CN107590580A - The appraisal procedure of residential block charging electric vehicle negative rules under tou power price - Google Patents
The appraisal procedure of residential block charging electric vehicle negative rules under tou power price Download PDFInfo
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Abstract
The invention discloses a kind of appraisal procedure of residential block charging electric vehicle negative rules under tou power price, trip statistical property based on electric automobile, the charging electric vehicle load dynamic probability model of price elasticity analysis is built, the uncertainty of response relation between car owner and tou power price is characterized with probability nature；The fluctuation of load dynamic probability is estimated by comentropy and out-of-limit probability, and then assesses the uncertainty of residential block charging electric vehicle load fluctuation degree and the overload risk of cell distribution transformer.
Description
Technical field
The present invention relates to a kind of appraisal procedure of residential block charging electric vehicle negative rules under tou power price, belong to
Electric automobile Demand Side Response field, electric power system dispatching field.
Background technology
Development of EV is to reduce greenhouse gas emission, reduce the important means relied on fossil energy.Therefore, it is electronic
Automobile by be future automobile field development trend.With the increasingly increasing of electric automobile access quantity in the distribution system of residential block
Add, its unordered charging can cause the peak-valley difference of power network to increase, and distribution transformer is chronically at the deleterious situations such as overlond running.It is based on
This, the bootable electric automobile of tou power price is charged in order, so as to effectively reduce the peak-valley difference of power network, transformer long-time mistake
Carrying row etc. endangers, but the charging electric vehicle load under tou power price guiding has certain fluctuation uncertainty, and car
There is also certain uncertainty, these uncertainties can aggravate residential block load fluctuation for the main responsiveness between tou power price
Randomness, increase cell distribution transformer overload risk, certain harm is brought to the safe and stable operation of distribution system.
The appraisal procedure of residential block charging electric vehicle negative rules, can consider residential block under tou power price
The uncertainty of response relation and distribution become between the uncertainty of charging electric vehicle load fluctuation, car owner and tou power price
The risk of depressor overlond running, the trip statistical property based on electric automobile, the charging electric vehicle of structure price elasticity analysis
Load dynamic probability model, the uncertainty of response relation between car owner and tou power price is characterized with probability nature；Pass through information
Entropy and out-of-limit probability are estimated the fluctuation of load dynamic probability, and then assess residential block charging electric vehicle load fluctuation degree
Uncertainty and cell distribution transformer overload risk, provide one for electric automobile agent and formulate electronic vapour comprehensively
Consider probabilistic appraisal procedure in car charging tou power price scheme.
At present, consider that uncertainty during electric automobile access residential block charging does not consider charging electric vehicle and born
The uncertainty of lotus fluctuation and influence of the user response tou power price uncertainty to scheduling strategy, under also not guiding electricity price
Systematic uncertainty estimated, and then assess influence of the pricing strategy to residential block distribution system.
The content of the invention
It is an object of the invention to provide a kind of assessment of residential block charging electric vehicle negative rules under tou power price
Method, the trip statistical property based on electric automobile, the charging electric vehicle load dynamic probability mould of structure price elasticity analysis
Type, the uncertainty of response relation between car owner and tou power price is characterized with probability nature；Pass through comentropy and out-of-limit probability pair
Load dynamic probability fluctuation is estimated, and then assess residential block charging electric vehicle load fluctuation degree uncertainty and
The overload risk of cell distribution transformer.Comprise the following steps：
1st, under a kind of tou power price residential block charging electric vehicle negative rules appraisal procedure, mainly comprising following
Step：
A, residential block charging electric vehicle tou power price λ is inputtedi(i=1,2 ... 24), electric automobile starting trip moment TS、
Finally go on a journey finish time TE, daily travel l statistical distribution.
B, by the daily travel l of electric automobile, solved by formula (1) and obtain the charging duration T of electric automobileC：
Wherein：P be electric automobile charge power, kW；U is hundred kilometers of power consumption, kWh/100km；η is imitated for charging
Rate,For the computing that rounds up, 1≤TC≤24。
C, by Latin Hypercube Sampling technology, sampling scale is set as V, and trip moment, charging are originated to electric automobile
Duration, finally trip finish time is sampled, and obtains sampling matrix E=[TS,TE,TC]T, wherein TS、TEAnd TCRespectively 1 ×
V dimension starting trip moment vector, finally go on a journey finish time vector, charging when long vector, each row form electric automobile in E
One charging scenarios.
