeys Normal probability distribution between at the beginning of every section of stroke, and its probability density function is:
All electronic private cars are considered as totality, each electric automobile is considered as a two-dimensional random variable, and it, which is combined, divides
Cloth function is：
P (C ∩ x)=P (C) P (C | x) (8)
Trip chain type and departure time are extracted by the method for Monte Carlo simulation.Nonworkdays uses identical calculations side
Formula.
2.1.2 the random most short used time path computing of meter and real-time traffic
If each link length is Li, the travel speed of each road of t is Vi,t, each side right value is set as by each side
Required time Ti, then have：
Random most short used time path, the traveling of corresponding automobile different sections of highway are obtained by band random chance factor M DP algorithm
The relevant parameters such as speed, the spatial and temporal distributions of private car can be obtained.As shown in figure 3, grey circuit represents driving path, it is right respectively
Should be in each period.
2.2 officer's car
The trip rule of officer's car is relatively simple, general only to be moved between institutional settings or other places.Officer's car
Trip include two kinds of active strokes, i.e. W-W and W-SR/SE/O, at the beginning of every section of stroke between probability distribution it is identical, take
From normal distribution, the spatial and temporal distributions of officer's car are can obtain using the Monte-carlo Simulation Method similar with private car.
2.3 taxi
Daily 0:When 00, taxi starts to accept an order, driven to where client from affiliated taxi company
Ground, then the destination for going to client to go on a journey, receive new order, so circulation, as shown in Figure 4 again after arriving at.Daily 23：
Stop accepting an order after 00, return to affiliated company location and hand over to the next shift.
Each electric taxi is 0:When 00 from company location, destination is randomly selected, with private car calculating side
Method is similar, and most short used time path is obtained using the most short used time road of meter and real-time traffic.Runed at random until two between at work
The individual time of handing over to the next shift returns to taxi company.It is corresponding with electric taxi travel speed, each electric taxi can be obtained
Spatial and temporal distributions.
2.4 bus
Bus route is fixed, and time of departure obedience is uniformly distributed.For each bus, using Monte Carlo mould
The mode of plan extracts the time of departure and the parking of each website makes that the time, corresponding different sections of highway travel speed, bus can be obtained
Spatial and temporal distributions.
3 real-time traffics and temperature energy consumption model
Electric automobile energy consumption factor has a lot, and wherein temperature influences most with traffic on its unit mileage power consumption
Greatly.It is too high or too low for temperature that driver can be caused to open air-conditioning, increase electric automobile energy consumption.Automobile-shaped under different traffics
Formula speed is different, and automobile energy consumption is also different.
3.1 traffic energy consumption models
Speed, acceleration are had nothing in common with each other under different categories of roads and jam situation when vehicle travels, single caused by correspondence
Potential energy consumption is also different.Document《EV Energy Consumption modeling and continual mileage estimation research based on driving cycle》Using electronic
Automobile stand method energy consumption experimental data, has obtained the electric automobile energy consumption model of different categories of roads.
According to《Urban road engineering design specification》(CJJ37-2012), urban road should be according to road in road network
Status, communication function and to service function along the line etc., are divided into four through street, trunk roads, secondary distributor road and branch road grades, root
The data provided according to traffic department, are divided into unimpeded, substantially unimpeded, slight congestion, moderate congestion by congestion in road and seriously gather around
Stifled six kinds, the road of every kind of grade has different travel speeds under different jam situations, as shown in table 4：
Different congestion degree road speeds under 4 each category of roads of table
The measured data modeling of the energy consumed based on the identical mileage of different category of roads downward drivings, obtains corresponding to often
Its different periods and the specific energy consumption under the real-time jam situation of different road conditions, as shown in table 5, wherein Eksl、Ezgl、Ecgl、EzlRespectively
Represent through street, trunk roads, secondary distributor road, the electric automobile unit mileage power consumption (unit of branch road：KWh/km), V represents electronic
Automobile section average overall travel speed (unit：km/h).
Unit mileage power consumption under 5 each category of roads of table
For each electric automobile in region, certain position is obtained according to its Temporal And Spatial Distribution Model at moment, it is corresponding real
When traffic congestion after can obtain moment EV units mileage power consumption.
