CN109359784A - The electric automobile load time forecasting methods and system of meter and operator - Google Patents
The electric automobile load time forecasting methods and system of meter and operator Download PDFInfo
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Abstract
The present invention provides a kind of meter and the electric automobile load time forecasting methods and system of operator, classify to electric car, obtain vehicle classification, counts the charging behavior of each vehicle classification, based on charging behavior, the probability density function and charge power of charging duration are obtained；According to the probability density function and charge power of charging duration, Vehicular charging predictive information is generated using Monte Carlo Method；It based on Vehicular charging predictive information, will take to change the electric car that electric mode charges and be based on operator's charging strategy model generating battery day charging situation, will not take and change the electric car that electric mode charges and be based on charging row to generate electric car charging situation；Based on battery day charging situation, electric car charging situation, the whole load that charges is obtained, load prediction curve is drawn.The present invention can determine that electric car concentrates influence situation of the electrical changing station to electric automobile load, and configuration electric car concentrates electrical changing station accordingly.
Description
Technical field
The present invention relates to electric vehicle engineering fields, and in particular, to it is a kind of meter and operator electric automobile load it is pre-
Method and system is surveyed, is predicted more particularly, to the electric automobile load in the case of consideration electric car Huan electricity operator.
Background technique
As the more and more trips of New-energy electric vehicle are among daily life, electric car charging access power grid is caused
Load aggravated the burden of load boom period power grid.Therefore load and use caused by reasonable prediction electric car part are appropriate
Method policy to load carry out regulation become reduce electric car access Load on Electric Power Grid impact a kind of thinking.It introduces electronic
Automobile changes battery mode and operator can reduce the randomness of electric car charging behavior to a certain extent, so as to electricity
Electrical automobile charging behavior carries out unified planning and regulation, reduces the peak value of load boom period.
It is pre- that patent document CN106855960A discloses a kind of lower electric car of the Peak-valley TOU power price guidance load that charges
Survey method has initially set up the charging load model of electric car；Then negative using the charging of Monte Carlo simulation method simulating sun
Lotus curve；Then price elasticity matrix of demand is established；Finally calculate the day charging load curve based on Peak-valley TOU power price.This
The characteristics of method is to consider the influence of Peak-valley TOU power price, but the case where do not account for electric car Huan electricity operator.
Patent document CN107122856A discloses a kind of space saturation load forecasting method under new situation, main to wrap
It includes: cell total load is predicted using cell load density index method；According to region future GDP to be measured, population size, electronic vapour
Vehicle permeability, road length and private car owning amount determine the electric car ownership in the region to be measured non-coming year；To electronic vapour
Vehicle is classified, and determines all types of electric car proportions, the type of charging and conversion electric facility and quantity；To all types of electric cars
Part throttle characteristics analyzed, respectively obtain working day electric automobile load and festivals or holidays electric automobile load, the greater be to
Survey region electric car total load；It is assigned to after electric car total load is superimposed with cell total load in each block of cells.The party
The characteristics of method, is to realize target of the electric automobile load prediction with urban power load with optimization, and considers electric car
Electric charging station exists, but it does not in time predict load based on the spatial distribution for load.
Summary of the invention
For the defects in the prior art, the object of the present invention is to provide the electric automobile load of a kind of meter and operator is pre-
Survey method and system.
The electric automobile load prediction technique of a kind of meter and operator that provide according to the present invention, comprising:
Motor vehicle behavior statistic procedure: classifying to electric car, obtains vehicle classification, counts the charging of each vehicle classification
Behavior is based on charging behavior, obtains the probability density function and charge power of charging duration；
Charge information generation step: according to the probability density function and charge power of charging duration, it is pre- to generate Vehicular charging
Measurement information；
Charge carry calculation step: being based on Vehicular charging predictive information, will take and change the electric car life that electric mode charges
At battery day charging situation, it will not take and change the electric car that electric mode charges and be based on charging row to generate electric car and charging feelings
Condition；
Prediction curve plot step: being based on battery day charging situation, electric car charging situation, obtains whole charge and bears
Lotus draws load prediction curve.
Preferably, the charge information generation step includes:
Set period of time step: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n
Time sampling point is denoted as t at the time of correspondence1,t2,...,tn；
It calculates moment load step: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle in tkThe load that moment generates.
