CN105720597B - For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage - Google Patents
For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage Download PDFInfo
- Publication number
- CN105720597B CN105720597B CN201410735513.7A CN201410735513A CN105720597B CN 105720597 B CN105720597 B CN 105720597B CN 201410735513 A CN201410735513 A CN 201410735513A CN 105720597 B CN105720597 B CN 105720597B
- Authority
- CN
- China
- Prior art keywords
- mrow
- power station
- msubsup
- power
- integrated power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, pass through particle algorithm optimization integration power station and power grid interaction power, the peak-valley difference of network load can be significantly reduced, integrated power station participates in the ability of power grid assistant service and power station energy storage system capacity is positively correlated relation, i.e. capacity is bigger, the ability that integrated power station participates in power grid assistant service is stronger, the effect of peak load shifting is more notable, and the present invention can also further reduce the battery loss cost for filling the integrated power station of storage.
Description
Technical field
The present invention relates to a kind of electric vehicle and power grid interaction technique field, particularly a kind of integrated power station of optimization and electricity
The method of the interactive power of net.
Background technology
Electric vehicle is the research hotspot of auto industry, has become national governments, energy manufacturer and automobile vendor at present
Focus, as the U.S. EV Project plan and China " ten thousand, cities " plan.It is anticipated that future is with battery
The continuous progress of technology, petroleum resources are petered out, and electric vehicle will be popularized gradually.As following electric vehicle is large-scale
Popularization, accesses grid charging on a large scale, due to the randomness and dispersiveness of charging load, will to grid generation, transmit electricity, match somebody with somebody
Electricity, electricity consumption, scheduling etc. bring very important influence.Such as cause new load peak, increase network loss, some node voltages are horizontal
Reduction etc..Therefore, how by rationally controlling the charging behavior of electric vehicle, reduce scale electric vehicle and charge to power grid
The adverse effect brought becomes major issue urgently to be resolved hurrily.
The research of electric vehicle and power grid interaction technique at present is stored up electric vehicle as the distribution in power grid
Energy unit, the effect of power grid frequency modulation, peak regulation is participated according to dispatch command.Part has studied battery to power grid centrally connected power supply
(Battery to grid, B2G) pattern.B2G patterns open new approaches for the development ＆ construction of electric automobile charging station, that is, build
If fill the integrated power station of storage.However, to filling in the correlative study in the integrated power station of storage, relevant optimization method is often only
Consider that power station provides the assessment of the assistant services ability such as peak-frequency regulation for power grid, have ignored the frequent charge and discharge of battery to battery life
Influence.In fact, since current battery is expensive, battery cost accounts for that power station cost proportion is larger, and frequent charge and discharge certainly will
Battery loss can be increased, make shorter battery life, and then influence to fill storage power station economy operation, increase the operation in power station into
This, while influence the effect of power grid assistant service.Therefore, it is unfavorable to bring to evade electric vehicle scale application to power grid
It influences, realizes the economy operation in power station, it is ensured that the effect of power grid assistant service, it is necessary to considering that battery loss cost is filled
The integrated power station running optimizatin method of storage optimizes research.
The content of the invention
It is an object of the invention to provide a kind of the integrated power station charge-discharge electric power of storage is filled for optimizing electric vehicle
Method, it can not only stabilize the load fluctuation of power grid well, reduce network load peak-valley difference, while can also efficiently reduce electricity
Electrical automobile fills the cost depletions of storage power station battery.
