CN105024432A - Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price - Google Patents

Abstract

The invention provides an electric vehicle charge-discharge optimized dispatching method based on virtual electricity price. The method comprises the following steps: an electric energy public service platform predicts and samples the basic daily load information of a target area within an optimization time interval; when a new EV is connected to a charging pile within the target area, the network connection information of the new EV is read; a user input the charging information of the vehicle; an EV charge-discharge power model is constructed; virtual electricity price is calculated to indirectly reflect the load level of the target area; a dispatching model with the charge-discharge power as an optimization variable is constructed; dynamic time-of-use electricity price for user cost calculation is determined by combining wavelet analysis preprocessing and fuzzy clustering methods; the user makes an automatic response decision; a charge-discharge operation is performed on the EV according to the decision of the user and a plan is uploaded. The electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is capable of realizing peak clipping and valley filling of EV cluster load and reducing the charge-discharge cost of the user on the basis of meeting the charging requirement of the user and the capacity limitation of a power distribution transformer. In case of a great EV cluster scale, the electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is still capable of meeting grid side expectations.

Description

A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price

Technical field:

The invention belongs to electric automobile (EV) and electrical network interaction technique field, particularly a kind of electric automobile discharge and recharge Optimized Operation based on virtual electricity price and its implementation.

Background technology:

In recent years, the requirement of people to energy and environment is more and more higher, and the under-reserve of fossil fuel and global warming are by growing interest, and the lifting of environmental protection ideas makes people be strongly required to reduce consumption of petroleum in traffic.Electric automobile, due to its special driven by energy mode, has good energy-conservation and low emission potentiality, is developed widely.Electric automobile can improve efficiency of energy utilization and reduce the pollution to environment, and it is popularized has become following trend with popularization, and various countries also take positive policies and measures to encourage the development of electric automobile.

The electrification of traffic system is changed and is made the energy requirement of vehicle be transferred to electric power system gradually from fossil fuel.But along with the extensive development of electric automobile, because the charging behavior of car owner is often relatively more random, a large amount of electric automobile access electrical network charges, and will certainly cause huge pressure to electric network composition and operation.Load peak-valley difference is important safety, an economic index of power system operation, the aggravation of peak-valley difference, can bring the adverse consequencess such as the reduction of grid equipment utilization ratio, the increase of supply side Trading risk.A large amount of electric automobile Stochastic accessing electrical network carries out unordered charging, can aggravate system loading peak-valley difference further, bring negative effect to the running status of distribution.

The electric automobile discharge and recharge of conservative control access power distribution network, can reduce the impact that extensive charging electric vehicle causes electrical network, make it meet distribution system stability and cost-effectiveness requirement.At present, the achievement in research both at home and abroad for the orderly discharge and recharge of electric automobile is existing a lot.Non-patent literature 1 proposes the orderly charging strategy based on dynamic sharing electricity price, while realization charging load peak load shifting, also reduces user's charging cost, but does not consider the effect of V2G.Non-patent literature 2 establishes distributing electric automobile discharge and recharge betting model so that electric cost is minimum for target, improves electricity consumption economy, and have adjusted system loading curve.Application number be 201410233619.7 patent application propose a kind of electric automobile cluster discharge and recharge optimal control method, provide practicable theoretical foundation for electric automobile participates in electrical network interaction.Above-mentioned research often lacks consideration charge-discharge electric power being carried out to continuously adjustabe, is optimizing the space degree of depth also existing development and perfection.

Reference listing:

[non-patent literature 1] " the orderly charging strategy of the electric automobile charging station based on dynamic sharing electricity price ", Proceedings of the CSEE, 2014,34 (22);

[non-patent literature 2] " distributing electric automobile networking strategy study ", electrotechnics journal, 2014,29 (8);

[patent documentation] " electric automobile cluster discharge and recharge optimal control method ", 201410233619.7.

Summary of the invention:

The present invention will overcome the above-mentioned shortcoming of prior art, provides a kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price.

