CN108320064A - A kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation - Google Patents
A kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation Download PDFInfo
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Abstract
The invention discloses a kind of to cooperate with charging dual-layer optimization dispatching method based on the electric vehicle of multi-objective particle swarm algorithm with wind-powered electricity generation, electric vehicle charging schedule is optimized using multi-objective particle swarm algorithm, includes charging with layer scheduling method from layer scheduling method in the charging of the electric vehicle of time angle and from the electric vehicle of space angle.The present invention improves wind power utilization, reduces carbon emission, reduces user's charging cost and improves user's charging satisfaction simultaneously.
Description
Technical field
The invention belongs to electric vehicle engineering fields more particularly to a kind of electric vehicle to cooperate with charging dual-layer optimization with wind-powered electricity generation
Dispatching method.
Background technology
With the development of economy and people are to the growing interest of environmental problem, and the status of clean energy resource is constantly promoted.Closely
Over a little years, the development of Chinese clean energy resource is very fast, and wind-powered electricity generation is as important one of clean energy resource, in economy and ring
Border, on have huge advantage, the research of each side in relation to wind-powered electricity generation is also increasing.But wind power output have apparent fluctuation and
Demodulate peak feature, after wind-power electricity generation large-scale grid connection the time domain fluctuation of power can cause peak load regulation network scarce capacity, influence electric power
The stable operation of system, in the low ebb phase of load, in the load valley phase, peak-load regulating off-capacity will also lead to the problem of and abandon wind.
Further, since its good environmental benefit, electric vehicle is in the support energetically and development for obtaining country in recent years.With
Flying for the development of electric vehicle engineering, especially the electric motor and controller system of the battery of performance brilliance and charging performance brilliance
Speed development, the development of electric vehicle are obviously accelerated.In the driving process of electric vehicle, the discharge capacity of carbon depends on primary energy
Structure, and the energy resource structure of China is still mainly based on thermoelectricity, therefore the unit mileage carbon emission level of electric vehicle is at certain
It could even be possible to being higher than orthodox car in the case of a little, the emission reduction ability of electric vehicle is greatly limited.It is practised in addition, being gone on a journey by car owner
Used influence, electric vehicle largely charge by networking afterload peak period, will also will increase peak-valley difference and system pressure.A large amount of electricity
The unordered charging behavior of electrical automobile can influence the safety and reliability of system.
Invention content
It is an object of the invention to overcome deficiency in the prior art, a kind of electric vehicle is provided and cooperates with charging double with wind-powered electricity generation
Layer Optimization Scheduling, solves in the prior art that wind-powered electricity generation is unstable, the unordered charging influence power system stability of a large amount of electric vehicles,
The technical issues of reducing the safety and reliability of wind power utilization and power grid.
In order to solve the above technical problems, the technical solution adopted in the present invention is：A kind of electric vehicle is cooperateed with wind-powered electricity generation to be filled
Electric double layer Optimization Scheduling, including：From the electric vehicle of time angle charging upper layer scheduling and from space angle
Electric vehicle charging lower layer scheduling；
Electric vehicle charging upper layer scheduling specifically comprises the following steps：
(1) when electric vehicle needs charging, the initial SOC and target SOC of electric vehicle are read out, according to reading
Breath of winning the confidence predicts electric automobile load；
(2) according to the information of load prediction, cost of electricity-generating minimum with carbon emission amount be minimum and system equivalent load variance most
Small is target, is optimized to upper layer scheduling model using particle cluster algorithm, and electric vehicle charging schedule upper layer policy is generated；
The scheduling of electric vehicle charging lower layer specifically comprises the following steps：
(1) according to the degree of crowding of different charging stations, the charging electricity price of timesharing subregion is calculated；
(2) according to upper layer scheduling model as a result, calculating electric vehicle quantity to be charged of each period；
(3) being charged with automobile user, expense is minimum and charging queuing time is most short for target, uses multiple target grain
Swarm optimization optimizes solution to lower layer's scheduling model, obtains optimal charging strategy.
Further, the carbon emission amount mainly considers the carbon emission amount and electric vehicle Life cycle of thermoelectricity itself
The carbon emission amount corresponded to later in charging is calculated, computational methods are as follows：
In formula, EhIndicate the carbon emission factor of thermoelectricity, the hop count when optimization of T tables is total；IevIndicate electric vehicle unit quantity of electricity carbon
Discharge；Ph,tIndicate the thermal power output of t periods, Pev,tFor t period electric vehicle charge powers.