D, hop count d when single electric automobile of calculating is corresponding chargeable under charging scenarios vvFor：
Wherein：TS,v、TE,v、TC,vRespectively TS、TE、TCV column elements in vector, represent electric automobile in charging scenarios v
In starting trip the moment, finally go on a journey finish time, charging duration.
E, under charging scenarios v, by TS,v、TE,v、TC,vEstablish the charging electric vehicle load mould based on price elasticity analysis
Type.
F, solved by Cumulants method counted and during electric automobile residential block load power each rank central moment, utilize
Gram-Charlier infinite series expansions ask for the probability-distribution function of residential block charging electric vehicle load, solve each
The entropy measure of moment residential block charging electric vehicle load and the out-of-limit probability of distribution transformer, and then can show that residential block is electronic
The uncertainty of automobile charging load fluctuation degree and the assessment models of distribution transformer overload risk.
Further, the charging electric vehicle load model based on price elasticity analysis is established in the step E, by resident
Starting trip moment T of area's electric automobile under charging scenarios vS,v, finally go on a journey finish time TE,v, charging duration TC,vAnd
Price elastic coefficient ε establishes comprising the following steps that for charging electric vehicle load model：
E1, last trip finish time T by electric automobile under charging scenarios vE,v, single electric automobile can be solved and existed
Corresponding initiation of charge moment T under charging scenarios vQ,v：
TQ,v∈(TE,v,TE,v+dv) (3)
Wherein：dvSolved and obtained by formula (2).
E2, single electric automobile the initiation of charge moment under charging scenarios v are TQ,vWhen charging scheme be：
Wherein：N is possible initiation of charge moment T in charging scenarios vQ,vNumber；Exist for electric automobile
The charge power at j moment under charging scenarios v, its value are：
E3, single electric automobile charge power corresponding to the j moment under charging scenarios v single order central moment (expectation) Cvj,1
For：
K (k >=2) rank central moment Cvj,k：
Wherein：pvjFor single electric automobile, the initiation of charge moment is the probability at j moment under charging scenarios v, is represented electronic
Automotive vehicle owner and the response relation of charging electricity price, in the costly period of charging, car owner's responsiveness is relatively low, when charging expense is relatively low
Section, car owner have higher responsiveness；
E4、pvjThe uncertainty of electric automobile main response tou power price is reacted, its solution formula is：
Wherein：ε is price elastic coefficient；λrFor the charging tou power price corresponding to the electric automobile of r moment residential blocks, member/
kW·h。
Further, the entropy measure and distribution that each moment residential block charging electric vehicle load is solved in the step F become
The out-of-limit probability of depressor, and then probabilistic assessment models of residential block charging electric vehicle load fluctuation degree, small can be drawn
The assessment models of area's distribution transformer overload risk.Comprise the following steps that：
F1, electric automobile recoverable amount is set in residential block as M, and different charging electric vehicle behaviors is separate,
As central-limit theorem can obtain single electric automobile at the j moment corresponding to charge power k rank central moments Lk,jFor：
Wherein：Cvj,kSolved and obtained by formula (7).
F2, it can be generally thought each moment residential block total load and include residential block conventional load and charging electric vehicle load,
Set resident's conventional load and obey desired value as μ normal distribution, standard deviation δ is 0.1 μ, then residential block conventional load three ranks
And its multistage cumulant very little can be neglected.Therefore, the single order of j moment residential blocks conventional load, second order cumulant are：
Wherein：Respectively desired value and variance of the residential block conventional load at the j moment.
F3, single electric automobile j moment charge powers in charging scenarios v k (k=1,2,3 ...) rank cumulant Dk,j
For：
Wherein：Lk,jIt is electric automobile in k rank central moments corresponding to j moment charge powers, is solved and obtained by formula (9).
F4, the property according to cumulant：The k rank cumulant of separate stochastic variable meets homogeneity with that can add
Property.Therefore, k rank cumulant B of the total load power in residential block at the j momentk,jFor：
Wherein：M be residential block in electric automobile recoverable amount, Dk,jSolved and obtained by formula (11).