3.2 temperature energy consumption models
When outdoor temperature it is too high or too low so as to beyond the comfort zone of human body when, or environmental wet is spent under low temperature
When height need to open air-conditioning progress defrosting-defogging, electric automobile driver can typically select air-conditioning in opening vehicle.Document《Air conditioning for automobiles
Life cycle climate performance assessment models》It has studied air-conditioning unlatching rate of the domestic several big cities under different temperatures section, logarithm
According to the calculation formula that least square air-conditioning unlatching rate is fitted after processing：
Kpect=7 × 10-5·TEMP4-0.0887·TEMP3+3.673·TEMP2-56.302·TEMP+310.7681 (10)
Wherein TEMP represents temperature, KpectAir-conditioning unlatching rate is represented, draws out its functional image, as shown in Figure 5.
Document《Pure electric automobile performance estimating method and popularization feasibility study based on real example》To electric automobile air conditioner
Open the relation between unit mileage power consumption to be studied, define temperature energy consumption proportionality coefficient, be i.e. air-conditioning is different after opening
The ratio between unit distance power consumption corresponding to temperature and benchmark power consumption.Data measured by experiment in text are handled, obtained most
A young waiter in a wineshop or an inn multiplies fitting formula：
Ktemp=10-3·(TEMP-10)2+1.2 (11)
Wherein KtempFor temperature proportional coefficient, TEMP is temperature, draws out its functional image, as shown in Figure 6.
According to temperatures above energy consumption model, the real time temperature at corresponding estimation range moment, can obtain electronic in region
To the influence situation of electric automobile unit mileage power consumption after the ratio that air conditioning for automobiles is opened, and air-conditioning unlatching.
4 charging electric vehicle carry calculations
4.1 matrixes represent
Multiple nodes and moment for being related in one day is represented with the form of matrix, spatial and temporal distributions are unified, effectively receive
Collection information and it is easy to calculate and assesses, and matrix is than loop control statement more efficiently in mathematical software MatLab.Define m rows
The charging matrix of loadings PE of n row, each time interval is represented per a line, each row represent each node, and matrix element represents certain
The charging load at place corresponding moment, i.e. PEi,jRepresent the charge power at i-node j moment.The quantity m of time interval, i.e. matrix
In row quantity, depending on temporal resolution and calculate requirement.As shown in Figure 7.
4.2 all kinds of car charging Load Calculation Methods
(1) A classes private car and officer's car., can because A classes private car and A class officer's cars are fixed location trickle charge pattern
Charging carry calculation is carried out using identical method, is introduced by taking A class private cars as an example.A classes private car has the slow of fixation
Fast charging station, for its main charging modes to go home to carry out trickle charge daily, charging start time is to arrive at the house moment.Returned from car owner
Family's whole period of being set out by second day is charging interval section, until full of.Each electronic vapour can be obtained based on method above
Traveling spatial and temporal distributions, the power consumption situation of car, and then its charging load in place parking site can be obtained.To A class private savings
For car, officer's car, because its place of charging is fixed, charging load corresponds to certain of PE matrixes caused by each electric automobile
One row.
(2) B classes private car, officer's car.Because B classes private car and B class officer's cars are by way of ground fast charge pattern, can use
Identical method carries out charging carry calculation, is introduced by taking B class private cars as an example.It is gone on a journey since the departure place in path
Before, first judge whether battery dump energy EC meets the needs of this section of distance.Consider that environment temperature calculates list with traffic
Position mileage power consumption, corresponds to this section of path length and obtains this section of distance power consumption ER1.If dump energy is not enough to be unsatisfactory for reaching
Next destination, that is, be unsatisfactory for：
EC>ER1 (12)
Then electric automobile carries out a quick charge in current location, and charging duration tcr is extracted using Monte Carlo mode,
And it is limited within 60min.If the charging time started is TS, the charging end time is TE, then has：
TE=TS+tcr (13)
It is corresponding each with PE matrixes by way of the load that charged caused by charging place at it for B classes private car, officer's car
Row.
(3) taxi.Taxi is considered as A class private cars for a long time, without fixed destination traveling, and taxi is on way
The corresponding each row with PE matrixes of charging load caused by charged place.