It realizes, is as follows the purpose of the present invention is technical solution in this way：
1) object function of the integrated power station using power grid peak-valley difference and battery loss cost minimization as optimization aim is determined；
2) constraints of the charge-discharge electric power in the integrated power station of structure, battery capacity and spare capacity；
3) object function described in step 1) and the constraints described in step 2) are combined, structure optimizes mathematical modulo
Type, and seek the mathematical model of optimization using particle cluster algorithm obtains interactive optimal of each period integration power station and power grid
Charge-discharge electric power；
Using power grid peak-valley difference and battery loss cost minimization as the calculating of the object function of optimization aim described in step 1)
Method is：
Plx(t)=Ply(t)+Plc(t)+P(t)
F=min [α (maxPlx(t)-minPlx(t))+βC]
In formula, Ply(t) it is the daily load of regional power grid, Plx(t) load for power grid after integrated power station is adjusted, C are
Integrated battery loss cost, rdFor the battery loss rate converted by unit discharge power, 0.4 is taken, α, β are respectively load song
The weight of line peak-valley difference and battery loss cost shared by optimization aim, and alpha+beta=1, P (t) being integrated power station and power grid
Interactive charge-discharge electric power, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
Further, the constraints described in step 2) is：
2-1) integrated power station and the constraints of the charge-discharge electric power P (t) of power grid interaction are：
-Pmax≤P(t)≤Pmax
In formula, PmaxRepresent the maximum power that power grid charges to integrated power station ,-PmaxRepresent that integrated power station is put to power grid
The maximum power of electricity；
2-2) the battery capacity constraints in integrated power station is：
kQmax≤Q(t)≤Qmax
In formula, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-3) constraints of spare capacity is：
Qb=kQmax
In formula, QbFor spare capacity, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-4) integrated power station and the charge-discharge electric power P (t) and battery capacity Q (t) in integrated power station of power grid interaction are full
Sufficient the following formula：
Δ Q=P (t) × t
Q (t+1)=Q (t)+Δ Q-Plc(t)
In formula, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
Further, spare capacity coefficient k value is 20%.
Further, the specific method for seeking mathematical model of optimization using particle cluster algorithm described in step 3) is：
Known conditions and algorithm basic parameter 3-1) are inputted, initializes position and the speed of particle；Known conditions includes area
The charging and conversion electric load of power distribution network daily load and electric vehicle in domain；
Particle position 3-2) is changed according to constraints；
The fitness of population, i.e. object function 3-3) are calculated, records particle individual desired positions and group's desired positions；
3-4) according to the particle rapidity of particle cluster algorithm and location update formula update particle rapidity and position；
3-5) check whether particle position is more than limit value, is then return to step 3-2), on the contrary then be transferred to step 3-6)；
The fitness of population 3-6) is calculated, updates and records particle optimal location and group most optimal location；
3-7) judge whether to reach default termination condition, be to stop calculating, and export particle position at this time, i.e., it is integrated
Power station and the charge-discharge electric power P (t), on the contrary then return to step 3-4 of power grid interaction).
Further, step 3-4) described in particle rapidity and location update formula be specially：
In formula, vId kIt is that v is the particle I speed that d is tieed up in kth time iteration；ω is inertia weight；c1、c2Be study because
Son empirically often takes c1=c2=2；rand1 k,rand2 kIt is the random number between [0,1]；xId kIt is particle I in kth time iteration
In d tie up position；pbestId kIt is the coordinate that particle I individuals extreme point is tieed up in d；gbestId kIt is entire group's global extremum
O'clock d dimension coordinate.
By adopting the above-described technical solution, the present invention has the advantage that：
The present invention can significantly reduce the peak of network load by particle algorithm optimization integration power station and power grid interaction power
Paddy is poor, and integrated power station participates in the ability of power grid assistant service and power station energy storage system capacity is positively correlated relation, i.e. capacity is got over
Greatly, integrated power station participate in power grid assistant service ability it is stronger, the effect of peak load shifting is more notable, and the present invention can also be into
The battery loss cost in the integrated power station of storage is filled in the reduction of one step.
Other advantages, target and the feature of the present invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.The target and other advantages of the present invention can be wanted by following specification and right
Book is sought to realize and obtain.
Description of the drawings
The description of the drawings of the present invention is as follows.
Fig. 1 is the flow diagram of the present invention；
Fig. 2 is a certain regional distribution network daily load curve figure；
Fig. 3 is the day charging and conversion electric load chart of electric vehicle in region；
Fig. 4 is power distribution network daily load curve figure before and after optimization；
Fig. 5 is station capacity-power grid peak-valley difference slip graph；
Fig. 6 is power station cost depletions figure under different capabilities.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
It is a kind of for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, as shown in Figure 1, including with
Lower step：
1) object function of the integrated power station using power grid peak-valley difference and battery loss cost minimization as optimization aim is determined；
2) constraints of the charge-discharge electric power in the integrated power station of structure, battery capacity and spare capacity；
3) object function described in step 1) and the constraints described in step 2) are combined, structure optimizes mathematical modulo
Type, and seek the mathematical model of optimization using particle cluster algorithm obtains interactive optimal of each period integration power station and power grid
Charge-discharge electric power.