F5, residential block total load power can be solved according to each rank cumulant of each moment total load power in residential block exist
Each rank central moment at each moment, then deploy solution by Gram-Charlier infinite series and obtain each moment residential block total load
The probability-distribution function F (x) of the power and probability density function f (x) of charging electric vehicle load.
F6, when residential block distribution transformer overload degree reach 60% when, transformer short-time overload allow the time be
0.75h, therefore, the assessment models X of residential block distribution transformer overload risk is：
X≤0.75 (14)
Wherein：Y is born to overload the limit value of load, kW by distribution transformer in residential block；Fj(Y) it is j moment residential blocks
Distribution function value of the internal loading power at transformer overload 60%；Fj(Y) by cumulant and Gram-Charlier Infinite Orders
Number expansion is asked for.
F7, the f by the j momentj(x) can solve to obtain the comentropy H of j moment charging electric vehicle loadsj；Comentropy Hj's
Calculation formula is：
Hj=-∫xfj(x)log fj(x)dx (15)
Wherein：fj(x) it is the probability density function of j moment residential blocks charging electric vehicle load, x is that different periods are electronic
Automobile charging load, kW.
F8, the probabilistic entropy measure assessment models of residential block charging electric vehicle load fluctuation are：
Wherein：HδThe standard deviation of charging load entropy measure during charging unordered for residential block electric automobile.
Compared with prior art, the beneficial effects of the invention are as follows：
First, the present invention embodies electricity by building the charging electric vehicle Load Probability model analyzed based on price elasticity
Response relation between electrical automobile car owner and charging electricity price, in the costly period of charging, car owner's responsiveness is relatively low, and charge expense
Relatively low period, car owner have higher responsiveness.
2nd, the present invention is estimated the fluctuation of load dynamic probability by comentropy and out-of-limit probability, and then assesses resident
The uncertainty of area's charging electric vehicle load fluctuation degree and the overlond running risk of cell distribution transformer.Can also be complete
Charging electric vehicle negative rules are assessed to electric automobile agent income, residential block distribution transformer overlond running wind in face
The influence of danger provides foundation.
Brief description of the drawings
Fig. 1 is embodiment residential block routine power load situation table.
Fig. 2 is case study on implementation using the unordered charging of conventional electric automobile and the inventive method uncertainty index table of comparisons.
Fig. 3 is case study on implementation using the unordered charging of conventional electric automobile and the control of the inventive method load power coverage probability
Figure.Wherein, (a) is to use unordered charged condition, and (b) is using the inventive method charged condition.
Embodiment：
The present invention is described further below by embodiment.Following embodiments are only to do an example, its
The selection of parameter is set based on existing small-sized residential block actual conditions, if being directed to large-scale residential block or other charging places,
The present invention is also still applicable.
Embodiment
A, the residential block electric automobile recoverable amount that the present invention uses is electronic with 24 hours one day for time scale for 500
The automobile starting trip moment obeys N (8.92,3.2422) normal distribution, finally go on a journey finish time obey N (17.47,
3.4122) normal distribution, daily travel l in units of km obey logarithm normal distribution, i.e.,：Lnl~N (3.46,
1.1422).The residential block routine power load is as shown in Figure 1.Hundred kilometers of power consumption U of electric automobile are in residential block
15.84kWh/100km, the charge efficiency estimate η of electric automobile is 0.87 in residential block, and electric automobile is filled using invariable power
Electricity, charge power P are 7.3kW.
Residential block civilian electricity price is 0.617 yuan/kWh.
B, by the daily travel l of electric automobile, solved by formula (1) and obtain the charging duration T of electric automobileC：
Wherein：P be electric automobile charge power, kW；U is hundred kilometers of power consumption, kWh/100km；η is imitated for charging
Rate,For the computing that rounds up, 1≤TC≤24。
C, Latin Hypercube Sampling is used to electric automobile starting trip moment, finish time of finally going on a journey, charging duration,
Sampling scale V is 2000, charging electric vehicle scene matrix E=[TS,TE,TC]T, wherein TS、TEAnd TCRespectively 1 × V is tieed up
Begin trip moment vector, finally go on a journey finish time vector, charging when long vector, each row form one of electric automobile in E
Charging scenarios.
