CN106096773A - A kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage - Google Patents

Abstract

The present invention relates to a kind of electric automobile and serve as the Multiobjective Optimal Operation method of energy storage, with containing electric automobile, wind-powered electricity generation, the micro-grid system of dc bus as research object, build meter and probabilistic electric automobile serve as the scheduling model of energy storage；Have employed many scenes generation technique and random process is decomposed into limited discrete probabilistic scene by scene reduction technology, use multi-agent system technology that Optimized Operation is divided into double-deck scheduling structure: micro-capacitance sensor dispatch layer and electric automobile dispatch layer.And Spot Price more New Policy is proposed on the basis of this scheduling structure, utilize bilayer scheduling that dispatching patcher is realized respectively: micro-grid system operating cost is minimum, and ensure exchange power in safe range；The charge-discharge electric power distributing each car makes the conversion of electric automobile power battery charging and discharging state minimum to ensure the longest two targets of battery cycle life.Can obviously reduce operating cost and the battery charging condition conversion times when serving as energy storage for the electric automobile of micro-grid system.

Description

A kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage

Technical field

The present invention relates to containing electric automobile as the micro-capacitance sensor Multiobjective Optimal Operation method of energy storage, particularly belong to Intelligent electric Network technology field.

Background technology

The system that micro-capacitance sensor is made up of distributed power source, energy storage, load, and have with major network and contact closely.With The progress of technology, the expanded utilization of regenerative resource such as wind-power electricity generation, it is possible not only to save the energy, it is also possible to avoid tradition electricity The electric energy long range propagation of net, reduces electric energy loss, reduces carbon emission simultaneously.And energy storage is possible not only to help and more efficiently uses Regenerative resource, it is also possible to the peak and low valley of regulation load.Showing according to statistics, each electric automobile averagely had at the time of 90% In idle state, so being considered mobile battery flexibly in microgrid, for multiple micro-capacitance sensor, substantial amounts of electricity Electrical automobile similarly is the energy storage device of movement, how to regulate the energy coordination between electric automobile and microgrid, microgrid group and major network Between the energy coordinate, particularly critical to the optimization realizing the targets such as micro-capacitance sensor operation cost is minimum, major network fluctuation is minimum, and it And major network between energy coordinated scheduling can avoid load peak.Therefore the Optimized Operation side of energy storage is served as to electric automobile Method is analyzed, and proposes on the premise of satisfied exchange power is in safe range, it is achieved " micro-grid system operating cost is Little, distribute the charge-discharge electric power of each car and make the conversion of electric automobile power battery charging and discharging state minimum to ensure circulating battery Two targets of longest-lived ".The Multiobjective Optimal Operation method of the charging electric vehicle energy storage that the present invention proposes can make micro-electricity Net system operation cost is minimum, makes batteries of electric automobile discharge and recharge transition status number of times minimum to ensure customer using cost simultaneously Less.

Utility model content

For above-mentioned the problems of the prior art, the present invention proposes the multiple-objection optimization side that a kind of electric automobile serves as energy storage Method, provides theoretical foundation and technical support for Model City's charging electric vehicle infrastructure construction, and is conducive to improving charging The economic benefit of overall operation in standing.

In order to solve above-mentioned technical problem, the utility model proposes techniques below scheme:

A kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage, and the method step is:

Step 1: obtain following one day load curve based on day preload and historical dataReceive wind power output big Little prediction dataScene probability τ_s；The maximum discharge power of electric automobile power batteryMaximum charge work( RateStored energy capacitance lower limitThe stored energy capacitance upper limitAnd use scenarios generation method and sight reduction method to subtract Few sight number；

Step 2: set up micro-capacitance sensor dispatch layer Mathematical Modeling, in its scheduling strategy, makes exchange power in the range of limiting (P^cap) solve the minimum operating cost (target 1) of micro-grid system, and draw exchange power now

Step 3: set up electric automobile dispatch layer economy and technology model, in its scheduling strategy so that electric automobile fills Discharge condition conversion times is minimum, and draws the minimum cost (target 2) of total electric automobile.In model, charging electric vehicle becomes This is proportionate with state conversion frequency；

Step 4: on the basis of multi-agent system technology, formulates electrical network electricity price more New Policy, it is assumed that the renewal time is 1h/ Secondary；

Step 5: the Spot Price that the response of micro-capacitance sensor each controller updates, again optimizing charge-discharge electric power makes micro-grid system Operating cost is minimum；

Step 6: electric automobile response micro-capacitance sensor up-to-date charge-discharge electric power distribution, corresponding its discharge and recharge behavior of renewal.Return Step 1.

Described scene generation technique uses discrete probability distribution to replace the uncertainty of stochastic variable, the generation of scene by Uncertain electric automobile trip custom constitutes (wind power output, load power consumption can be approximately considered and determine that value).Wind power output mould Type is as follows:

$P^W=\left\{\begin{array}{cc}0,& \begin{array}{ccc}{v}_f<v_{ci}& or& v_f>v_{co}\end{array}\\ \frac{v_f^3-v_{ci}^3}{v_r^3-v_{ci}^3}{P}^r,& v_{ci}\&le;v_f\&le;v_r\\ P^r,& v_r\&le;v_f\&le;v_{co}\end{array}\right.---\left(1\right)$

P^WFor the wind-power electricity generation power of prediction, v_fFor prediction of wind speed, v_ciFor incision wind speed, v_coFor cut-out wind speed, v_rFor volume Determine wind speed, P^rFor blower fan rated power；