(4) bus.The species of electric bus is many at present, and the charging modes of use also differ.It is electronic to embody
The space-time randomness of automobile distribution, it is assumed that all electric bus are the super capacity public transport by way of bus station quick charge
Car.Therefore bus is considered as the A class private cars that route is fixed, battery capacity and unit mileage power consumption are bigger, bus
In the corresponding each row with PE matrixes of load that charged caused by each website.
4.3 simulation flows are as shown in Figure 8.
5 sample calculation analysis
5.1 transportation network
As shown in figure 9, by taking Jincheng City, Shanxi Province as an example, by the abstract traffic containing 16 nodes, 20 sides of its transportation network
Network, wherein each side institute band color represents its category of roads, node 1-6 is residential block (H), and node 11-15 is trading estate
(W), node 7,8,9,10,16 is that amusement is gone shopping and rested area (SR/SE/O).According to the above-mentioned setting to city electrically-charging equipment, 6
H places and 5 W places have fast charge stake and trickle charge stake simultaneously, that is, are provided simultaneously with fast charge function and trickle charge function, 5 SR/SE/O
Place sets fast charge stake.
5.2 recoverable amounts are predicted
Domestic the year two thousand thirty car ownership prediction result is as shown in table 6.
The domestic year electric automobile recoverable amount of table 6 2030
With Jincheng City GDP compared with domestic GDP total amounts, to obtain the electric automobile recoverable amount such as institute of table 7 of the year two thousand thirty Jincheng City
Show：
7 2030 Jincheng City year of table car ownership
5.3 simulation analysis
5.3.1 parameter setting
It is 100% to set electric automobile permeability, is networked with simulating extensive electric automobile.Private car, taxi and public affairs
Business car is generally compact car, using long easy dynamic EV as standard, battery capacity 30kWh, trickle charge power 7kW, fast charge power 25kW.It is public
Friendship car is medium-and-large-sized vehicle, and using SWB612SC super capacity public transports car as standard, its capacitive energy is 5kWh, and the charging interval is
1min, charge power 300kW.Definition typical week is the degrees centigrade of temperature 25, traffic well one week.Define high temperature week
It is temperature more than 35 degree, other conditions and typical all identicals one week.Congestion week is traffic heavy congestion in peak period on and off duty, its
His condition and typical Zhou Xiang identicals one week.Typical week, high temperature week, congestion the inside of a week, nonworkdays active strokes, temperature are handed over
Logical and its corresponding electric automobile quantity provides in annex.Emulation start time assumes that all batteries of electric automobile SOC are
100%.Setting simulation step length is 1min, and simulation time is 20 weeks.
5.3.2 simulation result
(1) each node charging load of typical case's week lower Jincheng City A, B class car.
As shown in Figure 10, simulation result shows, for having the node of fast charge and trickle charge ability such as 11-16 simultaneously, charges
Load is one week to be that tendency is relatively gentle from the point of view of the cycle, peak-valley ratio is less than 30%.The main node such as 1 for undertaking fast charge function,
7th, 8,9,10 charging load fluctuations are larger, and peak-valley ratio is higher, and average peak-valley ratio is more than 75%, and highest is more than 90%.
(2) each node charging load of typical case's week lower Jincheng City bus.
As shown in figure 11, simulation result shows that super capacity public transport car is relatively equal in each website charging power load distributing daily
Even, night parking rest does not produce charging load.
(3) each node charging load of typical case's week lower Jincheng City taxi.
As shown in figure 12, simulation result shows that taxi produces charging load in each website each time, and in morning
Hand over to the next shift the period produce charging peak, it is similar to internal combustion engine taxi morning oiling characteristic.
(4) typical case's week lower Jincheng City charging electric vehicle total load.By all kinds of charging electric vehicle load superpositions of each node.
As shown in figure 13, simulation result shows, the class car of A, B two for accounting for absolute quantity determines the general trend of charging load.
(5) high temperature week lower Jincheng City charging electric vehicle load.Typical week is subjected to lateral comparison with high temperature Zhou Jiedian 10；
As shown in figure 14, simulation result shows, when temperature keeps a high position that node charging load peak can be caused to hold in one week
Continuous time increase, more typical Zhou Pingjun increase about 2.5 hours daily.
(3) under congestion serious conditions, Jincheng City charging electric vehicle load.Typical week is entered with congestion Zhou Jiedian 15
Row lateral comparison；
As shown in figure 15, simulation result shows, on the one hand node can be caused to fill after traffic congestion deterioration in city
The increase of electric load peak period, more typical Zhou Pingjun increase about 3 hours daily.On the other hand load peak can be caused to increase
Height, wherein load peak increase about 550kW.It can also be seen that traffic change also has shadow to duration of load application distribution
Ring, the peak for the load that charges shifts to an earlier date, pre-set time 15-20min.
(6) with the contrast of other method prediction result.Electric automobile quantity is arranged to 240,000 amounts, simulation step length is arranged to
Min, simulation time are arranged to 1440min (one day), and other simulation parameters are with document《Charging electric vehicle based on Trip chain is born
Lotus forecast model》(Chen Lidan, Nie Yongquan, clock celebrating [J] electrotechnics journals, 2015, (04):216-225.) it is configured.
Each node is charged, and load is superimposed to obtain region-wide total charging load condition, is contrasted with document simulation result；
As shown in figure 16, the prediction data of two methods is contrasted it can be found that there are two load peaks in one day, it is corresponding
In period at noon and the dusk period.Wherein peak value tendency is closer to the dusk period, and the inventive method period at noon peak value is bright
Aobvious to be higher than bibliography method, reason is：The spatial distribution of electric automobile is described as the phase in 5 big regions by above-mentioned literature method
Mutually transfer, majority charging moment occur in Trip chain ending phase.The inventive method simulate in the region of electric automobile when
Idle running moves, and simulation result finds to occur that fairly large electric automobile is quickly filled in job site around noon daily
Electricity.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
Cross above preferred embodiment the present invention is described in detail, it is to be understood by those skilled in the art that can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (6)
1. meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodology, it is characterised in that：This method include with
Lower step：
S1：First all kinds of electric automobiles are classified, analyze its space transfer and charge characteristic；
S2：Make abstract simulation to urban traffic network with Graph-theoretical Approach using Trip chain is theoretical；
S3：All kinds of electric automobiles are set up using Monte Carlo simulation and the random shortest path method based on Markovian decision
Time-spatial diversion model；
S4：Traffic and temperature energy consumption model are established according to electric automobile actual test data.
2. meter as claimed in claim 1 and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, its feature
It is：The step S1 is specially：
Charging modes are fixed parking stall trickle charge, and trip mode is come out early and return late for working day, stopped over, and day off steps out
EV types be private car A classes；
Charging modes are to be come out early and return late by way of ground fast charge, trip mode for working day, are stopped over, the EV that day off steps out
Type is private car B classes；
Charging modes are fixed parking stall trickle charge, and trip mode is that working day traffic path is fixed, the EV classes that day off does not go on a journey
Type is officer's car A classes；
Charging modes are by way of ground fast charge, and trip mode is that working day traffic path is fixed, and the EV types that day off does not go on a journey are
Officer's car B classes；
Charging modes are by way of bus stop quick charge, and trip mode is that traffic path is fixed with the travel time, whole year operation
EV types be bus；
Charging modes are to be fixed by way of ground fast charge station quick charge, trip mode to set out with the off-running time, and route is random, annual
The EV types of operation are taxi.
3. meter as claimed in claim 1 and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, its feature
It is：The step S2 is specially：
Trip chain is divided into simple chain and compound catenary；Simple chain only has one for the trip purpose of every Trip chain；Compound catenary is
Trip chain contains multiple trip purposes；It is divided into residence H, place of working W, shopping use by resident trip survey data to trip purpose
Eat SE, social entertainment SR, other O, while is also charging electric vehicle place；
Wherein, the electrically-charging equipment that residence H is set is fast charge+trickle charge；
The electrically-charging equipment that place of working W is set is fast charge+trickle charge；
The electrically-charging equipment that shopping dining SE is set is fast charge；
The electrically-charging equipment that social entertainment SR is set is fast charge；
The electrically-charging equipment that other O are set is fast charge；
Urban traffic network is abstracted as by digraph using the method for graph theory, each highway in city is arranged to side, will be handed over
Cross road mouth is arranged to node, and road length is arranged to the weight on side, and transportation network is abstracted as to the set of side and node.