Further, corresponding to the carbon emission amount on charging after the accounting of electric vehicle Life cycle includes mainly：Electricity
Carbon emission amount during electrical automobile lithium battery manufacture part and use；
Wherein, lithium battery manufactures the amount of carbon dioxide calculating that every degree electricity discharge is filled in part carbon emission amount reduction to electric vehicle
Formula is as follows：
In formula, IsumRepresent the total carbon emission amount of production lithium battery, SsumFor total kilometres, W is hundred kilometers of power consumption, ηc
Represent charge efficiency.
Further, the cost of electricity-generating includes fired power generating unit cost of electricity-generating and carbon transaction cost, and computational methods are as follows：
In formula,Indicate the thermal power unit operation cost of t moment；Indicate the carbon transaction cost of t moment；
Wherein：The thermal power unit operation method of cost accounting is as follows：
In formula, ai、bi、ciFor the cost of electricity-generating coefficient of i-th generator；, i expression generating set serial numbers；N is generator
Sum, Pi,tIt contributes for i-th generator t moment；
Carbon transaction costComputational methods it is as follows：
In formula, K is carbon transaction price, Pc,tFor the practical carbon emission amount of t moment system, MtFor t moment carbon emission quota,ε is quota coefficient.
Further, the computational methods of the equivalent load variance are as follows：
In formula, T is hop count when optimizing total；M is to investigate time window；K indicates time window serial number；Pl,tFor t moment routine
Load, Pw,tFor t moment wind power output, Pev,tFor t period electric vehicle charge powers；Pav,iIt is equivalent for i-th of time window
The average value of load,
Further, the method for calculating the charging electricity price of timesharing subregion is as follows：
By charging, electricity price guides electric vehicle orderly to charge on Spatial Dimension, and calculation formula is：
CRTOU,t,j=Cgrid,t+(Cser+Cj)
In formula：CRTOU,i,jIndicate the timesharing subregion charging electricity price of t period jth charging stations；CserIndicate that charging station basis is filled
Electric service price；CjIndicate the service price knots modification determined by jth charging station crowding,Its
In：Ni-1,jIndicate the electric vehicle quantity of (t-1) period jth charging station；Indicate the average electricity of (t-1) period each charging station
Electrical automobile quantity；△ C indicate the service price variable quantity caused by unit crowding.
Further, the computational methods of automobile user charging expense are as follows：
In formula：f1Represent charging expense；T indicates hop count when optimization is total；NtIndicate t periods electric vehicle quantity to be charged；
xtnIndicate that n-th electric vehicle of t periods charges in jth charging station, xtn=j, j=1,2 ... ... Jm, Jm indicate that charging station is total
Number.
Further, charge queuing time calculation formula it is as follows：
tnj=twj+tcnj+tdnj
In formula：tnjIndicate n-th electric vehicle to j charging stations charging total time used；twjWhen to be lined up in station
Between, the electric vehicle quantity and the j charging station scales that are charged by this period to j charging stations determine, are approximately considered twjOnly with fill
The directly proportional k of electric vehicle numberjFor proportionality coefficient, twj=kjNi,j；Ni,jIt is the electric vehicle just to charge in j charging stations the period
Number；tcnjFor the electric vehicle charging time；tdnjThe running time that charging station j is reached for n-th electric vehicle, is arrived with electric vehicle
The distance of charging station and n-th electric vehicle average overall travel speed vnIt is related,lnjIndicate that n-th electric vehicle arrives
Up to the distance of j charging stations.
Compared with prior art, the advantageous effect of the invention reached is：
(1) in layer model, the flatness of electric vehicle charging and wind-electricity integration afterload, the power generation of thermoelectricity had both been considered
Cost, it is also considered that the low-carbon of system makes the energy-saving and emission-reduction benefit of electric vehicle be fully played；Wind-powered electricity generation profit can be improved
With rate, system carbon emission and thermoelectricity cost of electricity-generating are reduced；
(2) in underlying model, it is contemplated that the benefit of user improves the enthusiasm of user with this, and user can participate in
To scheduling, keep model more rationally and effective, helps to improve user's charging satisfaction.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Specific implementation mode
The present invention propose it is a kind of cooperateed with wind-powered electricity generation based on the electric vehicle of multi-objective particle swarm algorithm charging dual-layer optimization
Dispatching method optimizes electric vehicle charging schedule using multi-objective particle swarm algorithm, improves wind power utilization, reduces carbon
Discharge reduces user's charging cost and improves user's charging satisfaction simultaneously.