Electric automobile trip model is mainly made up of variablees such as departure time, arrival time, distance travelleds, and formula is as follows:

$P_D\left(t_{d,i}\right)=\frac{t_{d,i}^{(v-2)/2}{e}^{-t_{d,i}/2}}{2^{v/2}\mathrm{\&Gamma;}(v/2)}---\left(2\right)$

$P_A\left(t_{ar}\right|t_{d,i})=\frac{1}{\sqrt{2{\mathrm{\&pi;\&sigma;}}_i^2}}{e}^{-\frac{{(t_{ar}-{\mathrm{\&mu;}}_i)}^2}{2{\mathrm{\&sigma;}}_i^2}}---\left(3\right)$

P (d)=(d₀+d)^-βexp(-d/α) (4)

P_D(t_d,i) it is automobile departure time probability distribution, t_d,iBeing the departure time of i-th time window, Δ t is the time Length of window；

P_A(t_ar|t_d,i) it is automobile arrival time probability distribution, t_arIt is the arrival time of i-th time window, σ_iIt is i-th The arrival time standard deviation of individual time window；μ_iIt is the arrival time expectation of i-th time window；

P (d) is every time distance travelled probability distribution, and d is distance travelled, d₀, α, β be index parameters, corresponding be often respectively Several 1.8th, the 20th, 1.25.

The collection of electric automobile and all scenes of wind-powered electricity generation shares S and represents, τ_sFor probability under scene s for the system:

τ_s=P_A(t_ar|t_d,i)·P_D(t_d,i)·P(d) (5)

Quick positive algorithm is finally used to cut down scene.

The maximum discharge power of described electric automobile power batteryMaximum charge powerStored energy capacitance Lower limitThe stored energy capacitance upper limitCan be obtained by following formula:

$P_{i,s}^{\min }\left(t\right)=-n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(6\right)$

$P_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(7\right)$

$B_{i,s}^{\min }\left(t\right)=\&lsqb;{\mathrm{\&Phi;}}_{i,s}(t+1){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(8\right)$

$B_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right)E---\left(9\right)$

n_i,s(t)=n_i,s(t-1)+||Γ_i,s(t)||₁-||Φ_i,s(t)||₁ (10)

n_i,sT () is t online discharge and recharge number of vehicles under s scene,For the maximum charge and discharge of single electric automobile Power；||Γ_i,s(t)||₁、||Φ_i,s(t)||₁(arrival is 1, no to be respectively i-th micro-capacitance sensor t arrival variable under s scene Be then 0), the variable that sets out (setting out is 1, is otherwise 0),State-of-charge during for setting out under s scene, Φ_i,s(t+1) it is t+1 The variable that sets out in moment, E is electrokinetic cell rated capacity.

Described micro-capacitance sensor dispatch layer Mathematical Modeling is as follows:

Object function micro-grid system operating cost is represented by:

$\underset{P_{i,s}^{ex}\left(t\right)}{min}\underset{s=1}{\overset{S}{\&Sigma;}}{\mathrm{\&tau;}}_s\underset{i=1}{\overset{I}{\&Sigma;}}\underset{t=1}{\overset{T}{\&Sigma;}}{C}_t{P}_{i,s}^{ex}\left(t\right)\mathrm{\&Delta;}t---\left(11\right)$

Corresponding constraints is:

Power-balance:

$P_{i,s}^W\left(t\right)+P_{i,s}^{ex}\left(t\right)=P_i^L\left(t\right)+P_{i,s}^B\left(t\right)---\left(12\right)$

Electrokinetic cell energy storage state updates:

$B_{i,s}\left(t\right)=B_{i,s}(t-1)+P_{i,s}^B\left(t\right)\mathrm{\&Delta;}t+{\mathrm{\&Delta;}}_{i,s}^B\left(t\right)---\left(13\right)$

Reached or left by electric automobile the energy storage energy variation causing:

${\mathrm{\&Delta;}}_{i,s}^B\left(t\right)=\&lsqb;{\mathrm{\&Gamma;}}_{i,s}\left(t\right){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(14\right)$

In the range of energy storage electricity is maintained at restriction:

$B_{i,s}^{\min }\left(t\right)\&le;B_{i,s}\left(t\right)\&le;B_{i,s}^{\max }\left(t\right)---\left(15\right)$

Charge-discharge electric power is in the range of limiting:

$P_{i,s}^{\min }\left(t\right)\&le;P_{i,s}\left(t\right)\&le;P_{i,s}^{\max }\left(t\right)---\left(16\right)$

I-th micro-capacitance sensor exchange power is in the range of limiting:

$-{\stackrel{\&OverBar;}{P}}_i^{ex}\&le;P_{i,s}^{ex}\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_i^{ex}---\left(17\right)$

Micro-grid system integrally exchanges power in the range of limiting:

$-P^{cap}\&le;\underset{i=1}{\overset{I}{\&Sigma;}}{P}_{i,s}^{ex}\left(t\right)\&le;P^{cap}---\left(18\right)$

B_i,s(t), P_i,sT () is respectively electrokinetic cell energy storage energy under s scene for i-th micro-capacitance sensor of t and charge and discharge Electrical power.By above-mentioned model, according to given, the minimum charging cost of micro-grid system can be solved, and draw exchange power now

Described electric automobile dispatch layer economy is respectively as follows: with technology model

Economic model is as object function:

$\min K_{i,s}^{total}=\underset{t=1}{\overset{T}{\&Sigma;}}{P}_{i,s}^B\left(t\right)\mathrm{\&Delta;}t\&CenterDot;C\left(t\right)+\underset{t=1}{\overset{T}{\&Sigma;}}\underset{j=1}{\overset{J_i}{\&Sigma;}}\frac{1}{2}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&CenterDot;\frac{c^b}{c^l}---\left(19\right)$

Part I represents electric cost, and Part II represents the cost depletions that battery charging and discharging causes, though totle drilling cost Minimum；C in formula^bFor batteries of electric automobile cost, c^lFor batteries of electric automobile cycle life；

Technology model is as constraints:

Jth automobile state-of-charge dynamically changes:

${\mathrm{soc}}_{i,j,s}(t+1)={\mathrm{soc}}_{i,j,s}\left(t\right)+\frac{\&lsqb;p_{i,j,s}^c\left(t\right)+p_{i,j,s}^d\left(t\right)\&rsqb;\mathrm{\&Delta;}t}{E},t\&Element;\&lsqb;t_{i,j,s}^a,t_{i,j,s}^d\&rsqb;---\left(20\right)$

In formulaIt is respectively arrival time and departure time,It is respectively the charging in the t period Power and discharge power；

State-of-charge when jth automobile sets out must is fulfilled for stroke institute subfam. Spiraeoideae:

${\mathrm{soc}}_{i,j,s}\left(t_{i,j,s}^d\right)\&GreaterEqual;\frac{\underset{m=1}{\overset{M}{\&Sigma;}}{d}_{i,j,s}^m}{kE}---\left(21\right)$

In formulaIt is the distance travelled that m plows；

Jth automobile charge and discharge power limit value:

$v_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}<p_{i,j,s}^d\left(t\right)<0,0<p_{i,j,s}^c\left(t\right)<u_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(22\right)$

Depth of discharge have to be larger than least restrictive state-of-charge:

soc＜ soc_i,j,s(t) ＜ 100% (23)

Total charge-discharge electric power of all automobiles is equal to the charge-discharge electric power sum of each automobile:

$\underset{j=1}{\overset{J_i}{\&Sigma;}}\left(p_{i,j,s}^c\right(t)+p_{i,j,s}^d(t\left)\right)=P_{i,s}^B\left(t\right)---\left(24\right)$

Total charge and discharge cycles number of times is necessarily less than the cycle-index upper limit:

$\frac{1}{2}\underset{t=1}{\overset{T}{\&Sigma;}}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&le;k---\left(25\right)$

Electric automobile can not discharge simultaneously and charge:

u_i,j,s(t)-v_i,j,s(t)≤1 (26)

Ensure that electric automobile can be only in unique charging and discharging state:

$\left\{\begin{array}{c}{u}_{i,j,s}^z\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\\ u_{i,j,s}^z\left(t\right)-v_{i,j,s}^{za}\left(t\right)\&le;1\\ u_{i,j,s}^{za}\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\end{array}\right.---\left(27\right)$

Assistance calculating discharge and recharge transition status:

$\left\{\begin{array}{c}{u}_{i,j,s}\left(t\right)-u_{i,j,s}(t-1)=u_{i,j,s}^z\left(t\right)+v_{i,j,s}^{za}\left(t\right)\\ v_{i,j,s}\left(t\right)-v_{i,j,s}(t-1)=v_{i,j,s}^z\left(t\right)+u_{i,j,s}^{za}\left(t\right)\end{array}\right.---\left(28\right)$

U in formula_i,j,s(t) (being 1 filling, be otherwise 0), v_i,j,sT () (is putting as-1, be otherwise 0),(turned by filling Putting is 1, is otherwise 0),(being filled for-1 by putting to turn, be otherwise 0),(being turned idle 1 by filling, be otherwise 0), (being left unused as-1 by putting to turn, be otherwise 0).

Described electrical network electricity price updates strategy and is expressed as follows:

Electricity price will be at lower a moment:

$p_k^{n+1}=\frac{\&part;C_k\left({P_{kL}}^n\right)}{\&part;{P_{kL}}^n}---\left(29\right)$

Load will be at lower a moment:

$P_{kL}^{n+1}=P_k\left(p^{n+1}\right)---\left(30\right)$

C_k(P_kL ⁿ)=a (P_kL ⁿ)²+b(P_kL ⁿ)+c (31)

A in formula, b, c value is the constant value setting according to practical operation situation,For subsequent time electricity price, Load for subsequent time.

Described exchange power is carried out on dc bus, it is not necessary to consider trend constraint.

Described Multiobjective Optimal Operation can be analyzed to two sub-target problems and is optimized respectively.

The utility model has the advantages that:

1st, the present invention is applicable in the micro-grid system containing wind generator system for the abundant all kinds of cities of wind energy, mainly to micro- Exchange power coordination scheduling between net and electric automobile are optimized as charge-discharge electric power scheduling during energy storage.

2nd, the electric automobile that the present invention proposes serve as the Multiobjective Optimal Operation method of energy storage effectively utilize scene to generate and Reduction method solves that electric automobile is gone on a journey at random, wind-force is exerted oneself, the instability problem of load.According to different Scene realizations to target Optimization.

3rd, the present invention uses MAS agency plant, specifically double-deck control system, and multi-objective problem is decomposed into two Sub-goal, realizes respectively in the upper strata of system with lower floor.Greatly simplify calculating process, reduce the complexity of calculating.

Brief description

The utility model is described in further detail with embodiment below in conjunction with the accompanying drawings.

Fig. 1 is the flow chart of the present invention.

Fig. 2 is the double-deck control architecture figure of Optimized Operation strategy.

Fig. 3 is micro-capacitance sensor dispatch layer control structure figure.

Table 1 is this strategy and centralized Control, the Contrast on effect table of decentralised control strategy based on tou power price.

Table 2 is that this strategy randomly draws 5 electric automobile discharge and recharge conversion times pair with when not using any control strategy Compare table.

Detailed description of the invention

Below in conjunction with the accompanying drawings embodiment of the present utility model is described further.

As Figure 1-3, a kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage, comprises the steps:

(1) following one day load curve is obtained based on day preload and historical dataReceive wind power output size Prediction dataScene probability τ_s；The maximum discharge power of electric automobile power batteryMaximum charge powerStored energy capacitance lower limitThe stored energy capacitance upper limitAnd use scenarios generation method and sight to cut down method minimizing Sight number；

(2) data receiving according to the first step and some typical scenes, set up micro-capacitance sensor dispatch layer Mathematical Modeling, In its scheduling strategy, make exchange power (P in the range of limiting^cap) solve the minimum operating cost (target 1) of micro-grid system, And draw exchange power now

(3) according to exchange power obtained in the previous stepCalculate the total charge-discharge electric power of electric automobileSet up electronic Truck dispartching layer economy and technology model, in its scheduling strategy so that electric automobile charging and discharging state conversion times is minimum, and Draw the minimum cost of total electric automobile(target 2).In model charging electric vehicle cost and state conversion frequency in Positive correlation；

(4) according to second and third step on the basis of multi-agent system technology, electrical network electricity price more New Policy is formulated, it is assumed that more The new time is 1h/ time；

(5) respond, according to each controller of micro-capacitance sensor obtained in the previous step, the Spot Price updating, again optimize charge and discharge electric work Rate makes micro-grid system operating cost minimum；

(6) electric automobile response micro-capacitance sensor up-to-date charge-discharge electric power distribution, corresponding its discharge and recharge behavior of renewal.Return step 1。

Below in conjunction with accompanying drawing, elaborating embodiments of the invention, the flow chart of the present invention is as shown in Figure 1.

Wind power output model is as follows:

$P^W=\left\{\begin{array}{cc}0,& \begin{array}{ccc}{v}_f<v_{ci}& or& v_f>v_{co}\end{array}\\ \frac{v_f^3-v_{ci}^3}{v_r^3-v_{ci}^3}{P}^r,& v_{ci}\&le;v_f\&le;v_r\\ P^r,& v_r\&le;v_f\&le;v_{co}\end{array}\right.---\left(1\right)$

P^WFor the wind-power electricity generation power of prediction, v_fFor prediction of wind speed, v_ciFor incision wind speed, v_coFor cut-out wind speed, P^rFor wind Machine rated power；

Electric automobile trip model is mainly made up of variablees such as departure time, arrival time, distance travelleds, its discrete probabilistic Distribution collection formula is as follows:

$P_D\left(t_{d,i}\right)=\frac{t_{d,i}^{(v-2)/2}{e}^{-t_{d,i}/2}}{2^{v/2}\mathrm{\&Gamma;}(v/2)}---\left(2\right)$

$P_A\left(t_{ar}\right|t_{d,i})=\frac{1}{\sqrt{2{\mathrm{\&pi;\&sigma;}}_i^2}}{e}^{-\frac{{(t_{ar}-{\mathrm{\&mu;}}_i)}^2}{2{\mathrm{\&sigma;}}_i^2}}---\left(3\right)$

P (d)=(d₀+d)^-βexp(-d/α) (4)

P_D(t_d,i) it is automobile departure time probability distribution, t_d,iBeing the departure time of i-th time window, Δ t is the time Length of window；

P_A(t_ar|t_d,i) it is automobile arrival time probability distribution, t_arIt is the arrival time of i-th time window, μ_iFor averagely Arrival time；

P (d) is every time distance travelled probability distribution, and d is distance travelled, d₀, α, β be constant, other parameters all definables.

The collection of electric automobile and all scenes of wind-powered electricity generation shares S and represents, τ_sFor probability under scene s for the system:

τ_s=P_A(t_ar|t_d,i)·P_D(t_d,i)·P(d) (5)

Quick positive algorithm is finally used to cut down scene.

In the first step, the maximum discharge power of described electric automobile power batteryMaximum charge powerStored energy capacitance lower limitThe stored energy capacitance upper limitCan be obtained by following formula:

$P_{i,s}^{\min }\left(t\right)=-n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(6\right)$

$P_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(7\right)$

$B_{i,s}^{\min }\left(t\right)=\&lsqb;{\mathrm{\&Phi;}}_{i,s}(t+1){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(8\right)$

$B_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right)E---\left(9\right)$

n_i,s(t)=n_i,s(t-1)+||Γ_i,s(t)||₁-||Φ_i,s(t)||₁ (10)

n_i,sT () is t online discharge and recharge number of vehicles under s scene, | | Γ_i,s(t)||₁、||Φ_i,s(t)||₁Respectively Reach variable (arrival is 1, is otherwise 0), the variable that sets out (setting out is 1, is otherwise 0) for i-th micro-capacitance sensor t under s scene,State-of-charge during for setting out under s scene, Φ_i,s(t+1) being the variable that sets out in t+1 moment, E is the specified appearance of electrokinetic cell Amount.

Micro-capacitance sensor dispatch layer Mathematical Modeling is as follows:

Object function micro-grid system operating cost is represented by:

$\underset{P_{i,s}^{ex}\left(t\right)}{min}\underset{s=1}{\overset{S}{\&Sigma;}}{\mathrm{\&tau;}}_s\underset{i=1}{\overset{I}{\&Sigma;}}\underset{t=1}{\overset{T}{\&Sigma;}}{C}_t{P}_{i,s}^{ex}\left(t\right)\mathrm{\&Delta;}t---\left(11\right)$

Corresponding constraints is:

Power-balance:

$P_{i,s}^W\left(t\right)+P_{i,s}^{ex}\left(t\right)=P_i^L\left(t\right)+P_{i,s}^B\left(t\right)---\left(12\right)$

Electrokinetic cell energy storage state updates:

$B_{i,s}\left(t\right)=B_{i,s}(t-1)+P_{i,s}^B\left(t\right)\mathrm{\&Delta;}t+{\mathrm{\&Delta;}}_{i,s}^B\left(t\right)---\left(13\right)$

Reached or left by electric automobile the energy storage energy variation causing:

${\mathrm{\&Delta;}}_{i,s}^B\left(t\right)=\&lsqb;{\mathrm{\&Gamma;}}_{i,s}\left(t\right){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(14\right)$

In the range of energy storage electricity is maintained at restriction:

$B_{i,s}^{\min }\left(t\right)\&le;B_{i,s}\left(t\right)\&le;B_{i,s}^{\max }\left(t\right)---\left(15\right)$

Charge-discharge electric power is in the range of limiting:

$P_{i,s}^{\min }\left(t\right)\&le;P_{i,s}\left(t\right)\&le;P_{i,s}^{\max }\left(t\right)---\left(16\right)$

I-th micro-capacitance sensor exchange power is in the range of limiting:

$-{\stackrel{\&OverBar;}{P}}_i^{ex}\&le;P_{i,s}^{ex}\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_i^{ex}---\left(17\right)$

Micro-grid system integrally exchanges power in the range of limiting:

$-P^{cap}\&le;\underset{i=1}{\overset{I}{\&Sigma;}}{P}_{i,s}^{ex}\left(t\right)\&le;P^{cap}---\left(18\right)$

B_i,s(t), P_i,sT () is respectively electrokinetic cell energy storage energy under s scene for i-th micro-capacitance sensor of t and charge and discharge Electrical power.By above-mentioned model, according to given, the minimum charging cost of micro-grid system can be solved, and draw exchange power now

Electric automobile dispatch layer economy is respectively as follows: with technology model

Economic model is as object function:

$\min K_{i,s}^{total}=\underset{t=1}{\overset{T}{\&Sigma;}}{P}_{i,s}^B\left(t\right)\mathrm{\&Delta;}t\&CenterDot;C\left(t\right)+\underset{t=1}{\overset{T}{\&Sigma;}}\underset{j=1}{\overset{J_i}{\&Sigma;}}\frac{1}{2}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&CenterDot;\frac{c^b}{c^l}---\left(19\right)$

Part I represents electric cost, and Part II represents the cost depletions that battery charging and discharging causes, though totle drilling cost Minimum；

Technology model is as constraints (20)-(28):

Jth automobile state-of-charge dynamically changes:

${\mathrm{soc}}_{i,j,s}(t+1)={\mathrm{soc}}_{i,j,s}\left(t\right)+\frac{\&lsqb;p_{i,j,s}^c\left(t\right)+p_{i,j,s}^d\left(t\right)\&rsqb;\mathrm{\&Delta;}t}{E},t\&Element;\&lsqb;t_{i,j,s}^a,t_{i,j,s}^d\&rsqb;---\left(20\right)$

In formulaIt is respectively arrival time and departure time,It is respectively the charging in the t period Power and discharge power；

State-of-charge when jth automobile sets out must is fulfilled for stroke institute subfam. Spiraeoideae:

${\mathrm{soc}}_{i,j,s}\left(t_{i,j,s}^d\right)\&GreaterEqual;\frac{\underset{m=1}{\overset{M}{\&Sigma;}}{d}_{i,j,s}^m}{kE}---\left(21\right)$

In formulaIt is the distance travelled that m plows；

Jth automobile charge and discharge power limit value:

$v_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}<p_{i,j,s}^d\left(t\right)<0,0<p_{i,j,s}^c\left(t\right)<u_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(22\right)$

Depth of discharge have to be larger than least restrictive state-of-charge:

soc＜ soc_i,j,s(t) ＜ 100% (23)

Total charge-discharge electric power of all automobiles is equal to the charge-discharge electric power sum of each automobile:

$\underset{j=1}{\overset{J_i}{\&Sigma;}}\left(p_{i,j,s}^c\right(t)+p_{i,j,s}^d(t\left)\right)=P_{i,s}^B\left(t\right)---\left(24\right)$

Total charge and discharge cycles number of times is necessarily less than the cycle-index upper limit:

$\frac{1}{2}\underset{t=1}{\overset{T}{\&Sigma;}}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&le;k---\left(25\right)$

Electric automobile can not discharge simultaneously and charge:

u_i,j,s(t)-v_i,j,s(t)≤1 (26)

Ensure that electric automobile can be only in unique charging and discharging state:

$\left\{\begin{array}{c}{u}_{i,j,s}^z\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\\ u_{i,j,s}^z\left(t\right)-v_{i,j,s}^{za}\left(t\right)\&le;1\\ u_{i,j,s}^{za}\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\end{array}\right.---\left(27\right)$

Assistance calculating discharge and recharge transition status:

$\left\{\begin{array}{c}{u}_{i,j,s}\left(t\right)-u_{i,j,s}(t-1)=u_{i,j,s}^z\left(t\right)+v_{i,j,s}^{za}\left(t\right)\\ v_{i,j,s}\left(t\right)-v_{i,j,s}(t-1)=v_{i,j,s}^z\left(t\right)+u_{i,j,s}^{za}\left(t\right)\end{array}\right.---\left(28\right)$

U in formula_i,j,s(t) (being 1 filling, be otherwise 0), v_i,j,sT () (is putting as-1, be otherwise 0),(turned by filling Putting is 1, is otherwise 0),(being filled for-1 by putting to turn, be otherwise 0),(being turned idle 1 by filling, be otherwise 0), (being left unused as-1 by putting to turn, be otherwise 0).

Electrical network electricity price updates strategy and is expressed as follows:

Electricity price will be at lower a moment:

$p_k^{n+1}=\frac{\&part;C_k\left({P_{kL}}^n\right)}{\&part;{P_{kL}}^n}---\left(29\right)$

Load will be at lower a moment:

$P_{kL}^{n+1}=P_k\left(p^{n+1}\right)---\left(30\right)$

C_k(P_kL ⁿ)=a (P_kL ⁿ)²+b(P_kL ⁿ)+c (31)

A in formula, b, c value is setting,For subsequent time electricity price,Load for subsequent time.

According to above method, input identical initial parameter, available as table the 1st, 2 contrast effect table, as seen from the table, The micro-grid system operating cost of this method and load peak are all that minimum, electric automobile discharge and recharge conversion times is obviously reduced.

In sum, it is proposed that electric automobile serve as the Multiobjective Optimal Operation method of energy storage, have employed scene raw Becoming, cutting down technology and multi-agent system technology, in micro-grid system, electric automobile achieves micro-in the case of serving as energy storage Network system operating cost is minimum, electric automobile user's discharge and recharge cost minimization (i.e. battery charging and discharging transition status number of times is minimum) Target.

It should be noted that any process described otherwise above or method describe and can be managed in flow chart or at this Xie Wei, represents the code of the executable instruction including one or more step for realizing specific logical function or process Module, fragment or part, and the scope of the preferred embodiments of the invention includes other realization, wherein can not press shown Or the order discussing, including in the way of involved function is while basic or in the opposite order, performing function, this should be by Embodiments of the invention person of ordinary skill in the field understood.

1 strategy of table and centralized Control, the Contrast on effect of decentralised control strategy based on tou power price

2 strategies of table with randomly draw the contrast of 5 electric automobile discharge and recharge conversion times when not using any control strategy

Number of times	Use this strategy discharge and recharge conversion times	Do not use any strategy discharge and recharge conversion times
			EV1	1	3
EV2	2	5
			EV3	1	2
EV4	1	3
			EV5	2	6

General principle, principal character and the advantage of the present invention have more than been shown and described.The technical staff of the industry should Understanding, the present invention is not restricted to the described embodiments, and the simply explanation present invention's described in above-described embodiment and specification is former Reason, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, these changes and improvements Both fall within scope of the claimed invention.Claimed scope is by appending claims and equivalent circle thereof Fixed.

Claims (8)

1. an electric automobile serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that: the method step is:

Step 1: obtain following one day load curve based on day preload and historical dataReceive the pre-of wind power output size Survey dataScene probability τ_s；The maximum discharge power of electric automobile power batteryMaximum charge powerStored energy capacitance lower limitThe stored energy capacitance upper limitAnd use scenarios generation method and sight to cut down method minimizing Sight number；

Step 2: set up micro-capacitance sensor dispatch layer Mathematical Modeling, in its scheduling strategy, makes exchange power (P in the range of limiting^cap) Solve the minimum operating cost (target 1) of micro-grid system, and draw exchange power now

Step 3: set up electric automobile dispatch layer economy and technology model, in its scheduling strategy so that electric automobile discharge and recharge State conversion frequency is minimum, and draws the minimum cost (target 2) of total electric automobile.In model charging electric vehicle cost with State conversion frequency is proportionate；

Step 4: on the basis of multi-agent system technology, formulates electrical network electricity price more New Policy, it is assumed that the renewal time is 1h/ time；

Step 5: the Spot Price that the response of micro-capacitance sensor each controller updates, again optimizing charge-discharge electric power makes micro-grid system run Cost minimization；

Step 6: electric automobile response micro-capacitance sensor up-to-date charge-discharge electric power distribution, corresponding its discharge and recharge behavior of renewal.Return step 1。

2. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that:

Described scene generation technique uses discrete probability distribution to replace the uncertainty of stochastic variable, and the generation of scene is not by true Fixed electric automobile trip custom constitutes (wind power output, load power consumption can be approximately considered and determine that value).Wind power output model is such as Under:

$P^W=\left\{\begin{array}{cc}0,& v_f<v_{ci}or{v}_f>v_{co}\\ \frac{v_f^3-v_{ci}^3}{v_r^3-v_{ci}^3}{P}^r,& v_{ci}\&le;v_f\&le;v_r\\ P^r,& v_r\&le;v_f\&le;v_{co}\end{array}\right.---\left(1\right)$

P^WFor the wind-power electricity generation power of prediction, v_fFor prediction of wind speed, v_ciFor incision wind speed, v_coFor cut-out wind speed, v_rFor specified wind Speed, P^rFor blower fan rated power；

Electric automobile trip model is mainly made up of variablees such as departure time, arrival time, distance travelleds, and formula is as follows:

$P_D\left(t_{d,i}\right)=\frac{t_{d,i}^{\left(v-2\right)/2}{e}^{-t_{d,i}/2}}{2^{v/2}\mathrm{\&Gamma;}(v/2)}---\left(2\right)$

$P_A\left(t_{ar}\right|t_{d,i})=\frac{1}{\sqrt{2{\mathrm{\&pi;\&sigma;}}_i^2}}{e}^{-\frac{{(t_{ar}-{\mathrm{\&mu;}}_i)}^2}{2{\mathrm{\&sigma;}}_i^2}}---\left(3\right)$

P (d)=(d₀+d)^-βexp(-d/α) (4)

P_D(t_d,i) it is automobile departure time probability distribution, t_d,iBeing the departure time of i-th time window, Δ t is that time window is long Degree；

P_A(t_ar|t_d,i) it is automobile arrival time probability distribution, t_arIt is the arrival time of i-th time window, σ_iWhen being i-th Between the arrival time standard deviation of window；μ_iIt is the arrival time expectation of i-th time window；

P (d) is every time distance travelled probability distribution, and d is distance travelled, d₀, α, β be index parameters, corresponding be respectively constant the 1.8th, 20、1.25。

The collection of electric automobile and all scenes of wind-powered electricity generation shares S and represents, τ_sFor probability under scene s for the system:

τ_s=P_A(t_ar|t_d,i)·P_D(t_d,i)·P(d) (5)

Quick positive algorithm is finally used to cut down scene.

3. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that The maximum discharge power of described electric automobile power batteryMaximum charge powerStored energy capacitance lower limitThe stored energy capacitance upper limitCan be obtained by following formula:

$P_{i,s}^{\min }\left(t\right)=-n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(6\right)$

$P_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(7\right)$

$B_{i,s}^{\min }\left(t\right)=\&lsqb;{\mathrm{\&Phi;}}_{i,s}(t+1){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(8\right)$

$B_{i,s}^{\max }\left(t\right)=n_{i,s}\left(t\right)E---\left(9\right)$

n_i,s(t)=n_i,s(t-1)+||Γ_i,s(t)||₁-||Φ_i,s(t)||₁ (10)

n_i,sT () is t online discharge and recharge number of vehicles under s scene,For the maximum charge and discharge power of single electric automobile； ||Γ_i,s(t)||₁、||Φ_i,s(t)||₁(arrival is 1, is otherwise to be respectively i-th micro-capacitance sensor t arrival variable under s scene 0), set out variable (setting out is 1, is otherwise 0),State-of-charge during for setting out under s scene, Φ_i,s(t+1) it is the t+1 moment The variable that sets out, E is electrokinetic cell rated capacity.

4. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that Described micro-capacitance sensor dispatch layer Mathematical Modeling is as follows:

Object function micro-grid system operating cost is represented by:

$\underset{P_{i,s}^{ex}\left(t\right)}{\min }\underset{s=1}{\overset{S}{\&Sigma;}}{\mathrm{\&tau;}}_s\underset{i=1}{\overset{I}{\&Sigma;}}\underset{t=1}{\overset{T}{\&Sigma;}}{C}_t{P}_{i,s}^{ex}\left(t\right)\mathrm{\&Delta;}t---\left(11\right)$

Corresponding constraints is:

Power-balance:

$P_{i,s}^W\left(t\right)+P_{i,s}^{ex}\left(t\right)=P_i^L\left(t\right)+P_{i,s}^B\left(t\right)---\left(12\right)$

Electrokinetic cell energy storage state updates:

$B_{i,s}\left(t\right)=B_{i,s}(t-1)+P_{i,s}^B\left(t\right)\mathrm{\&Delta;}t+{\mathrm{\&Delta;}}_{i,s}^B\left(t\right)---\left(13\right)$

Reached or left by electric automobile the energy storage energy variation causing:

${\mathrm{\&Delta;}}_{i,s}^B\left(t\right)=\&lsqb;{\mathrm{\&Gamma;}}_{i,s}\left(t\right){\mathrm{soc}}_{i,s}^0\&rsqb;\&CenterDot;E---\left(14\right)$

In the range of energy storage electricity is maintained at restriction:

$B_{i,s}^{\min }\left(t\right)\&le;B_{i,s}\left(t\right)\&le;B_{i,s}^{\max }\left(t\right)---\left(15\right)$

Charge-discharge electric power is in the range of limiting:

$P_{i,s}^{\min }\left(t\right)\&le;P_{i,s}\left(t\right)\&le;P_{i,s}^{\max }\left(t\right)---\left(16\right)$

I-th micro-capacitance sensor exchange power is in the range of limiting:

$-{\stackrel{\&OverBar;}{P}}_i^{ex}\&le;P_{i,s}^{ex}\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_i^{ex}---\left(17\right)$

Micro-grid system integrally exchanges power in the range of limiting:

$-P^{cap}\&le;\underset{i=1}{\overset{I}{\&Sigma;}}{P}_{i,s}^{ex}\left(t\right)\&le;P^{cap}---\left(18\right)$

B_i,s(t), P_i,sT () is respectively electrokinetic cell energy storage energy under s scene for i-th micro-capacitance sensor of t and charge and discharge electric work Rate.By above-mentioned model, according to given, the minimum charging cost of micro-grid system can be solved, and draw exchange power now

5. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that Described electric automobile dispatch layer economy is respectively as follows: with technology model

Economic model is as object function:

$\min K_{i,s}^{total}=\underset{t=1}{\overset{T}{\&Sigma;}}{P}_{i,s}^B\left(t\right)\mathrm{\&Delta;}t\&CenterDot;C\left(t\right)+\underset{t=1}{\overset{T}{\&Sigma;}}\underset{j=1}{\overset{J_i}{\&Sigma;}}\frac{1}{2}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&CenterDot;\frac{c^b}{c^l}---\left(19\right)$

Part I represents electric cost, and Part II represents the cost depletions that battery charging and discharging causes, even if totle drilling cost is minimum； C in formula^bFor batteries of electric automobile cost, c^lFor batteries of electric automobile cycle life；

Technology model is as constraints:

Jth automobile state-of-charge dynamically changes:

${\mathrm{soc}}_{i,j,s}(t+1)={\mathrm{soc}}_{i,j,s}\left(t\right)+\frac{\&lsqb;p_{i,j,s}^c\left(t\right)+p_{i,j,s}^d\left(t\right)\&rsqb;\mathrm{\&Delta;}t}{E},t\&Element;\&lsqb;t_{i,j,s}^a,t_{i,j,s}^d\&rsqb;---\left(20\right)$

In formulaIt is respectively arrival time and departure time,It is respectively the charge power in the t period And discharge power；

State-of-charge when jth automobile sets out must is fulfilled for stroke institute subfam. Spiraeoideae:

${\mathrm{soc}}_{i,j,s}\left(t_{i,j,s}^d\right)\&GreaterEqual;\frac{\underset{m=1}{\overset{M}{\&Sigma;}}{d}_{i,j,s}^m}{kE}---\left(21\right)$

In formulaIt is the distance travelled that m plows；

Jth automobile charge and discharge power limit value:

$v_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}<p_{i,j,s}^d\left(t\right)<0,0<p_{i,j,s}^c\left(t\right)<u_{i,j,s}\left(t\right){\stackrel{\&OverBar;}{p}}_{ev}---\left(22\right)$

Depth of discharge have to be larger than least restrictive state-of-charge:

soc＜ soc_i,j,s(t) ＜ 100% (23)

Total charge-discharge electric power of all automobiles is equal to the charge-discharge electric power sum of each automobile:

$\underset{j=1}{\overset{J_i}{\&Sigma;}}\left(p_{i,j,s}^c\right(t)+p_{i,j,s}^d(t\left)\right)=P_{i,s}^B\left(t\right)---\left(24\right)$

Total charge and discharge cycles number of times is necessarily less than the cycle-index upper limit:

$\frac{1}{2}\underset{t=1}{\overset{T}{\&Sigma;}}\left(u_{i,j,s}^z\right(t)-v_{i,j,s}^z(t\left)\right)\&le;k---\left(25\right)$

Electric automobile can not discharge simultaneously and charge:

u_i,j,s(t)-v_i,j,s(t)≤1 (26)

Ensure that electric automobile can be only in unique charging and discharging state:

$\left\{\begin{array}{c}{u}_{i,j,s}^z\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\\ u_{i,j,s}^z\left(t\right)-v_{i,j,s}^{za}\left(t\right)\&le;1\\ u_{i,j,s}^{za}\left(t\right)-v_{i,j,s}^z\left(t\right)\&le;1\end{array}\right.---\left(27\right)$

Assistance calculating discharge and recharge transition status:

$\left\{\begin{array}{c}{u}_{i,j,s}\left(t\right)-u_{i,j,s}(t-1)=u_{i,j,s}^z\left(t\right)+v_{i,j,s}^{za}\left(t\right)\\ v_{i,j,s}\left(t\right)-v_{i,j,s}(t-1)=v_{i,j,s}^z\left(t\right)+u_{i,j,s}^{za}\left(t\right)\end{array}\right.---\left(28\right)$

U in formula_i,j,s(t) (being 1 filling, be otherwise 0), v_i,j,sT () (is putting as-1, be otherwise 0),(by fill relay for 1, be otherwise 0),(being filled for-1 by putting to turn, be otherwise 0),(being turned idle 1 by filling, be otherwise 0),(by Putting to turn leaves unused as-1, is otherwise 0).

6. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that Described electrical network electricity price updates strategy and is expressed as follows:

Electricity price will be at lower a moment:

$p_k^{n+1}=\frac{\&part;C_k\left({P_{kL}}^n\right)}{\&part;{P_{kL}}^n}---\left(29\right)$

Load will be at lower a moment:

$P_{kL}^{n+1}=P_k\left(p^{n+1}\right)---\left(30\right)$

C_k(P_kL ⁿ)=a (P_kL ⁿ)²+b(P_kL ⁿ)+c (31)

A in formula, b, c value is the constant value setting according to practical operation situation,For subsequent time electricity price,For next The load in moment.

7. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that: Described exchange power is carried out on dc bus, it is not necessary to consider trend constraint.

8. a kind of electric automobile according to claim 1 serves as the Multiobjective Optimal Operation method of energy storage, it is characterised in that: Described Multiobjective Optimal Operation can be analyzed to two sub-target problems and is optimized respectively.