4. meter as claimed in claim 1 and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, its feature
It is：The step S3 is specially：
For each EV in region, it is known that after its starting point, using the Markovian decision with random chance factor gamma
Process obtains the optimal path with randomness in its space；
Wherein, the Markovian decision process is the state observed according to each moment, is selected from available action collection
Made decisions with an action, the state of system next step is random, and its state transition probability has Markov property；
Markovian decision process is：
M=(T, S, A (i), p, R)
Wherein, T is decision-making moment collection, represents to choose the time point set of action；S is state set, represent at each decision-making moment, be
System state space collection；A (i) collects for action, represents at any one decision-making moment, the state that policymaker observes, i.e., in state i
The available action collection chosen；P (j | i, it is a) state transition probability, represents under state i, a that takes action is transferred to state j's
Probability；R (i, it is a) Reward Program, represents under state i, after a that takes action, the return of policymaker's acquisition；
That is Markovian decision dynamic process is as follows：Policymaker state S (0) where T (0) moment, the selection one from A (0)
Act a (0) to perform, arrived next S (1) state by P (S (1) | S (0), a (0)) probability random transferring after execution, then held again
One action a (1) of row, successively backward, to the last moment T (N), reaches end-state S (N)；
It is the rule that a decision system selects action on each different conditions to define decision rule, is designated as f (S (0)), f (S
(1)),f(S(2))…f(N)；Definition strategy is the sequence of decision rule, is designated as π=(f (S (0)), f (S (1)), f (S (2)) ...
F ((S (N))), the collection of All Policies is collectively referred to as policy class, is designated as Π；From original state S (0) simultaneously used tactful π ∈ Π,
So thatMaximum or minimum value is obtained, then tactful π is optimal policy；Define value function VπIn state S during to use tactful π
(i) expected returns：
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Wherein E is it is expected, StFor t status, t=0 is corresponded to original state S (0), is expressed as with recursive form：
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To each tactful π, its corresponding value function VπA series of unique public solution of linear equations, optimal policy by based on
Recursive method obtains the expected return value of Dynamic Programming backward；
All node sets in region are considered as S, each node time instance passed through from origin-to-destination traveling is considered as the decision-making moment
t；Determine that next paths of traveling are considered as action a in each node, the path length that will be travelled is considered as return R；
If EV is travelled with part probability transfer mode under steam, it is equally distributed random general to introduce the obedience that value is 0~0.5
Rate factor gamma, represent, when policymaker is often performed with this action, there is probability γ to be transferred to another paths；By in Path selection
Shortest path is combined with randomness, is not influenceed by the original state and initial action set out, surplus for next decision-making
The strategy of remaining decision rule composition is optimal policy；
(1) for private car：
Working day, private car active strokes share 3 kinds：Active strokes 1：H-W；Active strokes 2：H-W-SR/SE/O；Active strokes
3：H-W+H-SR/SE/O；It is each to obey certain proportion；Working day each car Trip chain C obeys probability distribution：P (C)=pi(i=1,
2,3)
X obeys Normal probability distribution between at the beginning of every section of stroke, and its probability density function is：
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Wherein σeFor Variance of Normal Distribution, μeFor normal distribution average；
All electronic private cars are considered as totality, each electric automobile is considered as a two-dimensional random variable, its Joint Distribution letter
Number is：P (C ∩ x)=P (C) P (C | x), Trip chain type and departure time are extracted by the method for Monte Carlo simulation；
The random most short used time path computing of meter and real-time traffic：If each link length is Li, the travel speed of each road of t
For Vi,t, each side right value is set as by T the time required to each sidei, then havePass through band random chance factor M DP algorithm
The relevant parameter such as random most short used time path, the travel speed of corresponding automobile different sections of highway is obtained, obtains the when space division of private car
Cloth；
(2) for officer's car
Probability distribution between at the beginning of every section of stroke is identical, equal Normal Distribution, using the Meng Teka similar with private car
Lip river analogy method obtains the spatial and temporal distributions of officer's car；
(3) for taxi
Each taxi is 0:When 00 from company location, randomly select destination, using meter and real-time traffic it is most short
Used time, road obtained most short used time path；It is random between at work to run time return taxi company of being handed over to the next shift until two, with electricity
Dynamic taxi travel speed is corresponding, obtains the spatial and temporal distributions of each electric taxi；
(4) for bus
Time of departure obedience is uniformly distributed, for each bus, using extracted by the way of Monte Carlo simulation dispatch a car when
Between with the parking of each website make the time, corresponding different sections of highway travel speed, obtain the spatial and temporal distributions of bus.