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, be the flow chart of the present invention, including：It is dispatched from the electric vehicle of time angle charging upper layer
It is dispatched with from the electric vehicle of space angle charging lower layer.
Electric vehicle charging upper layer scheduling specifically comprises the following steps：
(1) when electric vehicle needs charging, the initial SOC and target SOC of electric vehicle are read out, according to reading
Breath of winning the confidence predicts electric automobile load；
(2) according to the information of load prediction, cost of electricity-generating minimum with carbon emission amount be minimum and system equivalent load variance most
Small is target, is optimized to upper layer scheduling model using particle cluster algorithm, and electric vehicle charging schedule upper layer policy is generated.
The present invention has fully considered the carbon emission amount in electric vehicle life cycle, after being compared with conventional fuel oil automobile,
The main carbon emission amount calculated during electric automobile lithium battery manufacture part and use, during electric vehicle use
Carbon emission amount depends on primary energy, needs in conjunction with practical charging scene concrete analysis, therefore in main calculating lithium battery manufacture
Carbon emission amount, and it is combined with electric vehicle charge capacity.The circular of carbon emission amount is as follows：
In formula, EhIndicate the carbon emission factor of thermoelectricity, the hop count when optimization of T tables is total；IevIndicate electric vehicle unit quantity of electricity carbon
Discharge；Ph,tIndicate the thermal power output of t periods, Pev,tFor t period electric vehicle charge powers.
Lithium battery manufacture part carbon emission amount calculates formula and is：Assuming that electric vehicle Life cycle mileage travelled is
150000km, for circulating battery number up to 10 years or more, setting electric vehicle life cycle only needed one group of battery.E150V type electricity
Motor-car battery weight is about 300kg.
Lithium battery manufactures the amount of carbon dioxide calculation formula that every degree electricity discharge is filled in part carbon emission amount reduction to electric vehicle
It is as follows：
In formula, IsumRepresent the total carbon emission amount of production lithium battery, SsumFor total kilometres, W is hundred kilometers of power consumption, ηc
Represent charge efficiency.
Cost of electricity-generating includes fired power generating unit cost of electricity-generating and carbon transaction cost, and computational methods are as follows：
In formula,Indicate the thermal power unit operation cost of t moment；Indicate the carbon transaction cost of t moment；
Wherein：The thermal power unit operation method of cost accounting is as follows：
In formula, ai、bi、ciFor the cost of electricity-generating coefficient of i-th generator；, i expression generating set serial numbers；N is generator
Sum, Pi,tIt contributes for i-th generator t moment；
Carbon transaction costComputational methods it is as follows：
In formula, K is carbon transaction price, Pc,tFor the practical carbon emission amount of t moment system, MtFor t moment carbon emission quota,ε is quota coefficient.
The computational methods of equivalent load variance are as follows：
In formula, T is hop count when optimizing total；M is to investigate time window；K indicates time window serial number；Pl,tFor t moment routine
Load, Pw,tFor t moment wind power output, Pev,tFor t period electric vehicle charge powers；Pav,iIt is equivalent for i-th of time window
The average value of load,
The scheduling of electric vehicle charging lower layer specifically comprises the following steps：
(1) according to the degree of crowding of different charging stations, the charging electricity price of timesharing subregion is calculated, it is specific as follows：
By charging, electricity price guides electric vehicle orderly to charge on Spatial Dimension, and calculation formula is：
CRTOU,t,j=Cgrid,t+(Cser+Cj)
In formula：CRTOU,i,jIndicate the timesharing subregion charging electricity price of t period jth charging stations；CserIndicate that charging station basis is filled
Electric service price；CjIndicate the service price knots modification determined by jth charging station crowding,Its
In：Ni-1,jIndicate the electric vehicle quantity of (t-1) period jth charging station；Indicate the average electricity of (t-1) period each charging station
Electrical automobile quantity；△ C indicate the service price variable quantity caused by unit crowding.
(2) according to upper layer scheduling model as a result, calculating electric vehicle quantity to be charged of each period；
(3) being charged with automobile user, expense is minimum and charging queuing time is most short for target, uses multiple target grain
Swarm optimization optimizes solution to lower layer's scheduling model, obtains optimal charging strategy.
The computational methods of automobile user charging expense are as follows：
In formula：f1Represent charging expense；T indicates hop count when optimization is total；NtIndicate t periods electric vehicle quantity to be charged；
xtnIndicate that n-th electric vehicle of t periods charges in jth charging station, xtn=j, j=1,2 ... ... Jm, Jm indicate that charging station is total
Number；
The calculation formula of charging queuing time is as follows：
tnj=twj+tcnj+tdnj
In formula：tnjIndicate n-th electric vehicle to j charging stations charging total time used；twjWhen to be lined up in station
Between, the electric vehicle quantity and the j charging station scales that are charged by this period to j charging stations determine, are approximately considered twjOnly with fill
The directly proportional k of electric vehicle numberjFor proportionality coefficient, twj=kjNi,j；Ni,jIt is the electric vehicle just to charge in j charging stations the period
Number；tcnjFor the electric vehicle charging time；tdnjThe running time that charging station j is reached for n-th electric vehicle, is arrived with electric vehicle
The distance of charging station and n-th electric vehicle average overall travel speed vnIt is related,lnjIndicate that n-th electric vehicle arrives
Up to the distance of j charging stations.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that including：Go out from time angle
It dispatches and is dispatched from the electric vehicle of space angle charging lower layer in electricity electrical automobile charging upper layer；
Electric vehicle charging upper layer scheduling specifically comprises the following steps：
(1) when electric vehicle needs charging, the initial SOC and target SOC of electric vehicle are read out, believed according to reading
Breath predicts electric automobile load；
(2) minimum with carbon emission amount, cost of electricity-generating is minimum and system equivalent load variance is minimum according to the information of load prediction
Target optimizes upper layer scheduling model using particle cluster algorithm, generates electric vehicle charging schedule upper layer policy；
The scheduling of electric vehicle charging lower layer specifically comprises the following steps：
(1) according to the degree of crowding of different charging stations, the charging electricity price of timesharing subregion is calculated；
(2) according to upper layer scheduling model as a result, calculating electric vehicle quantity to be charged of each period；
(3) being charged with automobile user, expense is minimum and charging queuing time is most short for target, uses multi-objective particle swarm
Algorithm optimizes solution to lower layer's scheduling model, obtains optimal charging strategy.
2. electric vehicle according to claim 1 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that institute
Carbon emission amount is stated mainly to consider to correspond to charging after the carbon emission amount of thermoelectricity itself and electric vehicle Life cycle are calculated
On carbon emission amount, computational methods are as follows：
In formula, EhIndicate the carbon emission factor of thermoelectricity, the hop count when optimization of T tables is total；IevIndicate electric vehicle unit quantity of electricity carbon emission；
Ph,tIndicate the thermal power output of t periods, Pev,tFor t period electric vehicle charge powers.
3. electric vehicle according to claim 2 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that electricity
Electrical automobile Life cycle calculates the carbon emission amount corresponded to later in charging：Electric automobile lithium battery manufactures part
With the carbon emission amount during use；
Wherein, lithium battery manufactures the amount of carbon dioxide calculation formula that every degree electricity discharge is filled in part carbon emission amount reduction to electric vehicle
It is as follows：
In formula, IsumRepresent the total carbon emission amount of production lithium battery, SsumFor total kilometres, W is hundred kilometers of power consumption, ηcIt represents
Charge efficiency.
4. electric vehicle according to claim 1 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that institute
It includes fired power generating unit cost of electricity-generating and carbon transaction cost to state cost of electricity-generating, and computational methods are as follows：
In formula,Indicate the thermal power unit operation cost of t moment；Indicate the carbon transaction cost of t moment；T tables optimize total period
Number；
Wherein：The thermal power unit operation method of cost accounting is as follows：
In formula, ai、bi、ciFor the cost of electricity-generating coefficient of i-th generator；I indicates generating set serial number；N is generator sum,
Pi,tIt contributes for i-th generator t moment；
Carbon transaction costComputational methods it is as follows：
In formula, K is carbon transaction price, Pc,tFor the practical carbon emission amount of t moment system, MtFor t moment carbon emission quota,ε is quota coefficient.
5. electric vehicle according to claim 1 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that institute
The computational methods for stating equivalent load variance are as follows：
In formula, T is hop count when optimizing total；M is to investigate time window；K indicates time window serial number；Pl,tIt is routinely negative for t moment
Lotus, Pw,tFor t moment wind power output, Pev,tFor t period electric vehicle charge powers；Pav,kFor i-th of time window equivalent negative
The average value of lotus,
6. electric vehicle according to claim 1 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that meter
The method of the charging electricity price of subregion is as follows when point counting：
By charging, electricity price guides electric vehicle orderly to charge on Spatial Dimension, and calculation formula is：
CRTOU,t,j=Cgrid,t+(Cser+Cj)
In formula：CRTOU,i,jIndicate the timesharing subregion charging electricity price of t period jth charging stations；CserIndicate charging station basis charging clothes
Business price；CjIndicate the service price knots modification determined by jth charging station crowding,Wherein：
Ni-1,jIndicate the electric vehicle quantity of (t-1) period jth charging station；Indicate that (t-1) period each charging station is averaged electronic vapour
Vehicle quantity；△ C indicate the service price variable quantity caused by unit crowding.
7. electric vehicle according to claim 6 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that electricity
The computational methods of electrical automobile user charging expense are as follows：
In formula：f1Represent charging expense；T indicates hop count when optimization is total；NtIndicate t periods electric vehicle quantity to be charged；xtnTable
Show that n-th electric vehicle of t periods charges in jth charging station, xtn=j, j=1,2 ... ... Jm, Jm indicate charging station sum；
Pev,tFor t period electric vehicle charge powers.
8. electric vehicle according to claim 1 cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation, which is characterized in that fill
The calculation formula of electric queuing time is as follows：
tnj=twj+tcnj+tdnj
In formula：tnjIndicate n-th electric vehicle to j charging stations charging total time used；twjFor queuing time in station, by this
Period determines to the electric vehicle quantity that j charging stations charge with j charging station scales, is approximately considered twjOnly with charging vehicle number
Directly proportional kjFor proportionality coefficient, twj=kjNi,j；Ni,jIt is the electric vehicle number just to charge in j charging stations the period；tcnjFor electricity
The electrical automobile charging time；tdnjThe running time of charging station j, the road with electric vehicle to charging station are reached for n-th electric vehicle
Journey and n-th electric vehicle average overall travel speed vnIt is related,lnjIndicate that n-th electric vehicle reaches j charging stations
Distance.
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| CN201810399869.6A CN108320064A (en) | 2018-04-28 | 2018-04-28 | A kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation |
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| CN201810399869.6A CN108320064A (en) | 2018-04-28 | 2018-04-28 | A kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation |
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| CN113450023A (en) * | 2021-07-28 | 2021-09-28 | 东北电力大学 | Electric automobile ordered charging method under gridding time-of-use electricity price |
| CN114037177A (en) * | 2021-11-22 | 2022-02-11 | 山东德佑电气股份有限公司 | Bus charging load optimization method in crowded traffic state based on train number chain |
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| CN114266445A (en) * | 2021-12-02 | 2022-04-01 | 国网浙江省电力有限公司 | Coordinated planning method for distributed power supply and electric vehicle charging station |
| CN114285063A (en) * | 2022-03-07 | 2022-04-05 | 河北工业大学 | Short-term carbon emission factor-based intelligent electric vehicle carbon-reducing charging method |
| CN114285063B (en) * | 2022-03-07 | 2022-05-20 | 河北工业大学 | Short-term carbon emission factor-based intelligent electric vehicle carbon-reducing charging method |
| WO2024060539A1 (en) * | 2022-09-23 | 2024-03-28 | 广东邦普循环科技有限公司 | Annual carbon emission amount estimation method and device for power battery |
| CN117081059A (en) * | 2023-08-24 | 2023-11-17 | 国网北京市电力公司 | Optimal control method, device, equipment and medium for charging and replacing power station cluster |
| CN117081059B (en) * | 2023-08-24 | 2024-06-11 | 国网北京市电力公司 | Optimal control method, device, equipment and medium for charging and replacing power station cluster |
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