D, hop count d when single electric automobile of calculating is corresponding chargeable under charging scenarios vvFor：
Wherein：TS,v、TE,v、TC,vRespectively TS、TE、TCV column elements in vector, represent electric automobile in charging scenarios v
In starting trip the moment, finally go on a journey finish time, charging duration.
E, under charging scenarios v, by TS,v、TE,v、TC,vEstablish the charging electric vehicle load mould based on price elasticity analysis
Type：
E1, last trip finish time T by electric automobile under charging scenarios vE,v, single electric automobile can be solved and existed
Corresponding initiation of charge period T under charging scenarios vQ,v：
TQ,v∈(TE,v,TE,v+dv) (3)
Wherein：dvSolved and obtained by formula (2).
E2, solution single electric automobile initiation of charge moment under charging scenarios v are TQ,vWhen charging scheme：
Wherein：N is possible initiation of charge period T in charging scenarios vQ,vNumber；For charging scenarios v
The charge power at lower j moment, its value are：
E3, single electric automobile charge power corresponding to the j moment under charging scenarios v single order central moment (expectation) Cvj,1
For：
K (k >=2) rank central moment Cvj,k：
E4, solve pvj：
Wherein：ε is price elastic coefficient, and value is -1.25；λrFor r moment residential blocks electric automobile charger assembled by several branch when electricity
Valency, member/kWh.
F, solved by Cumulants method counted and during electric automobile residential block load each rank central moment, utilize Gram-
Charlier infinite series expansions ask for the probability-distribution function of residential block charging electric vehicle load, and solution obtains each moment
The entropy measure of residential block charging electric vehicle load and the out-of-limit probability of distribution transformer, and then residential block electric automobile can be drawn
The assessment models of the probabilistic assessment models of the load fluctuation degree that charges, cell distribution transformer overload risk：
F1, electric automobile recoverable amount is set in residential block as M, and different charging electric vehicle behaviors is separate,
As central-limit theorem can obtain single electric automobile at the j moment corresponding to charge power k rank central moments Lk,jFor：
Wherein：Cvj,kSolved and obtained by formula (7).
The single order of F2, j moment (0 ＜ j≤24) residential block conventional load, second order cumulant are：
Wherein：Respectively desired value and variance of the residential block conventional load at the j moment.
F3, k (k=1,2,3 ...) rank half of calculating single electric automobile j moment charge powers in charging scenarios v are constant
Measure Dk,j：
Wherein：Lk,jIt is electric automobile in k rank central moments corresponding to j moment charge powers, is solved and obtained by formula (9).
F4, residential block total load power the j moment k rank cumulant Bk,jFor：
Wherein：M be residential block in electric automobile recoverable amount, Dk,jSolved and obtained by formula (11).
F5, residential block total load power solved each by each rank cumulant of each moment total load power in residential block
Each rank central moment at moment, then deploy solution by Gram-Charlier infinite series and obtain each moment residential block total load work(
The probability-distribution function F (x) of the rate and probability density function f (x) of charging electric vehicle load.
F6, when residential block distribution transformer overload degree reach 60% when, transformer short-time overload allow the time be
0.75h, therefore, the overload risk X and its assessment models of residential block distribution transformer can be solved：
X≤0.75 (14)
Wherein：The capacity of residential block distribution transformer is 630kVA, and Y was born to load with by distribution transformer in residential block
The limit value of lotus, kW；Fj(Y) it is distribution function value of the j moment residential blocks internal loading power at transformer overload 60%；Fj(Y) by
Cumulant and Gram-Charlier infinite series expansions are asked for.
F7, by charging electric vehicle load the j moment fj(x) can solve to obtain j moment charging electric vehicle loads
Comentropy Hj；
Comentropy HjCalculation formula be：
Hj=-∫xfj(x)log fj(x)dx (15)
Wherein：fj(x) it is the probability density function of j moment residential blocks charging electric vehicle load, x is electronic at different moments
Automobile charging load, kW.
F8, calculate the probabilistic entropy measure assessment models of residential block charging electric vehicle load fluctuation：
Wherein：HδThe standard deviation of charging load entropy measure during charging unordered for residential block electric automobile.
Case study on implementation effect is as shown in Figures 2 and 3.
Residential block conventional load Normal Distribution, therefore the fluctuation range of resident's conventional load is ± 3 δ, wherein δ is residence
The standard deviation of people area load fluctuation, resident's conventional load fluctuation range are typically uncontrollable.It can be observed by Fig. 1：Resident is conventional
Load is in the fluctuation range W of peak period：306.48kW, residential block load is in peak value period ripple during electric automobile unordered charging
Dynamic scope W1For 435kW, when considering the pricing strategy guiding charging electric vehicle of this patent uncertainty appraisal procedure, cell peak
It is worth period range of load fluctuation W2For 365kW, accordingly, it is considered under the pricing strategy guiding of this patent uncertainty appraisal procedure,
Charging electric vehicle load relatively unordered charging when the load fluctuation rate that reduces beBring into data obtain μ=
45.53%, nearly reduce half during the more unordered charging of load fluctuation, the uncertainty of load fluctuation has obvious reduction.
It can be drawn by Fig. 2, be contrasted with the unordered charging of electric automobile：
(1) when considering the pricing strategy guiding charging electric vehicle of this patent uncertainty appraisal procedure, electric automobile fills
The entropy measure of electric load has obvious reduction, is beneficial to residential block electric automobile agent and grasps cell load degree of fluctuation
Uncertainty, so as to have demand to grid company power purchase, avoid electric automobile agent and measured to grid company power purchase
Now serious surplus and vacancy situation, reduce the agential purchases strategies of residential block electric automobile.
(2) when considering the pricing strategy guiding charging electric vehicle of this patent uncertainty appraisal procedure, residential block distribution
The overload risk of transformer has obvious reduction, make the expectation overload operating time of cell distribution transformer safety limit it
It is interior, so as to extend the service life of cell transformer, improve the stability and reliability of cell distribution system.
Although the illustrative embodiment of the present invention is described above, so as to the technology people of the art
Member understands the present invention, it should be apparent that the invention is not restricted to the scope of embodiment, as long as various change is in institute of the present invention
Attached claim is limited with scope, and these changes are it will be apparent that all utilize the innovation and creation of present inventive concept
In the row of protection.
Claims (3)
1. the appraisal procedure of residential block charging electric vehicle negative rules, going out based on electric automobile under a kind of tou power price
Row statistical property, the charging electric vehicle load dynamic probability model of structure price elasticity analysis, car owner is characterized with probability nature
The uncertainty of response relation between tou power price；The fluctuation of load dynamic probability is surveyed by comentropy and out-of-limit probability
Degree, and then assess the uncertainty of residential block charging electric vehicle load fluctuation degree and the overload wind of cell distribution transformer
Danger, includes following key step：
A, residential block charging electric vehicle tou power price λ is inputtedi(i=1,2 ... 24), electric automobile starting trip moment TS, it is last
Go on a journey finish time TE, daily travel l statistical distribution；
B, by the daily travel l of electric automobile, solved by formula (1) and obtain the charging duration T of electric automobileC：
Wherein：P be electric automobile charge power, kW；U is hundred kilometers of power consumption, kWh/100km；η is charge efficiency；
For the computing that rounds up, 1≤TC≤24；
C, by Latin Hypercube Sampling technology, set sampling scale as V, to electric automobile originate trip the moment, charging duration,
Finally trip finish time is sampled, and obtains sampling matrix E=[TS,TE,TC]T, wherein TS、TEAnd TCRespectively 1 × V is tieed up
Begin trip moment vector, finally go on a journey finish time vector, charging when long vector, each row form one of electric automobile in E
Charging scenarios；
D, hop count d when single electric automobile of calculating is corresponding chargeable under charging scenarios vvFor：
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Wherein：TS,v、TE,v、TC,vRespectively TS、TE、TCV column elements in vector, represent electric automobile in charging scenarios v
Starting trip moment, finish time of finally going on a journey, charging duration；
E, under charging scenarios v, by TS,v、TE,v、TC,vEstablish the charging electric vehicle load model based on price elasticity analysis；
F, solved by Cumulants method counted and during electric automobile residential block load each rank central moment, utilize Gram-
Charlier infinite series expansions ask for the probability-distribution function of residential block charging electric vehicle load, and solution obtains each moment
The entropy measure of residential block charging electric vehicle load and the out-of-limit probability of distribution transformer, and then show that residential block electric automobile fills
The assessment models of the probabilistic assessment models of electric load degree of fluctuation, cell distribution transformer overload risk.
2. the appraisal procedure of residential block charging electric vehicle negative rules under tou power price according to claim 1,
It is characterized in that：The charging electric vehicle load model based on price elasticity analysis is established in the step E, it is electronic by residential block
Starting trip moment T of the automobile under charging scenarios vS,v, finally go on a journey finish time TE,v, charging duration TC,vAnd price bullet
Property coefficient ε establishes comprising the following steps that for charging electric vehicle load model：
E1, last trip finish time T by electric automobile under charging scenarios vE,v, single electric automobile can be solved and charged
Corresponding initiation of charge moment T under scene vQ,v：
TQ,v∈(TE,v,TE,v+dv) (3)
Wherein：dvSolved and obtained by formula (2)；
E2, single electric automobile the initiation of charge moment under charging scenarios v are TQ,vWhen charging scheme be：
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The charge power of j periods, its value are under electric field scape v：
E3, single electric automobile charge power corresponding to the j periods under charging scenarios v single order central moment (expectation) Cvj,1For：
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<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：pvjFor single electric automobile, the initiation of charge moment is the probability at j moment under charging scenarios v, represents electric automobile
Car owner and the response relation of charging electricity price, in the costly period of charging, car owner's responsiveness is relatively low, charges the expense relatively low period,
Car owner has higher responsiveness；
E4、pvjThe uncertainty of electric automobile main response tou power price is reacted, its solution formula is：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>j</mi>
</mrow>
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>j</mi>
<mo>+</mo>
<mi>N</mi>
</mrow>
</munderover>
<msubsup>
<mi>&lambda;</mi>
<mi>r</mi>
<mi>&epsiv;</mi>
</msubsup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
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<mn>1</mn>
</mrow>
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<mi>v</mi>
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<mi>j</mi>
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<mi>T</mi>
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<mi>N</mi>
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<mi>r</mi>
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<mo>)</mo>
</mrow>
</mrow>
</mfrac>
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<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>j</mi>
<mo>+</mo>
<mi>N</mi>
<mo>&le;</mo>
<mn>24</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
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<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>-</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>j</mi>
<mo>+</mo>
<mi>N</mi>
</mrow>
</munderover>
<msubsup>
<mi>&lambda;</mi>
<mi>r</mi>
<mi>&epsiv;</mi>
</msubsup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>j</mi>
<mo>+</mo>
<mi>N</mi>
</mrow>
</munderover>
<msubsup>
<mi>&lambda;</mi>
<mi>r</mi>
<mi>&epsiv;</mi>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>,</mo>
<mi>r</mi>
<mo>&le;</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>E</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>C</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
</msub>
<mo>-</mo>
<mn>24</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>&cup;</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：ε is price elastic coefficient；λrFor the charging tou power price corresponding to the electric automobile of r moment residential blocks, member/kWh.
3. the appraisal procedure of residential block charging electric vehicle negative rules under tou power price according to claim 1,
It is characterized in that：The entropy measure and distribution transformer of each moment residential block charging electric vehicle load are solved in the step F
Out-of-limit probability, and then the probabilistic assessment models of residential block charging electric vehicle load fluctuation degree, cell distribution can be drawn
The assessment models of transformer overload risk；Comprise the following steps that：
F1, electric automobile recoverable amount is set in residential block as M, and different charging electric vehicle behaviors is separate, in
Heart limit theorem can obtain k rank central moment L of the single electric automobile in charge power corresponding to the j momentk,jFor：
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>V</mi>
</mfrac>
<mo>&CenterDot;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>v</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>V</mi>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：Cvj,kSolved and obtained by formula (7)；
F2, it can be generally thought each moment residential block total load and include residential block conventional load and charging electric vehicle load, set
Resident's conventional load obey desired value be μ normal distribution, standard deviation δ is 0.1 μ, then three ranks of residential block conventional load and its
Multistage cumulant very little can be neglected；The single order of j moment residential blocks conventional load, second order cumulant are：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>H</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>&mu;</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>H</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msubsup>
<mi>&delta;</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：μj,(0≤j≤24) are respectively desired value and variance of the residential block conventional load at the j moment；
F3, single electric automobile j period charge powers in charging scenarios v k (k=1,2,3 ...) rank cumulant Dk,jFor：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msubsup>
<mi>&delta;</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mn>3</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>3</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mn>4</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>4</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mn>3</mn>
<msubsup>
<mi>L</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>L</mi>
<mi> </mi>
<mi>L</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：Lk,jIt is electric automobile in k rank central moments corresponding to j moment charge powers, is solved and obtained by formula (9)；
F4, the property according to cumulant：The k rank cumulant of separate stochastic variable meets homogeneity and additive property, occupies
K rank cumulant B of the people area total load power at the j momentk,jFor：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>M</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>D</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&mu;</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>M</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>D</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>&delta;</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mrow>
<mn>3</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>M</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>D</mi>
<mrow>
<mn>3</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mrow>
<mn>4</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>M</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>D</mi>
<mrow>
<mn>4</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>L</mi>
<mi> </mi>
<mi>L</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：M be residential block in electric automobile recoverable amount, Dk,jSolved and obtained by formula (11)；
F5, residential block total load power can be solved according to each rank cumulant of each moment total load power in residential block when each
Each rank central moment carved, then deploy solution by Gram-Charlier infinite series and obtain each moment residential block total load power
Probability-distribution function F (x) and charging electric vehicle load probability density function f (x)；
F6, when residential block distribution transformer overload degree reach 60% when, transformer short-time overload allow the time be 0.75h, because
This, the assessment models X of residential block distribution transformer overload risk is：
<mrow>
<mi>X</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>F</mi>
<mi>j</mi>
</msub>
<mo>(</mo>
<mi>Y</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
X≤0.75 (14)
Wherein：Y is born to overload the limit value of load, kW by distribution transformer in residential block；Fj(Y) it is j moment residential blocks internal loading
Distribution function value of the power at transformer overload 60%；Fj(Y) deployed by cumulant and Gram-Charlier infinite series
Formula is asked for；
F7, the f by the j momentj(x) can solve to obtain the comentropy H of j moment charging electric vehicle loadsj；Comentropy HjCalculating
Formula is：
Hj=-∫xfj(x)log fj(x)dx (15)
Wherein：fj(x) it is the probability density function of j moment residential blocks charging electric vehicle load, x is electric automobile at different moments
Charging load, kW；
F8, the probabilistic entropy measure assessment models of residential block charging electric vehicle load fluctuation are：
<mrow>
<msqrt>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>H</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<msub>
<mi>H</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<mn>24</mn>
</mfrac>
</msqrt>
<mo><</mo>
<msub>
<mi>H</mi>
<mi>&delta;</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>16</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：HδThe standard deviation of charging load entropy measure during charging unordered for residential block electric automobile.
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| CN103683424A (en) * | 2013-12-17 | 2014-03-26 | 清华大学 | Electric vehicle charging station sequential charging control method based on dynamic time-of-use electricity price |
| CN105825434A (en) * | 2015-11-25 | 2016-08-03 | 国家电网公司 | Charging load calculation method for electric vehicles |
| CN105976270A (en) * | 2016-05-25 | 2016-09-28 | 西南交通大学 | Dynamic probability distribution determination method for charging load of electric automobile under time-of-use power price |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103683424A (en) * | 2013-12-17 | 2014-03-26 | 清华大学 | Electric vehicle charging station sequential charging control method based on dynamic time-of-use electricity price |
| CN105825434A (en) * | 2015-11-25 | 2016-08-03 | 国家电网公司 | Charging load calculation method for electric vehicles |
| CN105976270A (en) * | 2016-05-25 | 2016-09-28 | 西南交通大学 | Dynamic probability distribution determination method for charging load of electric automobile under time-of-use power price |
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| Title |
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| 张夏霖 等: "含电动汽车与可控负荷的光伏智能小区两阶段优化调度", 《电网技术》 * |
| 臧志华 等: "不同充电策略下电动汽车对配电网的影响分析", 《电力信息与通信技术》 * |
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