5. meter as claimed in claim 1 and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, its feature
It is：The step S4 is specially：
S401：Traffic energy consumption model is established according to electric automobile actual test data：
The measured data modeling of the energy consumed based on the identical mileage of different category of roads downward drivings, obtains corresponding to daily not
With period and the specific energy consumption under the real-time jam situation of different road conditions, through street unit mileage power consumption is Eksl=0.247+
1.520/V-0.004*V+2.992*10(-5)*V；
Trunk roads unit mileage power consumption is Ezgl=-0.179+0.004*V+5.492/V；
Secondary distributor road unit mileage power consumption is Ecgl=0.21-0.001*V+1.531/V；
Branch road unit mileage power consumption is Ezl=0.208-0.002*V+1.553/V；
Wherein Eksl、Ezgl、Ecgl、EzlThe electric automobile unit mileage power consumption of through street, trunk roads, secondary distributor road, branch road is represented respectively
Amount, V represent electric automobile road-section average travel speed；
For each electric automobile in region, certain position is obtained according to its Temporal And Spatial Distribution Model at moment, it is corresponding to hand in real time
Moment EV units mileage power consumption is obtained after logical congestion；
S402：Temperature energy consumption model is established according to electric automobile actual test data：
The calculation formula of least square air-conditioning unlatching rate is：
Kpect=7 × 10-5·TEMP4-0.0887·TEMP3+3.673·TEMP2-56.302·TEMP+310.7681
Wherein TEMP represents temperature, KpectAir-conditioning unlatching rate is represented, draws out its functional image；
Temperature energy consumption proportionality coefficient is defined, i.e., unit distance power consumption corresponding to different temperatures and benchmark power consumption after air-conditioning unlatching
The ratio between, the fitting formula of least square temperature proportional coefficient is：
Ktemp=10-3·(TEMP-10)2+1.2
Wherein KtempFor temperature proportional coefficient, TEMP is temperature, draws out its functional image；
According to temperatures above energy consumption model, the real time temperature at corresponding estimation range moment, electric automobile air conditioner in region is obtained
To the influence situation of electric automobile unit mileage power consumption after the ratio of unlatching, and air-conditioning unlatching.
6. meter as claimed in claim 1 and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodologies, its feature
It is：The charging Load Calculation Method of all kinds of electric automobiles：
The charging matrix of loadings PE of m rows n row is defined, each time interval is represented per a line, each row represent each node, matrix
The charging load at element representation place corresponding moment, i.e. PEi,jRepresent the charge power at i-node j moment；The number of time interval
M, i.e. row in matrix quantity are measured, depending on temporal resolution and the requirement calculated；
(1) A classes private car and officer's car：According to the traveling spatial and temporal distributions of each electric automobile, charging modes, it is obtained at place
The charging load of parking site；For A classes private car, officer's car, because its place of charging is fixed, each electric automobile institute
Caused charging load corresponds to a certain row of PE matrixes；
(2) B classes private car, officer's car：Consider that environment temperature calculates unit mileage power consumption with traffic, correspond to this Duan Lu
Cheng Changdu obtains this section of distance power consumption ER1；If dump energy EC is not enough to be unsatisfactory for reaching next destination, that is, it is unsatisfactory for
EC>ER1, then electric automobile current location carry out a quick charge, charging duration tcr extracted using Monte Carlo mode,
And it is limited within 30min；If the charging time started is TS, the charging end time is TE, then has：TE=TS+tcr；
It is by needing to judge that can dump energy meet road during next place with A class private car calculation process difference
Journey needs；It is corresponding each with PE matrixes by way of the load that charged caused by charging place at it for B classes private car, officer's car
Row；
(3) taxi：Taxi is A class private cars for a long time, without fixed destination traveling, and taxi is by way of charging place
The corresponding each row with PE matrixes of caused charging load；
(4) bus：Bus is the A class private cars that route is fixed, battery capacity and unit mileage power consumption are bigger, bus
In the corresponding each row with PE matrixes of load that charged caused by each website.
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