Using power grid peak-valley difference and battery loss cost minimization as the calculating of the object function of optimization aim described in step 1)
Method is
Plx(t)=Ply(t)+Plc(t)+P(t)
F=min [α (maxPlx(t)-minPlx(t))+βC]
In formula, Ply(t) it is the daily load of regional power grid, Plx(t) load for power grid after integrated power station is adjusted, C are
Integrated battery loss cost, rdFor the battery loss rate converted by unit discharge power, 0.4 is taken, α, β are respectively load song
The weight of line peak-valley difference and battery loss cost shared by optimization aim, and alpha+beta=1, P (t) being integrated power station and power grid
Interactive charge-discharge electric power, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
Constraints described in step 2) is：
2-1) integrated power station and the constraints of the charge-discharge electric power P (t) of power grid interaction are：
-Pmax≤P(t)≤Pmax
In formula, PmaxRepresent the maximum power that power grid charges to integrated power station ,-PmaxRepresent that integrated power station is put to power grid
The maximum power of electricity；
2-2) the battery capacity constraints in integrated power station is：
kQmax≤Q(t)≤Qmax
In formula, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-3) constraints of spare capacity is：
Qb=kQmax
In formula, QbFor spare capacity, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-4) integrated power station and the charge-discharge electric power P (t) and battery capacity Q (t) in integrated power station of power grid interaction are full
Sufficient the following formula：
Δ Q=P (t) × t
Q (t+1)=Q (t)+Δ Q-Plc(t)
In formula, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
The specific method for seeking mathematical model of optimization using particle cluster algorithm described in step 3) is：
Known conditions and algorithm basic parameter 3-1) are inputted, initializes position and the speed of particle；Known conditions includes area
The charging and conversion electric load of power distribution network daily load and electric vehicle in domain；
Particle position 3-2) is changed according to constraints；
The fitness of population, i.e. object function 3-3) are calculated, records particle individual desired positions and group's desired positions；
3-4) according to the particle rapidity of particle cluster algorithm and location update formula update particle rapidity and position；
3-5) check whether particle position is more than limit value, is then return to step 3-2), on the contrary then be transferred to step 3-6)；
The fitness of population 3-6) is calculated, updates and records particle optimal location and group most optimal location；
3-7) judge whether to reach default termination condition, be to stop calculating, and export particle position at this time, i.e., it is integrated
Power station and the charge-discharge electric power P (t), on the contrary then return to step 3-4 of power grid interaction).
Further, step 3-4) described in particle rapidity and location update formula be specially：
In formula, vId kIt is that v is the particle I speed that d is tieed up in kth time iteration；ω is inertia weight；c1、c2Be study because
Son empirically often takes c1=c2=2；rand1 k,rand2 kIt is the random number between [0,1]；xId kIt is particle I in kth time iteration
In d tie up position；pbestId kIt is the coordinate that particle I individuals extreme point is tieed up in d；gbestId kIt is entire group's global extremum
O'clock d dimension coordinate.
By taking the daily load of somewhere power distribution network as an example, as shown in Figure 2.A maximum energy-storage battery capacity has been accessed in region
The integrated power station of storage is filled for 8MWh, the day charging and conversion electric load of electric vehicle is as shown in Figure 3.
It can be obtained according to Fig. 2 and Fig. 3, the peak load period of the power grid daily load of this area is 11:00-15:00, the paddy load period
For 04:00-07:00；And the charging and conversion electric peak load period 10 of electric vehicle:00-14:00, the paddy load period is 03:00-06:00,
The two variation tendency is substantially uniform.Such variation tendency can cause peak load to be overlapped, and increase power grid peak-valley difference, go out
Existing " on peak plus peak " phenomenon, reduces load rate of grid so that network load fluctuation further increases, and it is steady to be unfavorable for power grid security
Fixed operation.
Daily load is superimposed for power distribution network daily load with electric vehicle day charging and conversion electric load before optimizing in region.Fig. 4 is shown
After optimization method, the contrast curve with power distribution network daily load before optimization.As shown in Figure 4, regional power grid considers electronic
The period that peak load occurs in daily load after automobile charging and conversion electric load is about 12:00-14:00, the period of paddy load is about
05:00-07:00.In addition, compared with not considering the optimization method in power station, optimization method using the present invention formulates integration electricity
It stands after charge and discharge plan, power grid daily load is significantly improved, and is reduced power grid peak load, is increased power grid paddy load, can have
Effect evades scale electric vehicle and charges the load fluctuation brought to power grid, stabilizes power grid peak-valley difference.