The present invention is for the purpose of peak load shifting, take into account power distribution system load information and user's energy loss expense and battery loss cost, propose a kind of electric automobile discharge and recharge Optimized Operation based on virtual electricity price and its implementation, make when a large amount of electric automobile access grid charging, realize the peak load shifting of electric automobile cluster load, and reduce the discharge and recharge cost of user.Establishing based on virtual electricity price theory with charge-discharge electric power is the electric automobile discharge and recharge Optimal Operation Model (CA-vTOS) of optimized variable, and comprehensive wavelet analysis and fuzzy clustering determine the dynamic sharing electricity price for electric automobile user discharge and recharge cost calculation.The detailed process of technical scheme is as follows:

1) the basic daily load information of electric energy public service platform prediction, sampling optimization period region of interest within, specifically comprises:

S11. base load power curve is predicted: according to known parameters and model, dope the basic daily load curve of target area;

S12. optimize discretization continuous time in the period: determine minimum optimization period Δ t, discretization analysis is carried out to the optimization period, successively the basic daily load curve of step S11 gained is sampled, obtain the predicted load L of each k ∈ [1, J] in the period _b(k); Wherein J be in one day according to setting determination divide optimization time hop count;

2) when the charging pile having new electric automobile l to access in target area, read the inbound information of vehicle l, comprise network entry time T _{in, l}, electrokinetic cell capacity C _s,lwith initial state-of-charge (State of Charge, SOC) S _{0, l}deng;

3) user inputs the charge information of vehicle l: expect from net time T _{out, l}, expect state-of-charge S _e,l; The charge requirement that can meet user is judged in conjunction with described reading information and described user's input information, if can not, then inform that user inputs by User Interface incorrect, prompting user re-enters expection from net time or expectation SOC information, if user accepts prompting, and input can be implemented, correct charge information, then carry out step 4) ~ step 10) operation, otherwise, abandon this user, perform step 2);

4) the charge-discharge electric power model of electric automobile is built: P _l(k)=p _l(k) f _m,l(k);

In formula, P _lk () represents the charge-discharge electric power of vehicle l; p _lk () represents vehicle l and the system Power Exchange in the k period, p _lk () >0 represents that vehicle l is in charged state; p _lk () <0 represents and is in discharge condition; p _lk ()=0 expression is in floating charge state; f _m,lk (), for characterizing the operability of day part to Vehicular battery, expression formula is:

Wherein, T _m,lfor vehicle l accesses the duration T of electrical network _{pe, l}=T _{out, l}-T _{in, l}the period set comprised;

Suppose that the electric automobile power battery participating in scheduling is lithium battery, according to the discharge and recharge correlation properties of lithium battery, within the single period, lithium electricity can be considered it is invariable power discharge and recharge, and the relation of its state-of-charge and corresponding discharge and recharge time is characterized by:

S _l(k)＝S _l(k-1)+P _l(k)η(P _l(k))Δt/C _s,l(2)

In formula, S _l(k-1), S _lk () represents that vehicle l is at kth-1, the SOC of a k period respectively; η (P _l(k)) represent Power Exchange efficiency, concrete relevant with Power Exchange direction:

$\mathrm{\&eta;}\left(P_l\right(k\left)\right)=\left\{\begin{array}{cc}{\mathrm{\&eta;}}_c,& P_l\left(k\right)\&GreaterEqual;0,\\ 1/{\mathrm{\&eta;}}_d,& P_l\left(k\right)<0.\end{array}\right.---\left(3\right)$

Wherein, η _c, η _drepresent charge and discharge efficiency respectively;

5) read in from electric energy public service platform the total load information that vehicle l accesses moment target area

wherein, when representing vehicle l access, the distribution total load information in k session target region; represent electric automobile cluster load: wherein, M _l-1when representing that vehicle l accesses electrical network, complete the vehicle set of charging plan; This computational process is completed by electric energy public service platform;

6) virtual electricity price information is calculated, indirectly the load level of reflection target area:

In formula, when representing vehicle l access, the virtual electricity price of k period; with represent virtual bidding price adjustment coefficient; I _r,j, φ _r,jrepresent respectively with reference to electricity price and reference load value; [u] ⁺represent max{0, u}; represent prediction total load, wherein represent base load predicted value;

7) electric automobile discharge and recharge Optimal Operation Model is set up;