Preferably, i-th vehicle is in tkThe load P that moment generatesi(tk) Monte Carlo Method is used, obtain Vehicular charging
Initial time and Vehicular charging duration, when obtaining charging starting according to Vehicular charging initial time and Vehicular charging duration calculation
It carves, charging finishing time；Initial time, charging finishing time calculating charge using following formula:
Wherein TendFor charging termination moment, TstartFor the initial time that charges, TlastFor charging duration；
Determine i-th vehicle in t each time sampling pointkLogical value whether moment chargesIf time sampling point exists
Tend、TstartBetween, thenIt is set as 1, otherwise, thenIt is set as 0.
Preferably, it will take to change the electric car that electric mode charges and generate based on operator's charging strategy model and fill battery day
Electric situation, wherein operator's charging strategy model is
Wherein,Indicate tkI-th of battery of moment whether charging logical value,Value is 1 or 0；
Δ t indicates the time interval of setting；
M is the battery sum that operator needs to charge；
For the power of i-th of battery charging；
PmaxIndicate operator's maximum electric power；
viThe inverse of duration needed for charging for i-th of battery, i.e. charge rate；
For tkThe Spot Price of moment operator charging.
The electric automobile load forecasting system of a kind of meter and operator that provide according to the present invention, comprising:
Motor vehicle behavior statistical module: classifying to electric car, obtains vehicle classification, counts the charging of each vehicle classification
Behavior is based on charging behavior, obtains the probability density function and charge power of charging duration；
Charge information generation module: according to the probability density function and charge power of charging duration, it is pre- to generate Vehicular charging
Measurement information；
Charge load calculation module: being based on Vehicular charging predictive information, will take and change the electric car life that electric mode charges
At battery day charging situation, it will not take and change the electric car that electric mode charges and be based on charging row to generate electric car and charging feelings
Condition；
Prediction curve drafting module: being based on battery day charging situation, electric car charging situation, obtains whole charge and bears
Lotus draws load prediction curve.
Preferably, the charge information generation module includes:
Setting time root module: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n
Time sampling point is denoted as t at the time of correspondence1,t2,...,tn；
It calculates moment loading module: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle in tkThe load that moment generates.
A kind of computer readable storage medium for being stored with computer program provided according to the present invention, the computer journey
The step of method described in any one of Claims 1-4 is realized when sequence is executed by processor.
Compared with prior art, the present invention have it is following the utility model has the advantages that
The present invention fully considers influence of the electric car charging operator to electric automobile load, introduces electric automobile operation
Quotient can reduce to a certain extent electric car charging behavior randomness, so as to charging batteries of electric automobile behavior into
Row unified planning and regulation reduce the peak value of load boom period electric car charging bring.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is that electric automobile load of the invention predicts overall flow schematic diagram；
Fig. 2 is the quantitative approach flow chart of the electric car to charge in statistics certain time length of the invention.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to the present invention
Protection scope.
After the present invention considers introducing electric car Huan electricity operator, charging batteries of electric automobile process is to network load
It influences.The randomness of electric car charging behavior can be reduced to a certain extent by introducing electric automobile operation quotient, so as to right
Charging batteries of electric automobile behavior carries out unified planning and regulation, reduces the peak of load boom period electric car charging bring
Value, and configuration electric car concentrates electrical changing station accordingly.
The electric automobile load prediction technique of a kind of meter and operator that provide according to the present invention, comprising:
Motor vehicle behavior statistic procedure: classifying to electric car, obtains vehicle classification, counts the charging of each vehicle classification
Behavior is based on charging behavior, obtains the probability density function and charge power of charging duration；
Charge information generation step: according to the probability density function and charge power of charging duration, using Monte Carlo Method
Generate Vehicular charging predictive information；
Charge carry calculation step: being based on Vehicular charging predictive information, will take the electric car base for changing electric mode and charging
Battery day charging situation is generated in operator's charging strategy model, will not taken and be changed electric car that electric mode charges and be based on charging
Behavior generates electric car charging situation；
Prediction curve plot step: being based on battery day charging situation, electric car charging situation, obtains whole charge and bears
Lotus draws load prediction curve.
Specifically, the charge information generation step includes:
Set period of time step: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n
Time sampling point is denoted as t at the time of correspondence1,t2,...,tn；
It calculates moment load step: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle in tkThe load that moment generates.
Specifically, i-th vehicle is in tkThe load P that moment generatesi(tk) Monte Carlo Method is used, obtain Vehicular charging
Initial time and Vehicular charging duration, when obtaining charging starting according to Vehicular charging initial time and Vehicular charging duration calculation
It carves, charging finishing time；Initial time, charging finishing time calculating charge using following formula:
Wherein TendFor charging termination moment, TstartFor the initial time that charges, TlastFor charging duration；
Determine i-th vehicle in t each time sampling pointkLogical value whether moment chargesIf time sampling point exists
Tend、TstartBetween, thenIt is set as 1, otherwise, thenIt is set as 0.
Specifically operator's charging strategy model is
Wherein,Indicate tkI-th of battery of moment whether charging logical value,Value is 1 or 0；
Δ t indicates the time interval of setting；
M is the battery sum that operator needs to charge；
For the power of i-th of battery charging；
PmaxIndicate operator's maximum electric power；
viThe inverse of duration needed for charging for i-th of battery, i.e. charge rate；
For tkThe Spot Price of moment operator charging.
A kind of computer readable storage medium for being stored with computer program provided according to the present invention, which is characterized in that
The computer program realizes the step of above-mentioned method when being executed by processor.
The electric automobile load forecasting system of a kind of meter and operator that provide according to the present invention, comprising:
Motor vehicle behavior statistical module: classifying to electric car, obtains vehicle classification, counts the charging of each vehicle classification
Behavior is based on charging behavior, obtains the probability density function and charge power of charging duration；
Charge information generation module: according to the probability density function and charge power of charging duration, using Monte Carlo Method
Generate Vehicular charging predictive information；
Charge load calculation module: being based on Vehicular charging predictive information, will take the electric car base for changing electric mode and charging
Battery day charging situation is generated in operator's charging strategy model, will not taken and be changed electric car that electric mode charges and be based on charging
Behavior generates electric car charging situation；
Prediction curve drafting module: being based on battery day charging situation, electric car charging situation, obtains whole charge and bears
Lotus draws load prediction curve.
Specifically, the charge information generation module includes:
Setting time root module: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n
Time sampling point is denoted as t at the time of correspondence1,t2,...,tn；
It calculates moment loading module: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle in tkThe load that moment generates.
It is provided by the invention meter and operator electric automobile load forecasting system, can by meter and operator it is electronic
The step process of car load prediction technique is realized.Those skilled in the art can be pre- by the electric automobile load of meter and operator
Survey method is interpreted as the preference of the electric automobile load forecasting system of the meter and operator.
Preference of the invention is further described below for attached drawing.
In the specific implementation process, the charging behavior of all kinds of electric cars is analyzed first.To a regional electronic vapour
Vehicle type is classified.Electric car can be divided into electronic private car by purposes function, electric bus, electric taxi and electronic
Officer's car.Different electric cars has different trip habits, corresponds to different start to charge and the probability of charging duration
Density function.And the vehicle of different vehicle is different, corresponding charge power is often different.
For electric bus, since its effect is the trip for facilitating whole city resident, its charging time necessarily by
To the influence of people's work and rest rule.Bus generally morning 5:30 dispatches a car, and receive class in the evening 23:30, then its charging time can be considered
For 23:30~5:30.Since bus is public transport company's operation, charging is ordered into and can be regulated and controled.And by
Big in bus battery capacity, every daily travel is long, and duration of charge is often also longer.Think rising for electric bus
Begin charging time Normal Distribution N (23.5,3.42), duration of charge also Normal Distribution N (1.77,0.562), it fills
Electrical power is about 12kW.
For electric taxi, it is characterized in that it has vehicle similar with private car, but often travel daily away from
It is close from bus.General electric taxi driver in order to save time, understands the battery of additional carrying about standby or directly matches
The battery of large capacity.Since the night volume of the flow of passengers is reduced, most taxis can charge at night.Electric taxi
Charge time started Normal Distribution N (3,3.42), duration of charge Normal Distribution N (1.52,0.52), charge function
Rate is about 7kW.
For electronic officer's car, its purpose is that providing convenience in work hours and quitting time for employee, and mentioned for company
For offical service.Therefore it has the travel time similar with private car.But general company manages for convenience, may
The charging time of officer's car is arranged in the work hours rather than evening hours, the night management cost of company can be reduced in this way.
Its time started of charging should meet normal distribution N (7,3.42), duration of charge meet normal distribution N (1.88,
0.642), charge power 3.5kW.
For electronic private car, the principal status of public economy is accounted in new energy electric motor vehicle quantity is exactly electronic private car.Private car has
The more regular travel time and come back the time, and daily trip distance is relatively short, is typically only what job site was got home
Distance.Think that the private car initiation of charge time meets normal distribution N (17.6,3.42), since its trip distance is relatively short, because
This every daily consumption electricity is also less, and the charging time meets normal distribution N (0.71,0.282), charge power 3.5kW.
Analysis electric car, which is taken, changes electric proportion.In four class electric cars, due to electric bus, taxi is public
Business vehicle has fixed operating agency management, therefore its electric charging process can consider all by Carrier Management.Electronic private car
Since radix is big, and freedom to act, therefore the main object for changing power mode consideration will be become, consider that the electronic private car in part uses
Change the case where power mode is charged.
As shown in Figure 1, the probabilistic model of the charging behavior of the electric car obtained according to analysis is built using Monte Carlo Method
Vertical electric automobile load prediction model.Specific modeling process is as follows, the citywide electric car quantity obtained first according to prediction
It extracts to obtain charging time started and the duration of charge information of each automobile using Monte Carlo Method, and successively calculates
The period that each electric car charges daily.All electric cars that each detection time point is charging then are counted to cause
Load summation, summarize the load prediction data that whole day is calculated.The specific method is as follows: set step-length as Δ t, it will be one day 24 small
When be divided into t time sampling point of n=24/ Δ, each sampled point is denoted as t at the time of correspondence1,t2,...,tn.If i-th vehicle is in tk
Logical value whether moment charges is set asIts value is 1 or 0.Produced by so vehicle is in the intraday a certain moment
Load are as follows:
Entire city is at a time since electric vehicle accesses load caused by power grid are as follows:
Any time be can be obtained by this way due to the load value of electric car.Wherein N be charge it is electronic
The sum of automobile.To simplify the analysis and analyze worst case, it is believed that the electric car in city requires to carry out at least one daily
Secondary charging, then N is the sum of electric car in city here.
In order to carry out load prediction and simulation, need to know the Boolean whether charging of each moment electric carIt should
Boolean can be used Monte Carlo method and obtain.Since the charging initial time and charging duration of known electric car are distributed,
Therefore charging initial time and the charging finishing time of electric car can be calculated.The specific method is as follows shown in formula:
Wherein TendFor charging termination moment, TstartFor the initial time that charges, TlastFor charging duration.Using Monte Carlo
Method obtains each car and starts to charge information with charging duration, and at the time of it is calculated starts to charge and terminate charging,
And logical value is determined by way of judging each time sampling pointAnd a certain moment is calculated by formula (2)
Load value, and daily load prediction curve is finally calculated.Wherein, at each sampled point decision logic value method flow
As shown in Figure 2.
Then, operator's charging strategy is analyzed.To the battery for the not enough power supply that electric car is changed at operator, operation
Quotient daily will charge to it.Assuming that all battery arrangements charging that operator daily will change the previous day, and want daily
The battery that all the previous days are changed is full of, simultaneously because operator's capacity is limited, the power that synchronization charges exists
The upper limit.Operator will realize the minimum of itself charging cost while limitation more than meeting.The concrete mathematical model of foundation
It is as follows.
If time interval Δ t is 15 minutes (0.25 hour), then one day time can be divided into 96 sampled points in total, point
T is not set as it1,t2,...,tk,...,t96.If M is the battery sum that operator needs to charge,For battery charging power,
viThe inverse of duration needed for charging for i-th of battery,For tkWhen the moment i-th battery whether charging logical value,
For tkThe Spot Price of moment operator charging.So for operator's charging process, target be charging process cost most
It is low, i.e.,
Its charging process needs the constraint condition met are as follows:
It is acquired whether each battery of each moment charges according to above Optimized model using optimization method appropriate
Logical valueIt can summarize to obtain the daily charging load of operator.
Finally, comprehensive operation quotient sub-load and electric car sub-load, draw load prediction curve.
One skilled in the art will appreciate that in addition to realizing system provided by the invention in a manner of pure computer readable program code
It, completely can be by the way that method and step be carried out programming in logic come so that provided by the invention other than system, device and its modules
System, device and its modules are declined with logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion
The form of controller etc. realizes identical program.So system provided by the invention, device and its modules may be considered that
It is a kind of hardware component, and the knot that the module for realizing various programs for including in it can also be considered as in hardware component
Structure；It can also will be considered as realizing the module of various functions either the software program of implementation method can be Hardware Subdivision again
Structure in part.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow
Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (7)
1. the electric automobile load prediction technique of a kind of meter and operator characterized by comprising
Motor vehicle behavior statistic procedure: classifying to electric car, obtains vehicle classification, counts the charging row of each vehicle classification
To be based on charging behavior, obtaining the probability density function and charge power of charging duration；
Charge information generation step: according to the probability density function and charge power of charging duration, Vehicular charging prediction letter is generated
Breath；
Charge carry calculation step: being based on Vehicular charging predictive information, will take and change the electric car generation electricity that electric mode charges
Pond day charging situation will not taken and change the electric car that electric mode charges and be based on charging row to generate electric car charging situation；
Prediction curve plot step: being based on battery day charging situation, electric car charging situation, obtains the whole load that charges, draws
Load prediction curve processed.
2. the electric automobile load prediction technique of meter according to claim 1 and operator, which is characterized in that the charging
Information generation step includes:
Set period of time step: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n time
T is denoted as at the time of sampled point corresponds to1,t2,...,tn；
It calculates moment load step: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle t corresponding to k-th of time sampling pointkThe load that moment generates.
3. the electric automobile load prediction technique of meter according to claim 2 and operator, which is characterized in that described i-th
Vehicle is in tkThe load P that moment generatesi(tk) Monte Carlo Method is used, Vehicular charging initial time and Vehicular charging duration are obtained,
Charging initial time, charging finishing time are obtained according to Vehicular charging initial time and Vehicular charging duration calculation；Charging starting
Moment, charging finishing time, which calculate, uses following formula:
Wherein TendFor charging termination moment, TstartFor the initial time that charges, TlastFor charging duration；
Determine i-th vehicle in t each time sampling pointkLogical value whether moment chargesIf time sampling point is in Tend、
TstartBetween, thenIt is set as 1, otherwise, thenIt is set as 0.
4. the electric automobile load prediction technique of meter according to claim 1 and operator, which is characterized in that changed taking
The electric car that electric mode charges is based on operator's charging strategy model and generates battery day charging situation, wherein the operator
Charging strategy model is
Wherein,Indicate tkI-th of battery of moment whether charging logical value,Value is 1 or 0；
Δ t indicates the time interval of setting；
M is the battery sum that operator needs to charge；
K=1,2 ..., n；The total number of n expression time sampling point；
For the power of i-th of battery charging；
PmaxIndicate operator's maximum electric power；
viThe inverse of duration needed for charging for i-th of battery；
For tkThe Spot Price of moment operator charging.
5. the electric automobile load forecasting system of a kind of meter and operator characterized by comprising
Motor vehicle behavior statistical module: classifying to electric car, obtains vehicle classification, counts the charging row of each vehicle classification
To be based on charging behavior, obtaining the probability density function and charge power of charging duration；
Charge information generation module: according to the probability density function and charge power of charging duration, Vehicular charging prediction letter is generated
Breath；
Charge load calculation module: being based on Vehicular charging predictive information, will take and change the electric car generation electricity that electric mode charges
Pond day charging situation will not taken and change the electric car that electric mode charges and be based on charging row to generate electric car charging situation；
Prediction curve drafting module: being based on battery day charging situation, electric car charging situation, obtains the whole load that charges, draws
Load prediction curve processed.
6. the electric automobile load forecasting system of meter according to claim 1 and operator, which is characterized in that the charging
Information generating module includes:
Setting time root module: setting step-length was divided into n time sampling point for 24 hours one day according to step-length, by n time
T is denoted as at the time of sampled point corresponds to1,t2,...,tn；
It calculates moment loading module: calculating the load that electric car access power grid generates are as follows:
Wherein, k=1,2 ..., n；
Indicate i-th vehicle in tkLogical value whether moment charges,Value is 1 or 0；
Indicate i-th vehicle charge power；
N indicates the sum of the electric car to charge；
Pi(tk) indicate i-th vehicle t corresponding to k-th of time sampling pointkThe load that moment generates.
7. a kind of computer readable storage medium for being stored with computer program, which is characterized in that the computer program is located
The step of reason device realizes method described in any one of Claims 1-4 when executing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811447327.8A CN109359784A (en) | 2018-11-29 | 2018-11-29 | The electric automobile load time forecasting methods and system of meter and operator |
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