In order to which without loss of generality, the present invention is respectively also 4MWh, 5MWh, 5.5MWh, 6MWh, 7MWh to energy-storage battery capacity
The integrated power station of different capabilities is waited to be emulated, further studies the variation of integrated power station energy-storage battery capacity to excellent
Change the influence of Policy Result.Fig. 5 is relation of the network load peak-valley difference slip with integrated station capacity.
From figure 5 it can be seen that when stored energy capacitance is 4MWh, 5MWh, 5.5MWh, 6MWh, 7MWh, 8MWh, power grid peak valley
Poor slip is respectively：5.4%th, 9.4%, 10.5%, 13.1%, 15.1%.Network load peak-valley difference slip changes with filling
The variation positive correlation of the integrated power station energy storage system capacity of storage, gradually increases with the increase of capacity, when capacity is 4MW,
Network load peak-valley difference slip is minimum, is 5.4%, when capacity is 8MW, network load peak-valley difference slip is up to
15.1%, during remaining capacity, then fall between.But this positive correlation is not linear relationship, but is started to increase comparatively fast, after
Face increase is slower, increases when being specially 4MW~6MW than very fast, and the amplitude of growth is larger, and 6MW~8MW increases are slower, growth
Amplitude is smaller.This may be primarily due to that integrated power station energy storage system capacity is bigger, and it is stronger to participate in the ability that power grid is adjusted,
Battery discharge capacity increases so that its cost depletions increases, and optimization method is its cost depletions of reduction, it has to take the side of compromise
Method has slowed down the increasing degree of peak load shifting ability.
Fig. 6 optimization methods of the present invention and do not count and the optimization method of battery loss under energy-storage battery cost depletions and energy storage electricity
The comparison of tankage.It can be seen that under optimization method of the present invention, integrated power station energy-storage battery capacity is 4MWh, 5MWh,
When 6MWh, 7MWh, 8MWh, battery loss cost is respectively 1053 yuan, 1184 yuan, 1464 yuan, 2012 yuan, 5150 yuan.And not
Under meter and battery loss Cost Optimization Approach, battery loss cost is respectively 1489 yuan, 1681 yuan, 1976 yuan, 2768 yuan, 7004
Member.Battery loss cost increases as integrated power station energy-storage battery capacity increases.This is primarily due to integrated electricity
It stands the increase of energy-storage battery capacity, required battery necessarily increases, then in the case where single battery cost depletions are certain, always
Battery loss cost can also increase.In addition, the battery loss cost under optimization method of the present invention is significantly lower than and takes no account of battery
Cost depletions optimization method, using optimization method proposed by the present invention, can stabilize as far as possible load fluctuation, peak load shifting it is same
When, moreover it is possible to integrated power station battery loss cost is effectively reduced, significantly improves and fills the integrated power station performance driving economy of storage, when not
Carry out the popularization with electric vehicle, when filling the quantity in the integrated power station of storage and capacity substantial increase, reduce battery loss cost
It will be considerable to economic benefit caused by integrated power station.
It can be seen that the optimization that the integrated power station operation of storage is filled using a kind of electric vehicle of the present invention from result above
Control method can significantly reduce network load peak-valley difference, and the assistant services such as peak load shifting are provided for power grid.Integrated power station participates in
The ability of power grid assistant service is positively correlated relation with power station energy storage system capacity, i.e. capacity is bigger, and integrated power station participates in electricity
The ability of net assistant service is stronger, and peak load shifting effect is more notable.With do not count and the optimization method of battery loss cost compared with,
Optimization method of the present invention can also further reduce the battery damage for filling the integrated power station of storage in the case where reducing network load peak-valley difference
Cost is consumed, is a kind of economic, reliable optimal control method.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with
The present invention is described in detail in good embodiment, it will be understood by those of ordinary skill in the art that, it can be to the skill of the present invention
Art scheme is modified or replaced equivalently, and without departing from the objective and scope of the technical program, should all be covered in the present invention
Right among.