In conjunction with above step, to minimize virtual discharge and recharge cost for target, set up electric automobile discharge and recharge Optimal Operation Model to optimize the charge-discharge electric power of electric automobile, institute's established model is as follows:

$\min V_l=\underset{k\&Element;T_{m,l}}{\&Sigma;}\left\{P_l\left(k\right){\mathrm{\&gamma;}}_l\left(L_a^l\right(k),k)\mathrm{\&Delta;}t\right\}---\left(6\right)$

s.t.S _min≤S _l(k)≤S _max(7)

-P _d≤P _l(k)≤P _c(8)

$S_{0,l}+\underset{k\&Element;T_{m,l}}{\&Sigma;}\mathrm{\&Delta;}t\frac{P_l\left(k\right)}{C_{s,l}}\&GreaterEqual;S_{E,l}---\left(9\right)$

$L_a^l\left(k\right)\&le;{\mathrm{\&kappa;}}_T{A}_T,\widehat{L_a^l\left(k\right)}\&le;{\mathrm{\&kappa;}}_T{A}_T---\left(10\right)$

k＝1,2,…,J.

T _pe,l>T _c,ll＝1,2,…,n (11)

In formula (6), V _lrepresent the virtual discharge and recharge cost of vehicle l; N _lrepresent T _m,lthe length of set; In formula (7), S _max, S _minrepresent maximum and the minimum value of the SOC allowed, prevent controlled vehicle overcharge and overdischarge; Formula (8) represents charge-discharge electric power constraint, P _lk () possesses continuously adjustable characteristic, but usually will be subject to the specified charge and discharge Power Limitation of electrokinetic cell or charger; Formula (9) represents charge requirement constraint, and when vehicle leaves according to appointment, the SOC of its battery need meet the expectation; Formula (10) indication transformer maximum load retrains, κ _tfor transformer efficiency, A _tfor the rated capacity of transformer; Formula (11) represents time relationship constraint, and n optimizes the access Fleet Size in the period, T _c,lfor charging to the shortest time expected needed for SOC: T _c,l=(S _e,l-S _{0, l}) C _s,l/ P _cη _c;

Solve above-mentioned Optimal Operation Model, complete the optimization of current access charging and discharging vehicle power, now, the discharge and recharge operation plan of vehicle l is: $P_l=\left(P_l\right(T_{m,l}^1),P_l\left(T_{m,l}^2\right),...,P_l\left(T_{m,l}^{N_l}\right)),$ Wherein, $T_{m,l}^i(i=1,2,...,N_l)$ Represent set T _m,lin i-th element;

8) electric automobile user cost calculation: U _l=(c _{cd, l}| η _c=η _d=1)+c _{bat, l}+ c _{loss, l};

In formula, U _lrepresent user cost; c _{bat, l}represent the service life of lithium battery loss conversion cost of vehicle l; c _{loss, l}represent energy loss expense; c _{cd, l}| η _c=η _d=1, the desirable discharge and recharge expense under charge and discharge efficiency term is not considered in expression: in formula, pri (k) represents electricity price information, and in the present invention, it is a kind of dynamic sharing electricity price, and namely peak, low ebb electricity price are fixed, and the electricity price of peak interval of time change, wherein, peak, low ebb electricity price are expressed as pri ^h, pri ^l; Utilize wavelet analysis and the division of fuzzy clustering method realization to tou power price peak interval of time:

S81. preliminary treatment: information on load is carried out the wavelet decomposition that yardstick is 3, by the high fdrequency component zero setting of one, two layer, obtains new information on load after reconstruct

S82. attribute characterization: adopt type bigger than normal and the trapezoidal Fuzzy Distribution of type less than normal half to carry out attribute characterization to the information on load after reconstruct, form attribute matrix A (a) _{j × 2}, computational methods are:

$\left\{\begin{array}{c}{a}_{k1}=\frac{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-L_a^{l*}\left(k\right)}{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}\\ a_{k2}=\frac{L_a^{l*}\left(k\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}\end{array}\right.---\left(12\right)$

S83. translation-standard deviation conversion is carried out to matrix A, set up fuzzy similarity matrix R (r) with subtrabend's absolute value method _{j × J};

S84. continuous quadratic side is asked to similar matrix, i.e. R → R ²→ R ⁴→...→ R ²ⁱ, until there is R in →... ^kο R ^k=R ^k(ο represents fuzzy matrix composition computing), now, R ^kbe the transitive closure t (R) of this similar matrix;

S85. at transitive closure t (R)=(t _ij) in, make λ be t _ijin a certain value, ask for the λ-Level Matrix R of t (R) _ij, then, the descending value of λ just can formative dynamics cluster, gets suitable λ value, determines peak interval of time splitting scheme;

9) the autonomous Response Decision of electric automobile user;

Utilize step 7) described in the electric automobile charge-discharge electric power that obtains of Optimized model and step 8) the dynamic sharing electricity price that obtains determines the discharge and recharge cost of user, and discharge and recharge operation plan is informed user with corresponding situation of Profit, by user from main response charge mode, Response to selection operation plan or start unordered charging;

10) according to user's decision-making, discharge and recharge operation is implemented to electric automobile, and upload plan;

If user selects to start unordered charging, electrically-charging equipment provides lasting invariable power charging service for the electric automobile of access, until meet the charge requirement of user or vehicle leaves; If user's Response to selection is dispatched, then according to operation plan P _lconcrete discharge and recharge operation is implemented to electric automobile, accordingly, just determines the discharge and recharge plan of vehicle l; This discharge and recharge plan is uploaded to electric energy public service platform, by electric energy public service platform, plan load is integrated, complete once to the real-time update of described target area information on load, and wait for that next electric automobile networks; If there is new vehicle access, then skip to step 2); Lasting execution terminates to optimizing the period by this process.

Beneficial effect of the present invention at least comprises following several aspect:

(1) on the basis meeting user's charge requirement and distribution transformer capacity restriction, the peak load shifting of electric automobile cluster load can be realized, and reduces the discharge and recharge cost of user, be easy to be accepted by electric automobile user.

(2) still can, at connection electrical network period level and smooth load curve, avoid occurring peak valley unusual appearance, and now user's income can corresponding reduction when electric automobile cluster scale is larger, meet grid side expectation.

(3) by playing the load transfer plan potentiality of electric automobile cluster, the optimization electricity consumption of system is achieved; The dynamic sharing electricity price for electric automobile discharge and recharge simultaneously carried, can the matching optimization charge-discharge electric power, the certain profits measurement of electric automobile user that gives to participate in scheduling that obtain, for institute's established model provides a kind of implementation method, be conducive to the bidirectional equalization realizing power supply and demand and user's economic benefit.

Accompanying drawing illustrates:

Fig. 1 is electric automobile discharge and recharge Optimized Operation Organization Chart of the present invention;

Fig. 2 is the electric automobile discharge and recharge Optimized Operation flow chart based on virtual electricity price of the present invention;

Fig. 3 is the load curve under unordered and CA-vTOS discharge and recharge Optimizing Mode of the present invention;

Fig. 4 is of the present invention unordered with the user cost correlation curve figure under CA-vTOS discharge and recharge Optimizing Mode;

Fig. 5 is the unordered load curve comparison diagram with CA-vTOS discharge and recharge Optimizing Mode under different electric automobile scale of the present invention.

Embodiment:

Be described further the present invention below in conjunction with accompanying drawing, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.The electric automobile discharge and recharge Optimized Operation framework that the present invention builds as shown in Figure 1, comprises the steps:

1) the basic daily load information of electric energy public service platform prediction, sampling optimization period region of interest within, specifically comprises:

S11. base load power curve is predicted: according to known parameters and model, dope the basic daily load curve of target area;

S12. optimize discretization continuous time in the period: determine minimum optimization period Δ t, discretization analysis is carried out to the optimization period, successively the basic daily load curve of step S11 gained is sampled, obtain the predicted load L of each k ∈ [1, J] in the period _b(k); Wherein J be in one day according to setting determination divide optimization time hop count;

2) when the charging pile having new electric automobile l to access in target area, read the inbound information of vehicle l, comprise network entry time T _{in, l}, electrokinetic cell capacity C _s,lwith initial state-of-charge S _{0, l}deng;

3) user inputs the charge information of vehicle l: expect from net time T _{out, l}, expect state-of-charge S _e,l; The charge requirement that can meet user is judged in conjunction with described reading information and described user's input information, if can not, then inform that user inputs by User Interface incorrect, prompting user re-enters expection from net time or expectation SOC information, if user accepts prompting, and input can be implemented, correct charge information, then carry out step 4) ~ step 10) operation, otherwise, abandon this user, perform step 2);

4) the charge-discharge electric power model of electric automobile is built: P _l(k)=p _l(k) f _m,l(k);

Suppose that the electric automobile power battery participating in scheduling is lithium battery, according to the discharge and recharge correlation properties of lithium battery, within the single period, lithium electricity can be considered it is invariable power discharge and recharge, and the relation of its state-of-charge and corresponding discharge and recharge time characterizes such as formula shown in (2);

5) read in from electric energy public service platform the total load information that vehicle l accesses moment target area

6) calculate virtual electricity price information, indirectly the load level of reflection target area, the relation of virtual electricity price and load is such as formula shown in (4) ~ (5):

7) electric automobile discharge and recharge Optimal Operation Model is set up, shown in (6) ~ (11);

Solve above-mentioned Optimal Operation Model, complete the optimization of current access charging and discharging vehicle power, now, the discharge and recharge operation plan of vehicle l is: $P_l=\left(P_l\right(T_{m,l}^1),P_l\left(T_{m,l}^2\right),...,P_l\left(T_{m,l}^{N_l}\right)),$ Wherein, $T_{m,l}^i(i=1,2,...,N_l)$ Represent set T _m,lin i-th element;

8) electric automobile user cost calculation: U _l=(c _{cd, l}| η _c=η _d=1)+c _{bat, l}+ c _{loss, l};

9) the autonomous Response Decision of electric automobile user;

Utilize step 7) described in the electric automobile charge-discharge electric power that obtains of Optimized model and step 8) the dynamic sharing electricity price that obtains determines the discharge and recharge cost of user, and discharge and recharge operation plan is informed user with corresponding situation of Profit, by user from main response charge mode, Response to selection operation plan or start unordered charging;

10) according to user's decision-making, discharge and recharge operation is implemented to electric automobile, and upload plan;

If user selects to start unordered charging, electrically-charging equipment provides lasting invariable power charging service for the electric automobile of access, until meet the charge requirement of user or vehicle leaves; If user's Response to selection is dispatched, then according to operation plan P _lconcrete discharge and recharge operation is implemented to electric automobile, accordingly, just determines the discharge and recharge plan of vehicle l; This discharge and recharge plan is uploaded to electric energy public service platform, by electric energy public service platform, plan load is integrated, complete once to the real-time update of described target area information on load, and wait for that next electric automobile networks; If there is new vehicle access, then skip to step 2); Lasting execution terminates to optimizing the period by this process.

Step 1 of the present invention) be responsible for by electric energy public service platform; Step 2) ~ 10) perform in each charging pile in the target area, detailed process is as shown in Figure 2.

In the present embodiment, the power distribution network of residential block electrically-charging equipment cluster is comprised for goals research region with one.Access transformer capacity is 750kVA, and efficiency is 0.95, and with base load and electric automobile cluster load under this transformer, the highest baseline load accounts for 80% of distribution transformer maximum load.If the electric automobile quantity of this distribution service is 50, the electrokinetic cell capacity of electric automobile is 60kWh, and specified charge and discharge power is 7kW, and charge and discharge efficiency is 0.92, battery SOC border (S _max, S _min) be 0.9 and 0.1.According to the feature of this residential block base load information, for-0.21; I _{r, 1}, I _{r, 2}get low ebb electricity price and peak electricity tariff respectively, be set to 0.37425 yuan/(kWh), 1.5096 yuan/(kWh) respectively; φ _{r, 1}get low ebb load average 405.1019, φ _{r, 2}get the difference 75.7441 of peak, low ebb load average.The probability of user's Response to selection discharge and recharge scheduling is 1, if the SOC expected when user leaves is 0.9, design evaluation time length is 24h, and time interval Δ t is 0.5h.Choosing λ is 0.9.

If electric automobile user leaves residence time point Normal Distribution (desired value is 7:45, and standard deviation is 1h) morning; The time point Normal Distribution (desired value is 19:00, and standard deviation is 1.5h) returned to quarters afternoon; When returning to quarters, power cell of vehicle SOC Normal Distribution (desired value is 0.6, and standard deviation is 0.1), and establish the parameters such as the discharge and recharge beginning and ending time of EV, initial SOC separate.

In order to better embody control effects of the present invention, simulation comparison is in unordered charge mode.Unordered with load curve under CA-vTOS discharge and recharge Optimizing Mode as shown in Figure 3, the Cost comparisons of each electric automobile user is as shown in Figure 4.

Table 1 is unordered with CA-vTOS effect of optimization statistical information

Table 1 is unordered with CA-vTOS effect of optimization statistical information.Associative list 1, accompanying drawing 3 and accompanying drawing 4 are known, under unordered charge mode, a large amount of electric automobile concentrates on the charging of load evening peak period, exacerbates system peak-valley difference further, peak load exceedes 10.55% of transformer capacity restriction, has influence on the safe and reliable operation of distribution.In CA-vTOS discharge and recharge Optimizing Mode, vehicle discharges when virtual electricity price peak, charge during low ebb, peak load shifting effect is played to system loading, compared to unordered charging, peak-valley difference, load fluctuation rate all decrease, and because CA-vTOS discharge and recharge Optimizing Mode can regulate charge-discharge electric power flexibly, therefore have larger advantage improving in load fluctuation.

In order to embody under different electric automobile cluster scale the present invention compared to the superiority of unordered charge mode, under the present embodiment emulation obtains different electric automobile cluster scale, the load curve comparison diagram of CA-vTOS discharge and recharge Optimizing Mode and unordered charge mode, as shown in Figure 5; And the load curve statistical information of CA-vTOS pattern under different scales, as shown in table 2.

The statistical information of CA-vTOS pattern under table 2 different scales

From accompanying drawing 5 and table 2, along with the increase of rechargeable energy permeability, load fluctuation standard deviation can increase, illustrate that the electric automobile of access is larger, exceeded the best access scale of this distribution, but at mainly the parking the period of vehicle (16:00 to next day 8:00 between), the load curve after optimization is still very level and smooth, " peak valley is put upside down " phenomenon can't be there is, highlight the superiority of CA-vTOS.

As mentioned above, just the present invention can be realized preferably, above-described embodiment is only exemplary embodiments of the present invention, not be used for limiting practical range of the present invention, can carry out multiple change, amendment, replacement and modification to these embodiments when not departing from principle of the present invention and aim, scope of the present invention is by claim and equivalency thereof.

Claims (1)

1., based on an electric automobile discharge and recharge Optimization Scheduling for virtual electricity price, the charge power rated value of every platform charging pile is P _c, discharge power rated value is P _d, comprise the steps:

1) the basic daily load information of electric energy public service platform prediction, sampling optimization period region of interest within, specifically comprises:

S11. base load power curve is predicted: according to known parameters and model, dope the basic daily load curve of target area;

S12. optimize discretization continuous time in the period: determine minimum optimization period Δ t, discretization analysis is carried out to the optimization period, successively the basic daily load curve of step S11 gained is sampled, obtain the predicted load L of each k ∈ [1, J] in the period _b(k); Wherein J be in one day according to setting determination divide optimization time hop count;

2) when the charging pile having new electric automobile l to access in target area, read the inbound information of vehicle l, comprise network entry time T _{in, l}, electrokinetic cell capacity C _s,lwith initial state-of-charge (State of Charge, SOC) S _{0, l}deng;

3) user inputs the charge information of vehicle l: expect from net time T _{out, l}, expect state-of-charge S _e,l; The charge requirement that can meet user is judged in conjunction with described reading information and described user's input information, if can not, then inform that user inputs by User Interface incorrect, prompting user re-enters expection from net time or expectation SOC information, if user accepts prompting, and input can be implemented, correct charge information, then carry out step 4) ~ step 10) operation, otherwise, abandon this user, perform step 2);

4) the charge-discharge electric power model of electric automobile is built: P _l(k)=p _l(k) f _m,l(k);

In formula, P _lk () represents the charge-discharge electric power of vehicle l; p _lk () represents vehicle l and the system Power Exchange in the k period, p _lk () >0 represents that vehicle l is in charged state; p _lk () <0 represents and is in discharge condition; p _lk ()=0 expression is in floating charge state; f _m,lk (), for characterizing the operability of day part to Vehicular battery, expression formula is:

Wherein, T _m,lfor vehicle l accesses the duration T of electrical network _{pe, l}=T _{out, l}-T _{in, l}the period set comprised;

Suppose that the electric automobile power battery participating in scheduling is lithium battery, according to the discharge and recharge correlation properties of lithium battery, within the single period, lithium electricity can be considered it is invariable power discharge and recharge, and the relation of its state-of-charge and corresponding discharge and recharge time is characterized by:

S _l(k)＝S _l(k-1)+P _l(k)η(P _l(k))Δt/C _s,l(2)

In formula, S _l(k-1), S _lk () represents that vehicle l is at kth-1, the SOC of a k period respectively; η (P _l(k)) represent Power Exchange efficiency, concrete relevant with Power Exchange direction:

$\mathrm{\&eta;}\left(P_l\right(k\left)\right)=\left\{\begin{array}{cc}{\mathrm{\&eta;}}_c,& P_l\left(k\right)\&GreaterEqual;0,\\ 1/{\mathrm{\&eta;}}_d,& P_l\left(k\right)<0.\end{array}\right.---\left(3\right)$

Wherein, η _c, η _drepresent charge and discharge efficiency respectively;

5) read in from electric energy public service platform the total load information that vehicle l accesses moment target area

wherein, when representing vehicle l access, the distribution total load information in k session target region; represent electric automobile cluster load: wherein, M _l-1when representing that vehicle l accesses electrical network, complete the vehicle set of charging plan; This computational process is completed by electric energy public service platform;

6) virtual electricity price information is calculated, indirectly the load level of reflection target area:

In formula, when representing vehicle l access, the virtual electricity price of k period; with represent virtual bidding price adjustment coefficient; I _r,j, φ _r,jrepresent respectively with reference to electricity price and reference load value; [u] ⁺represent max{0, u}; represent prediction total load, wherein represent base load predicted value;

7) electric automobile discharge and recharge Optimal Operation Model is set up;

In conjunction with above step, to minimize virtual discharge and recharge cost for target, set up electric automobile discharge and recharge Optimal Operation Model to optimize the charge-discharge electric power of electric automobile, institute's established model is as follows:

$\min V_l=\underset{k\&Element;T_{m,l}}{\mathrm{\&Sigma;}}\left\{P_l\left(k\right){\mathrm{\&gamma;}}_l(L_a^l\left(k\right),k)\mathrm{\&Delta;t}\right\}---\left(6\right)$

s.t. S _min≤S _l(k)≤S _max(7)

-P _d≤P _l(k)≤P _c(8)

$S_{0,l}+\underset{k\&Element;T_{m,l}}{\&Sigma;}\mathrm{\&Delta;}t\frac{P_l\left(k\right)}{C_{s,l}}\&GreaterEqual;S_{E,l}---\left(9\right)$

$L_a^l\left(k\right)\&le;{\mathrm{\&kappa;}}_T{A}_T,\widehat{L_a^l\left(k\right)}\&le;{\mathrm{\&kappa;}}_T{A}_T---\left(10\right)$

k＝1,2,…,J.

T _pe,l>T _c,ll＝1,2,…,n (11)

In formula (6), V _lrepresent the virtual discharge and recharge cost of vehicle l; N _lrepresent T _m,lthe length of set; In formula (7), S _max, S _minrepresent maximum and the minimum value of the SOC allowed, prevent controlled vehicle overcharge and overdischarge; Formula (8) represents charge-discharge electric power constraint, P _lk () possesses continuously adjustable characteristic, but usually will be subject to the specified charge and discharge Power Limitation of electrokinetic cell or charger; Formula (9) represents charge requirement constraint, and when vehicle leaves according to appointment, the SOC of its battery need meet the expectation; Formula (10) indication transformer maximum load retrains, κ _tfor transformer efficiency, A _tfor the rated capacity of transformer; Formula (11) represents time relationship constraint, and n optimizes the access Fleet Size in the period, T _c,lfor charging to the shortest time expected needed for SOC: T _c,l=(S _e,l-S _{0, l}) C _s,l/ P _cη _c;

Solve above-mentioned Optimal Operation Model, complete the optimization of current access charging and discharging vehicle power, now, the discharge and recharge operation plan of vehicle l is: $P_l=\left(P_l\right(T_{m,l}^1),P_l\left(T_{m,l}^2\right),...,P_l\left(T_{m,l}^{N_l}\right)),$ Wherein, $T_{m,l}^i(i=1,2,...,N_l)$ Represent set T _m,lin i-th element;

8) electric automobile user cost calculation: U _l=(c _{cd, l}| η _c=η _d=1)+c _{bat, l}+ c _{loss, l};

In formula, U _lrepresent user cost; c _{bat, l}represent the service life of lithium battery loss conversion cost of vehicle l; c _{loss, l}represent energy loss expense; c _{cd, l}| η _c=η _d=1, the desirable discharge and recharge expense under charge and discharge efficiency term is not considered in expression: in formula, pri (k) represents electricity price information, and in the present invention, it is a kind of dynamic sharing electricity price, and namely peak, low ebb electricity price are fixed, and the electricity price of peak interval of time change, wherein, peak, low ebb electricity price are expressed as pri ^h, pri ^l; Utilize wavelet analysis and the division of fuzzy clustering method realization to tou power price peak interval of time:

S81. preliminary treatment: information on load is carried out the wavelet decomposition that yardstick is 3, by the high fdrequency component zero setting of one, two layer, obtains new information on load after reconstruct

S82. attribute characterization: adopt type bigger than normal and the trapezoidal Fuzzy Distribution of type less than normal half to carry out attribute characterization to the information on load after reconstruct, form attribute matrix A (a) _{j × 2}, computational methods are:

$\left\{\begin{array}{c}{a}_{k1}=\frac{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-L_a^{l*}\left(k\right)}{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}\\ a_{k2}=\frac{L_a^{l*}\left(k\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}{\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)-\underset{1\&le;k\&le;J}{\max }\left(L_a^{l*}\right(k\left)\right)}\end{array}\right.---\left(12\right)$

S83. translation-standard deviation conversion is carried out to matrix A, set up fuzzy similarity matrix R (r) with subtrabend's absolute value method _{j × J};

S84. continuous quadratic side is asked to similar matrix, i.e. R → R ²→ R ⁴→...→ R ²ⁱ→..., until occur ( represent fuzzy matrix composition computing), now, R ^kbe the transitive closure t (R) of this similar matrix;

S85. at transitive closure t (R)=(t _ij) in, make λ be t _ijin a certain value, ask for the λ-Level Matrix R of t (R) _ij, then, the descending value of λ just can formative dynamics cluster, gets suitable λ value, determines peak interval of time splitting scheme;

9) the autonomous Response Decision of electric automobile user;

Utilize step 7) described in the electric automobile charge-discharge electric power that obtains of Optimized model and step 8) the dynamic sharing electricity price that obtains determines the discharge and recharge cost of user, and discharge and recharge operation plan is informed user with corresponding situation of Profit, by user from main response charge mode, Response to selection operation plan or start unordered charging;

10) according to user's decision-making, discharge and recharge operation is implemented to electric automobile, and upload plan;

If user selects to start unordered charging, electrically-charging equipment provides lasting invariable power charging service for the EV of access, until meet the charge requirement of user or vehicle leaves; If user's Response to selection is dispatched, then according to operation plan P _lconcrete discharge and recharge operation is implemented to electric automobile, accordingly, just determines the discharge and recharge plan of vehicle l; This discharge and recharge plan is uploaded to electric energy public service platform, by electric energy public service platform, plan load is integrated, complete once to the real-time update of described target area information on load, and wait for that next electric automobile networks; If there is new vehicle access, then skip to step 2); Lasting execution terminates to optimizing the period by this process.