CN104899667B - A kind of Forecasting Methodology of electric vehicle charge and discharge behavior - Google Patents
A kind of Forecasting Methodology of electric vehicle charge and discharge behavior Download PDFInfo
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Abstract
A kind of Forecasting Methodology of electric vehicle charge and discharge behavior, the described method comprises the following steps：A. electric vehicle charge and discharge are formulated to constrain substantially；B. set objectives function；C. MSN influence powers are calculated；D. it utilizesCrisscross inheritance particle cluster algorithm parameter is modified；E. premised on electric vehicle charge and discharge constrain substantially, object function is solved using modified crisscross inheritance particle cluster algorithm, obtains charge and discharge plan and the distribution network load of electric vehicle.The present invention predicts the charge and discharge plan of automobile user using crisscross inheritance particle cluster algorithm and has fully considered influence of the mobile community network to charge and discharge plan, the accuracy of prediction result is substantially increased, network load can be adjusted for power supply department, reliable reference data is provided.
Description
Technical field
The present invention relates to a kind of Forecasting Methodologies for considering the electric vehicle charge and discharge behavior that mobile community network influences, and belong to
In transmission ＆ distribution electro-technical field.
Background technology
The large-scale use of electric vehicle forms huge charge requirement, is also brought to the planning of power grid, operation huge
Big challenge, therefore the charge and discharge behavior of electric vehicle is predicted and studied just be particularly important.
With tou power price strategy improve (abbreviation of Vehicle-to-grid, it describes such a with V2G
System：It is by the electro-motor for being connected to power grid that energy is defeated when hybrid electric vehicle or pure electric vehicle be not in operation
To power grid, in turn, when the battery of electric vehicle needs to be full of, electric current can extract from power grid and be given to battery) technology
Development, electric vehicle gradually starts the role for playing the part of network load attemperator while as the vehicles.It is same with this
When, mobile community network (Mobile Social Network, MSN) is growing, into for people's lives indispensable one
Part.The influence power of MSN can influence even to change the charge and discharge plan of user, and then " peak clipping is filled out for optimization to a certain extent
Paddy ", even automobile user create bigger income.
The existing electric vehicle charge and discharge Multiobjective Optimal Operation scheme based on tou power price utilizes crisscross inheritance particle
The charge and discharge plan of group's algorithm prediction automobile user, idiographic flow are as shown in Figure 1.The program can be predicted relatively accurately
The charge and discharge plan of electric vehicle, and network load adjusting is relatively apparent, user's cost is smaller.However there is no consider for this scheme
Influences of the MSN to user's charge and discharge plan, is affected to some extent precision of prediction, it is therefore necessary to be improved.
Invention content
It is an object of the invention to be directed to the drawback of the prior art, provide a kind of Consideration comprehensive electric vehicle charge and discharge
The Forecasting Methodology of electric behavior adjusts network load for power supply department and provides reliable reference data.
Problem of the present invention is realized with following technical proposals：
A kind of Forecasting Methodology of electric vehicle charge and discharge behavior, the described method comprises the following steps：
A. electric vehicle charge and discharge are formulated to constrain substantially：
PLij＜ Pij＜ PHij
- C/5 ＜ Iij＜ C/3
SOCijmin＜ SOCij＜ SOCijmax,
Wherein, PijFor i-th vehicle period j charge-discharge electric power, positive value for electric discharge, negative value for charging；PLijRepresent electricity
Electrical automobile maximum charge power is negative value；PHijMaximum discharge power is represented, is positive value；IijFor electric vehicle i filling in moment j
Discharge current, C are full of required electric current for vehicle lithium battery 1h；SOCijFor the quantity of electric charge of i-th electric vehicle in time j,
SOCijminAnd SOCijmaxLowest charge amount and maximum amount of charge are represented respectively；
B. set objectives function：
1. network load mean square deviation computation model when building electric vehicle charge and discharge：
In formula, P1For network load mean square deviation；pTjWhen networking for no electric vehicle, network is in the load of period j；Pave
Average load after networking for electric vehicle；N represents the quantity of electric vehicle；
2. build automobile user income calculation model：
In formula, T1For the income of electric vehicle, negative value represents profit, and positive value represents loss；MjFor electricity price, positive value, which represents, fills
Electricity price, negative value represent to power grid and feed electricity price；
3. above-mentioned two model is converted into single model by weighting processing, final object function is obtained：
Alpha+beta=1,
T is T in formula1And P1Final goal value after weighting merging, α, β are weight coefficient；PmaxFor former network load；Tmax
The cost needed when receiving and adjust for electric vehicle from electricity minimum to electricity maximum value；
C. MSN influence powers ω is calculated2：
ω2=p+q,
In formula, p is external action, and q is internal influence, first respectively with reference to influence of the different p and q values for network
Beginningization p=0.005, q=0.7；
D. ω is utilized2Crisscross inheritance particle cluster algorithm parameter is modified：
Basic particle group algorithm module in crisscross inheritance particle swarm algorithm model is as follows：
In formula,Represent the particle l positions that d is tieed up in kth time iteration；To represent particle l in kth time iteration
The speed of d dimensions；The optimal location tieed up in k iteration in d for particle l；For all particles in k iteration
The optimal location of d dimensions；ω is inertial factor；C1、C2For Studying factors；
Utilize ω2To particle cluster algorithm inertia weight and Studying factors amendment：
ω=a ω1+ b (1- ω2),
C1=C1s+ sin ω,
C2=C2s+ cos ω,
Wherein：
In formula, a, b are MSN influence powers and the weight coefficient of inertial factor weighting, meet a+b=1, C1sAnd C2sRespectively
Studying factors C1And C2Adjusted value, ω1For primary particle group's algorithm dynamic change inertial factor, ωmaxFor ω1Theoretical maximum
0.9, ωminFor ω1Theoretical minimum value 0.4, n be particle cluster algorithm in current particle iterations, N be particle cluster algorithm grain
Sub iterations in total；
E. premised on electric vehicle charge and discharge constrain substantially, using modified crisscross inheritance particle cluster algorithm to target letter
Number is solved, and obtains charge and discharge plan and the distribution network load of electric vehicle.
The charge and discharge plan of present invention prediction automobile user has simultaneously fully considered mobile community network to charge and discharge meter
The influence drawn substantially increases the accuracy of prediction result, can adjust network load for power supply department and provide reliable reference
Data.
Description of the drawings
The invention will be further described below in conjunction with the accompanying drawings.
Fig. 1 is the transfer automobile charge and discharge Multiobjective Optimal Operation protocol procedures figure based on tou power price；
Fig. 2 is electric vehicle charge and discharge concept map under the influence of MSN；
Fig. 3 is to predict flow chart based on the MSN electric vehicle charge and discharge influenced；
Fig. 4 is the network load under 3 kinds of scheduling strategies.
Each symbol inventory is in figure and in text：PijFor i-th vehicle period j charge-discharge electric power；PLijRepresent electronic vapour
Vehicle maximum charge power；PHijRepresent maximum discharge power；IijFor electric vehicle i moment j charging and discharging currents；C is vehicle lithium
Battery 1h is full of required electric current；SOCijFor the quantity of electric charge of i-th electric vehicle in time j, SOCijminAnd SOCijmaxRespectively
Represent lowest charge amount and maximum amount of charge；P1For network load mean square deviation；pTjWhen networking for no electric vehicle, network is in the time
The load of section j；PaveAverage load after networking for electric vehicle；N represents the quantity of electric vehicle；T1Receipts for electric vehicle
Benefit；MjFor electricity price；T is T1And P1Final goal value after weighting merging, α, β are weight coefficient；PmaxFor former network load；Tmax
The cost needed when receiving and adjust for electric vehicle from electricity minimum to electricity maximum value；ω2For MSN influence powers；P is outside
It influences；Q is internal influence；Represent the particle l positions that d is tieed up in kth time iteration；To represent particle l in kth time repeatedly
The speed of Dai Zhong d dimensions；The optimal location tieed up in k iteration in d for particle l；It is all particles in k iteration
In d dimension optimal location；W is inertial factor；C1、C2For Studying factors；A, b is weight coefficient；C1sAnd C2sRespectively learn
Practise factor C1And C2Adjusted value；ω1For primary particle group's algorithm dynamic change inertial factor, ωmaxFor ω1Theoretical maximum
0.9, ωminFor ω1Theoretical minimum value 0.4, n be particle cluster algorithm in current particle iterations, N be particle cluster algorithm grain
Sub iterations in total.
Specific embodiment
The present invention includes two parts content：MSN is to the influence power of electric vehicle charge and discharge behavior；It is electronic under this influence power
The charge and discharge plan of automobile.It elaborates with reference to Fig. 2, Fig. 3 to the present invention：
(1) Fig. 2 is influence power concept maps of the MSN to electric vehicle charge and discharge behavior.As a composition grain in MSN
Son, the informational influence that electric vehicle is propagated node propagation by information in network (are known as external action p) and network internal phase
Individual (other electric vehicles) idea of association, behavioral implications (are known as internal influence q).P and q has collectively constituted MSN to electronic vapour
The influence power of vehicle charge and discharge, is formulated as：
ω2=p+q (1)
In formula, ω2As MSN is to the influence power of electric vehicle charge and discharge.
MSN influence powers influence electric vehicle charge and discharge behavior, adjust the former network load of distribution region composition, reach and " cut
The purpose of peak load ".
(2) Fig. 3 is to predict flow chart based on the MSN electric vehicle charge and discharge influenced, and specific steps are described as follows：
Step S1:Start.
Step S2:Electric vehicle charge and discharge are formulated to constrain substantially.It must satisfy following constraint during electric vehicle charge and discharge：Electricity
Electrical automobile charge-discharge electric power must protect battery to greatest extent within the tolerance range of batteries of electric automobile；At the same time,
The quantity of electric charge of batteries of electric automobile will also meet user using needs at any time, within the scope of quantity of electric charge bound.Restricted model
It is as follows：
PLij＜ Pij＜ PHij (2)
- C/5 ＜ Iij＜ C/3 (3)
SOCijmin＜ SOCij＜ SOCijmax (4)
PijFor i-th vehicle period j charge-discharge electric power, positive value for electric discharge, negative value for charging；PLijRepresent electronic vapour
Vehicle maximum charge power is negative value；PHijMaximum discharge power is represented, is positive value；IijFor electric vehicle i moment j charge and discharge
Electric current, C are full of required electric current for vehicle lithium battery 1h；SOCijFor the quantity of electric charge of i-th electric vehicle in time j,
SOCijminAnd SOCijmaxLowest charge amount and maximum amount of charge are represented respectively.
Step S3:Set objectives function.The model of the present invention is stable and ensure electric vehicle income to adjust network load
For target making object function, object function meets the constraints in step S2.
The stability of network load can represent that mean square deviation is smaller to represent that network load is more stable with load mean square deviation.
Network load mean square deviation computation model when building electric vehicle charge and discharge：
In formula, P1For network load mean square deviation；When networking for no electric vehicle, network is in the load of period j；PijIt is
I vehicle period j charge-discharge electric power, positive value for electric discharge, negative value for charging；PaveIt is average negative after networking for electric vehicle
Lotus；N represents the quantity of electric vehicle.
By tou power price scheduling strategy and V2G technologies, automobile user can pass through Reasonable Regulation And Control electric vehicle electricity
The pond quantity of electric charge obtains income：During charging, this moment income is negative, and during electric discharge, this moment income is just.
Automobile user income calculation model：
In formula, T1It is to bear to represent user's income just, to lose just to represent user for the income of electric vehicle；PijIt is i-th
Vehicle is in the charge-discharge electric power of period j；MjElectric vehicle charging electricity price is represented for positive value, electric vehicle is represented to electricity for negative value
Net feed electricity price.
In order to ensure that network load is optimal while ensure user's income, above-mentioned two model is converted into list by weighting processing
One model：
Formula (8) meets constraints：
Alpha+beta=1 (9)
T is the two weighting final goal value in formula, and α, β are weight coefficient；PmaxFor former network load；TmaxFor electric vehicle
Receive the cost needed when adjusting from electricity minimum to electricity maximum value.
Ensure automobile user income while formula (8) is final goal function, i.e. network load minimum.Differ
Formula (2), (3), (4) are the base region constraint of electric vehicle charge and discharge.
Step S4:Calculate MSN influence powers.MSN influence power computational methods are formula (1), and this will not be repeated here.
Step S5:MSN influence powers are added, crisscross inheritance particle cluster algorithm parameter is modified.
Basic particle group algorithm module in crisscross inheritance particle swarm algorithm model is as follows：
In formula,Represent the particle l positions that d is tieed up in kth time iteration, i.e. electric vehicle a certain moment fills
Discharge power；To represent the particle l speed that d is tieed up in kth time iteration；It is tieed up in k iteration in d for particle l
Optimal location；The optimal location tieed up in k iteration in d for all particles；ω is inertial factor, represents electric vehicle
The coefficient of charge and discharge behavior is not changed；C1、C2For Studying factors, represent respectively electric vehicle according to own situation, extraneous circumstance into
The coefficient of row charge and discharge Behavioral change, remaining parameter are particle cluster algorithm basic parameter, and therefore not to repeat here.
Influences of the MSN to electric vehicle charge and discharge is embodied in and particle cluster algorithm inertia weight is repaiied with Studying factors
On just, it is described as follows：
ω=a ω1+ b (1- ω2) (12)
C1=C1s+sinω (13)
C2=C2s+cosω (14)
Wherein：
In formula, a, b are MSN influence powers and the weight coefficient of inertial factor weighting, meet a+b=1, C1sAnd C2sRespectively
Studying factors C1And C2Adjusted value, ω1For primary particle group's algorithm dynamic change inertial factor, ωmaxFor ω1Theoretical maximum
0.9, ωminFor ω1Theoretical minimum value 0.4, n be particle cluster algorithm in current particle iterations, N be particle cluster algorithm grain
Sub iterations in total；
Step S6:Initialize master data.The basic parameter of model is set, prepares emulation.
Step S7:Using crisscross inheritance PSO Algorithm, wherein premised on charge and discharge constrain, by object function
Solve adaptive value.
Step S8:Acquire optimal solution, as electric vehicle charge and discharge plan.
Step S9:Terminate.
The present invention closer to real daily life situation, can by MSN influence powers and tou power price strategy for power department more
Regulate and control electric vehicle charge and discharge behavior truly to predict the charge and discharge behavior of electric vehicle, and then by controlling the two, from
And achieve the purpose that adjust network load, while be automobile user extra earning, excitation user participates in network load and adjusts.Indirectly
It predicts electric vehicle plan of travel, predicts that electric vehicle trip provides technical support for municipal traffic department.
Table 1 is the simulation comparison of the present invention and the prior art as a result, compared with the existing technology, being predicted using this method
When load mean square deviation reduce 1/4, and user's income increases substantially, and meets our simulated target.
1 simulated grid load stability of the present invention (load mean square deviation) of table and user's income
Fig. 4 is the network load under 3 kinds of scheduling strategies, by Fig. 4 it can be found that same tou power price, MSN influence (this
Inventive method) under network load it is gentler, " peak load shifting " effect is more preferable；Meanwhile the grid load curve that no MSN influences
Be shifted 0.5h to the right relative to the curve under the influence of MSN, show the electric vehicle charge and discharge under the influence of MSN adjust more and
When, efficiently.
Claims (1)
1. a kind of Forecasting Methodology of electric vehicle charge and discharge behavior, it is characterized in that, it the described method comprises the following steps：
A. electric vehicle charge and discharge are formulated to constrain substantially：
PLij＜ Pij＜ PHij
- C/5 ＜ Iij＜ C/3
SOCijmin＜ SOCij＜ SOCijmax；
Wherein, PijFor i-th vehicle period j charge-discharge electric power, positive value for electric discharge, negative value for charging；PLijRepresent electronic vapour
Vehicle maximum charge power is negative value；PHijMaximum discharge power is represented, is positive value；IijFor electric vehicle i moment j charge and discharge
Electric current, C are full of required electric current for vehicle lithium battery 1h；SOCijFor the quantity of electric charge of i-th electric vehicle in time j,
SOCijminAnd SOCijmaxLowest charge amount and maximum amount of charge are represented respectively；
B. set objectives function：
1. network load mean square deviation computation model when building electric vehicle charge and discharge：
In formula, P1For network load mean square deviation；pTjWhen networking for no electric vehicle, network is in the load of period j；PaveFor electricity
Average load after electrical automobile networking；N represents the quantity of electric vehicle；
2. build automobile user income calculation model：
In formula, T1For the income of electric vehicle, negative value represents profit, and positive value represents loss；MjFor electricity price, positive value represents charging electricity
Valency, negative value represent to power grid and feed electricity price；
3. above-mentioned two model is converted into single model by weighting processing, final object function is obtained：
Alpha+beta=1；
T is T in formula1And P1Final goal value after weighting merging, α, β are weight coefficient；PmaxFor former network load；TmaxFor electricity
Electrical automobile receives the cost needed when adjusting from electricity minimum to electricity maximum value；
C. MSN influence powers ω is calculated2：
ω2=p+q；
In formula, p is external action, and q is internal influence, with reference to influence of the different p and q values for network, initializes p respectively
=0.005, q=0.7；
D. ω is utilized2Crisscross inheritance particle cluster algorithm parameter is modified：
Basic particle group algorithm module in crisscross inheritance particle swarm algorithm model is as follows：
In formula,Represent the particle l positions that d is tieed up in kth time iteration；To represent that particle l d in kth time iteration are tieed up
Speed；The optimal location tieed up in k iteration in d for particle l；It is tieed up in k iteration in d for all particles
Optimal location；ω is inertial factor；C1、C2For Studying factors；
Utilize ω2To particle cluster algorithm inertia weight and Studying factors amendment：
ω=a ω1+b(1-ω2)
C1=C1s+sin ω
C2=C2s+cos ω；
Wherein：
In formula, a, b are MSN influence powers and the weight coefficient of inertial factor weighting, meet a+b=1, C1sAnd C2sRespectively learn because
Sub- C1And C2Adjusted value, ω1For primary particle group's algorithm dynamic change inertial factor, ωmaxFor ω1Theoretical maximum 0.9,
ωminFor ω1Theoretical minimum value 0.4, n be particle cluster algorithm in current particle iterations, N for particle cluster algorithm particle it is total
Iterations altogether；
E. premised on electric vehicle charge and discharge constrain substantially, using modified crisscross inheritance particle cluster algorithm to object function into
Row solves, and obtains charge and discharge plan and the distribution network load of electric vehicle.
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| CN106655232B (en) * | 2017-01-13 | 2018-12-11 | 东北电力大学 | It is a kind of meter and three-phrase burden balance electric car distribution charge and discharge strategy |
| CN109214095B (en) * | 2018-09-13 | 2023-04-07 | 云南民族大学 | Electric vehicle charging and discharging multi-objective optimization scheduling method |
| CN112907153B (en) * | 2021-01-15 | 2022-11-01 | 中原工学院 | Electric vehicle scheduling method considering various requirements of user in mixed scene |
| CN117634931B (en) * | 2024-01-25 | 2024-04-16 | 华北电力大学 | Electric automobile adjustment capability prediction method and system considering charging behavior |
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| CN102722767A (en) * | 2012-07-02 | 2012-10-10 | 山东鲁能智能技术有限公司 | Electromobile charging and exchanging power station stationing and planning system and method |
| CN103679299A (en) * | 2013-12-30 | 2014-03-26 | 华北电力大学（保定） | Electric automobile optimal peak-valley time-of-use pricing method giving consideration to owner satisfaction degree |
| CN103840521A (en) * | 2014-02-27 | 2014-06-04 | 武汉大学 | Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow |
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| CN102722767A (en) * | 2012-07-02 | 2012-10-10 | 山东鲁能智能技术有限公司 | Electromobile charging and exchanging power station stationing and planning system and method |
| CN103679299A (en) * | 2013-12-30 | 2014-03-26 | 华北电力大学（保定） | Electric automobile optimal peak-valley time-of-use pricing method giving consideration to owner satisfaction degree |
| CN103840521A (en) * | 2014-02-27 | 2014-06-04 | 武汉大学 | Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow |
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