Claims (5)
1. for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, which is characterized in that specific steps are such as
Under：
1) object function of the integrated power station using power grid peak-valley difference and battery loss cost minimization as optimization aim is determined；
2) constraints of the charge-discharge electric power in the integrated power station of structure, battery capacity and spare capacity；
3) object function described in step 1) and the constraints described in step 2) are combined, builds mathematical model of optimization, and
The mathematical model of optimization is sought using particle cluster algorithm, obtains the optimal charge and discharge in each period integration power station and power grid interaction
Power；
Using power grid peak-valley difference and battery loss cost minimization as the computational methods of the object function of optimization aim described in step 1)
For：
Plx(t)=Ply(t)+Plc(t)+P(t)
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>C</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<msub>
<mi>r</mi>
<mi>d</mi>
</msub>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>P</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
F=min [α (maxPlx(t)-minPlx(t))+βC]
In formula, Ply(t) it is the daily load of regional power grid, Plx(t) load for power grid after integrated power station is adjusted, C are integrated
Electrochemical cell cost depletions, rdFor the battery loss rate converted by unit discharge power, 0.4 is taken, α, β are respectively load curve peak
The weight of paddy difference and battery loss cost shared by optimization aim, and alpha+beta=1, P (t) being integrated power station and power grid are interactive
Charge-discharge electric power, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
2. it is as described in claim 1 for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, it is special
Sign is that the constraints described in step 2) is：
2-1) integrated power station and the constraints of the charge-discharge electric power P (t) of power grid interaction are：
-Pmax≤P(t)≤Pmax
In formula, PmaxRepresent the maximum power that power grid charges to integrated power station ,-PmaxRepresent what integrated power station was discharged to power grid
Maximum power；
2-2) the battery capacity constraints in integrated power station is：
kQmax≤Q(t)≤Qmax
In formula, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-3) constraints of spare capacity is：
Qb=kQmax
In formula, QbFor spare capacity, QmaxThe maximum stored energy capacitance in being integrated power station, k are spare capacity coefficient；
2-4) integrated power station with the charge-discharge electric power P (t) of power grid interaction and in integrated power station battery capacity Q (t) satisfactions with
Lower formula：
Δ Q=P (t) × t
Q (t+1)=Q (t)+Δ Q-Plc(t)
In formula, Plc(t) the day charging and conversion electric load of electric vehicle in region is represented.
3. it is as claimed in claim 2 for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, it is special
Sign is：Spare capacity coefficient k value is 20%.
4. it is as claimed in claim 2 for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, it is special
Sign is that the specific method for seeking mathematical model of optimization using particle cluster algorithm described in step 3) is：
Known conditions and algorithm basic parameter 3-1) are inputted, initializes position and the speed of particle；Known conditions is included in region
The charging and conversion electric load of power distribution network daily load and electric vehicle；
Particle position 3-2) is changed according to constraints；
The fitness of population, i.e. object function 3-3) are calculated, records particle individual desired positions and group's desired positions；
3-4) according to the particle rapidity of particle cluster algorithm and location update formula update particle rapidity and position；
3-5) check whether particle position is more than limit value, is then return to step 3-2), on the contrary then be transferred to step 3-6)；
The fitness of population 3-6) is calculated, updates and records particle optimal location and group most optimal location；
3-7) judge whether to reach default termination condition, be to stop calculating, and export particle position at this time, i.e., integrated power station
With the charge-discharge electric power P (t), on the contrary then return to step 3-4 of power grid interaction).
5. it is as claimed in claim 4 for optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage, it is special
Sign is, step 3-4) described in particle rapidity and location update formula be specially：
<mrow>
<msubsup>
<mi>v</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>&omega;v</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<msubsup>
<mi>rand</mi>
<mn>1</mn>
<mi>k</mi>
</msubsup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>pbest</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<msubsup>
<mi>rand</mi>
<mn>2</mn>
<mi>k</mi>
</msubsup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>gbest</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>I</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
</mrow>
In formula, vId kIt is that v is the particle I speed that d is tieed up in kth time iteration；ω is inertia weight；c1、c2It is Studying factors, presses
C is often taken according to experience1=c2=2；rand1 k,rand2 kIt is the random number between [0,1]；xId kIt is particle I d in kth time iteration
The position of dimension；pbestId kIt is the coordinate that particle I individuals extreme point is tieed up in d；gbestId kIt is that entire group's global extremum point exists
The coordinate of d dimensions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410735513.7A CN105720597B (en) | 2014-12-05 | 2014-12-05 | For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410735513.7A CN105720597B (en) | 2014-12-05 | 2014-12-05 | For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105720597A CN105720597A (en) | 2016-06-29 |
| CN105720597B true CN105720597B (en) | 2018-05-22 |
Family
ID=56143787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410735513.7A Active CN105720597B (en) | 2014-12-05 | 2014-12-05 | For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105720597B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106299511B (en) * | 2016-08-02 | 2019-02-26 | 万马联合新能源投资有限公司 | Electric automobile charging station energy storage capacity optimization method |
| CN108964021B (en) * | 2018-06-25 | 2022-07-01 | 国网陕西省电力公司经济技术研究院 | Method for controlling capacity space-time distribution characteristic of frequency-modulated electric vehicle capable of accessing network |
| CN110281809B (en) * | 2019-05-28 | 2021-06-08 | 北京航盛新能科技有限公司 | Electric automobile V2G control system based on particle swarm optimization algorithm |
| CN111845453B (en) * | 2020-07-10 | 2024-01-30 | 国网天津市电力公司 | Electric vehicle charging station double-layer optimized charging and discharging strategy considering flexible control |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580215A (en) * | 2013-09-07 | 2014-02-12 | 国家电网公司 | Economy analysis method for electric vehicles to provide auxiliary services |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101108529B1 (en) * | 2010-01-25 | 2012-01-30 | 서울과학기술대학교 산학협력단 | Method of determining distributed generation terminal voltage in three phase unbalance electrification system and apparatus for the same |
| JP2012043206A (en) * | 2010-08-19 | 2012-03-01 | Kansai Electric Power Co Inc:The | Control device and control method |
-
2014
- 2014-12-05 CN CN201410735513.7A patent/CN105720597B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580215A (en) * | 2013-09-07 | 2014-02-12 | 国家电网公司 | Economy analysis method for electric vehicles to provide auxiliary services |
Non-Patent Citations (1)
| Title |
|---|
| 电动汽车换电站有序充电调度策略研究;田文奇等;《电力系统保护与控制》;20121101;第40卷(第21期);第114-118页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105720597A (en) | 2016-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Meng et al. | Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| Li et al. | Analysis of a new design of the hybrid energy storage system used in the residential m-CHP systems | |
| CN105024432B (en) | A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price | |
| CN103311942B (en) | Control method of battery energy storage system for peak clipping and valley filling in distribution network | |
| CN104966127B (en) | A kind of electric vehicle economic load dispatching method based on demand response | |
| Luo et al. | Optimal coordination of plug-in electric vehicles in power grids with cost-benefit analysis—Part I: Enabling techniques | |
| CN107994595B (en) | Peak clipping and valley filling control method and system applying control method | |
| CN102647005B (en) | Method for orderly charging electric automobile | |
| CN106230020B (en) | The electric vehicle interactive response control method of distributed generation resource consumption is considered under a kind of micro-capacitance sensor | |
| CN109217290B (en) | Microgrid energy optimization management method considering electric vehicle charging and discharging | |
| CN102361327B (en) | Battery energy storage system peaking cutting and valley filling method with consideration of battery service life | |
| CN103248064A (en) | Composite energy charging energy storage system and method thereof | |
| CN104348256A (en) | Multi-type battery energy storage power station energy management method taking charge-discharge rate into account | |
| CN105720597B (en) | For optimizing the method that electric vehicle fills the integrated power station charge-discharge electric power of storage | |
| CN111064214A (en) | Power distribution network optimal scheduling method based on electric vehicle two-stage rolling strategy | |
| CN110277781A (en) | The Economic Dispatch method of garden power grid is filled in a kind of storage containing light | |
| CN103904749B (en) | A kind ofly consider the orderly charge control method of the electric automobile of wind power output fluctuation | |
| CN102738879B (en) | Electric vehicle intelligent charger capable of automatically responding to tou price | |
| CN110289622A (en) | The economic optimization dispatching method a few days ago of energy router is filled in a kind of light storage | |
| CN109742755B (en) | Node partition-based active power distribution network operation scheduling method with electric automobile | |
| CN110415016A (en) | A kind of charging pricing practice strategy based on optimization charge and discharge strategy | |
| CN111489009B (en) | Optimization calculation method and device for operation mode of electric vehicle charging station | |
| Chun et al. | Unit commitment considering coordinated dispatch of large scale electric vehicles and wind power generation | |
| CN103580051B (en) | Consider the wind storage system battery charge state Optimal Control System of part throttle characteristics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |