CN103927588A - Hybrid energy storage power station capacity determination method for stabilizing wind power fluctuations - Google Patents

Abstract

The invention discloses a hybrid energy storage power station capacity determination method for stabilizing wind power fluctuations. The method comprises the steps that a hybrid energy storage power station charge-discharge process model is built with the goal of reducing storage battery charge-discharge frequency or reducing capacitance; a hybrid energy storage power station capacity optimized objective function is built with the goal of minimizing the sum of the building cost and the running cost of a hybrid energy storage power station; on the condition that the requirement of the hybrid energy storage power station charge-discharge process model is met, the PSO algorithm is adopted for solution and calculation of the hybrid energy storage power station capacity optimized objective function, and the optimal capacitance value of the hybrid energy storage power station is determined. According to the hybrid energy storage power station capacity determination method for stabilizing the wind power fluctuations, the hybrid energy storage power station is built based on a typical power type energy storage VRLA and an energy type energy storage UC, the charge-discharge process model with the complementary advantages is built by means of the features of the typical power type energy storage VRLA and the energy type energy storage UC, and coordinating and organic running of media of a hybrid energy storage system can be achieved; a capacity planning model is built with the goal of minimizing the running cost and the building cost of the energy storage power station, and the capacity is optimized.

Description

Determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation

Technical field

The present invention relates to power swing and stabilize field, relate in particular to and a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation.

Background technology

Along with the lifting of regenerative resource permeability, its undulatory property has been brought huge challenge to the safe and reliable operation of electrical network.Energy-accumulating power station, because of its characteristic that charges and discharge to energy, becomes level and smooth renewable energy source power, overcomes the important way of its undulatory property.One of key issue of wind energy turbine set configuration energy storage is, in the face of the restricting relation between smooth effect and input cost, how to coordinate to determine stored energy capacitance, makes limited capacity meet validity and the economy of accumulator system operation simultaneously.Visible, the capacity optimization of energy-accumulating power station is the important content of wind energy turbine set energy storage configuring.

Actually rare for the research of hybrid energy-storing station capacity planning problem at present.

VRLA belongs to energy type energy storage device, and output power variation range is little, and speed is slow and to discharge and recharge number of times few, and cost is low simultaneously, can be used to compensate the trend component that in wind power, fluctuation is mild, energy is larger; UC possesses and frequently discharges and recharges handoff response ability, can discharge and recharge number of times high, is applicable to present the frequent fluctuation of the random component of variation characteristic fast and stabilizes, simultaneously many than VRLA costliness of UC cost at present.As can be seen here, the operation in hybrid energy-storing power station should be brought into play UC and frequently charge and discharge characteristic and limit its capacity simultaneously, and it is played a role in charging and discharging interval at small size energy; Should effectively avoid charging and discharging state frequent transitions for VRLA, and suitably promote its relative capacity, make its amplitude energy discharge and recharge interval in work.

Summary of the invention

Object of the present invention is exactly in order to address the above problem, and has proposed a kind of energy-accumulating power station method for planning capacity of dynamically adjusting based on state-of-charge, and the method has realized the stored energy capacitance optimization of taking dispatching requirement, storage energy operation life-span and economy into account.

To achieve these goals, the present invention adopts following technical scheme:

Determine a method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, comprise the following steps:

(1) to reduce accumulator cell charging and discharging number of times or to reduce capacitance setting up hybrid energy-storing power station charge and discharge process model as target.

(2) build hybrid energy-storing station capacity optimization aim function taking hybrid energy-storing power plant construction cost and operating cost summation minimum as target, and set up hybrid energy-storing power station and discharge and recharge constraint condition and the capacity-constrained condition of power.

(3) meeting under the condition of hybrid energy-storing power station charge and discharge process model, selecting PSO algorithm to solve calculating to hybrid energy-storing station capacity optimization aim function, determining the optimum capacity numerical value in hybrid energy-storing power station.

In described step (1) taking reduce accumulator cell charging and discharging number of times as the hybrid energy-storing power station charge and discharge process model of target as:

If P (t) is t moment wind-powered electricity generation unit output power P _w(t) with period reference output power P _ref(t) difference:

P(t)=P _W(t)-P _ref(t)

P (t) is decomposed into high fdrequency component P _{Δ H}and trend component P (t) _{Δ L}(t), and meet:

P(t)=P _ΔH(t)+P _ΔL(t)

If f (t) is P _{Δ L}(t) with null value be related to zone bit and sign and the P of f (t) _{Δ L}(t) identical, in the time meeting f (t-1) f (t) <0:

If f (t) >0 and Δ t _f< Δ t _thr, S _bat.VRLA=-1, P _discha.VRLA=0.

If f (t) <0 and Δ t _f< Δ t _thr, S _bat.VRLA=1, P _charge.VRLA=0.

In formula, f (t-1) f (t) <0 represents that P Δ L (t) and null value relation change; Δ t _ffor the duration of f (t) maintenance current state; Δ t _thrfor duration threshold value; P _diacha.VRLArepresent battery discharging power, P _charge.VRLArepresent charge in batteries power; S _bat.VRLAfor charge in batteries, electric discharge and floating charge state mark, corresponding numerical value 1 ,-1,0 respectively.

In described step (1) taking reduce capacitance as the hybrid energy-storing power station charge and discharge process model of target as:

If P (t) is t moment wind-powered electricity generation unit output power P _w(t) with period reference output power P _ref(t) difference:

P(t)=P _W(t)-P _ref(t)

P (t) is decomposed into high fdrequency component P _{Δ H}and trend component P (t) _{Δ L}(t), and meet:

P(t)=P _ΔH(t)+P _ΔL(t)

P _{discha/charge.UC}=P _ΔH(t)·k

P _{discha/charge.VRLA}=P _ΔL(t)+P _ΔH(t)·(1-k)。

In formula, k is power reduction coefficient, for interval (0,1] positive number, for ensureing that electric capacity fills-puts-the cyclic balance state that fills in what continues, needs to ensure k>0; P _charge.UCand P _discha.UCbe respectively the charging and discharging power of electric capacity; P _charge.VRLAand P _discha.VRLArepresent respectively the charging and discharging power of accumulator.

The control strategy of described hybrid energy-storing power station charge and discharge process model is:

(I) works as S _bat.VRLA=1 and S _bat.UC=1, and accumulator times power charges to SOC _vRLA>=SOC _vRLA.maxtime, k=1; Now:

If a. SOC _uC>=SOC _uC.max, accumulator system completely fills, and occurs abandoning wind, abandons wind power to be:

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)>0。

If b. P _charge.VRLA>P _{charge.VRLA.max}, electric capacity promotes the charging of k value, works as P _{Δ H}+ P _{Δ L}>P _{charge.VRLA.max}+

P _{charge.UC.max}time, there is abandoning wind, abandon wind power and be:

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.VRLA.max}-P _{charge.UC.max}。

(II) works as S _bat.VRLA=-1 and S _bat.UC=1, P _discha.VRLAsubtract power discharge, and P _{discha.VRLA.min}=0 o'clock,

If a. P _discha.VRLA=P _{discha.VRLA.min}, UC promotes the charging of k value, works as SOC _uC>SOC _uC.maxtime, there is abandoning wind, abandon wind power and be:

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)<0。

If b. UC charge power exceedes P _{charge.UC.max},, there is abandoning wind in charge power deficiency, abandons wind power to be:

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.UC.max}。

Wherein, S _bat.VRLAand S _bat.UCbe respectively charging, electric discharge and the floating charge state mark of accumulator and electric capacity, respectively corresponding numerical value 1 ,-1,0; SOC _vRLA.maxand SOC _uC.maxbe respectively the SOC operation maximal value of VRLA and UC; P _charge.UCand P _discha.UCbe respectively the charging and discharging power of electric capacity; P _charge.VRLAand P _discha.VRLArepresent respectively the charging and discharging power of accumulator; P _{charge.UC.max}for the maximum charge power of accumulator; P _{charge.VRLA.max}and P _{discha.VRLA.min}represent respectively maximum charge power and the minimum discharge power of accumulator.

The control strategy of described hybrid energy-storing power station charge and discharge process model also comprises:

(I) works as S _bat.VRLA=1 and S _bat.UC=-1, P _charge.VRLAsubtract power charging, and P _{charge.VRLA.min}=0 o'clock,

If a. P _charge.VRLA=P _{charge.VRLA.min}, UC promotes the electric discharge of k value, works as SOC _uC<SOC _uC.mintime, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL(t)>0；

If b. UC discharge power exceedes P _{discha.UC.max}, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=|P _ΔH(t)+P _ΔL(t)-P _{discha.UC.max}|。

(II) works as S _bat.VRLA=-1 and S _bat.UC=-1, VRLA times power is discharged to SOC _vRLA<SOC _vRLA.mintime, k=1; Now,

If a. SOC _uC<SOC _uC.min, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL(t)<0；

If b. | P _{Δ H}(t)+P _{Δ L}(t) | >P _{charge.VRLA.max}+ P _{charge.UC.max}, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=|P _ΔH(t)+P _ΔL(t)|-P _{charge.VRLA.max}-P _{charge.UC.max}。

Wherein, P _{charge.VRLA.max}and P _{discha.VRLA.min}be respectively maximum charge power and the minimum discharge power of accumulator; P _{charge.UC.max}and P _{discha.UC.max}be respectively electric capacity maximum charge power and maximum discharge power; S _bat.VRLAand S _bat.UCbe respectively charging, electric discharge and the floating charge state mark of accumulator and electric capacity, respectively corresponding numerical value 1 ,-1,0; SOC _uC.minand SOC _uC.maxbe respectively SOC operation minimum value and the maximal value of UC; P _abadonand P _lackbe respectively and abandon wind power and stabilize discharge power vacancy value.

In described step (2), hybrid energy-storing station capacity optimization aim function is:

$\begin{array}{c}C={\mathrm{\&rho;}}_{\mathrm{VRLA}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{VRLA}}+{\mathrm{\&rho;}}_{\mathrm{UC}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{UC}+}\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{VRLA}}+V_{\mathrm{VRLA}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{VRLA}.\max })+\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{UC}}-V_{\mathrm{UC}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{UC}.\max })+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{VRLA}}(t-1)-V_{\mathrm{VRLA}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{VRLA}.\max }\mathrm{\&Delta;t})+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{UC}}(t-1)-V_{\mathrm{UC}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{UC}.\max }\mathrm{\&Delta;t})\end{array}$

In formula, ρ _vRLA, ρ _vRBbe respectively accumulator and unit of capacity capacity construction cost; T by the window length of extraction service data; β is eolian of abandoning of unit capacity; α is not by reaching the unit punishment cost of stabilizing the scarce capacity of target; Posit is for getting positive function, and its implication is y=Posit(x), when x>0, y=x; X≤0 o'clock, y=0; V _vRLA(t-1), V _uC(t-1) be corresponding previous moment capacity value; Δ t is sampling interval; P _{charge.VRLA.max}, P _{charge.UC.max}be respectively the maximum charge power of accumulator and electric capacity; V _vRLA.min, V _uC.minbe respectively the minimum discharge capacity of accumulator and electric capacity, corresponding with SOC numerical value; P _{discha.VRLA.max}, P _{discha.UC.max}be respectively the maximum discharge power of accumulator and electric capacity, V _opt.VRLAand V _opt.UCbe respectively the optimum capacity of VRLA and UC.

The constraint condition that the middle hybrid energy-storing of described step (2) power station discharges and recharges power is:

-P _discha.max＜P(t)＜P _charge.max

In formula, P _charge.max, P _discha.maxbe respectively the maximum that accumulator or electric capacity are corresponding and discharge and recharge power, P(t) for accumulator or electric capacity corresponding discharge and recharge power.

In described step (2), hybrid energy-storing station capacity constraint condition is:

$\left\{\begin{array}{c}{V}_{\min }\&le;V\left(t\right)\&le;V_{\max }\\ V_{\mathrm{VRLA}}\left(t\right)>V_{\mathrm{VRB}}\left(t\right)\end{array}\right.$

In formula, hybrid energy-storing station capacity V(t) variation should be limited in the max cap. V of accumulator and electric capacity _maxwith minimum capacity V _minbetween.

The step of selecting PSO algorithm to solve calculating to hybrid energy-storing station capacity optimization aim function in described step (3) is:

1) extract wind energy turbine set service data time window length T and corresponding data thereof, stabilize target and decompose and determine target charging and discharging state separately.

2) population dimension D, maximum iteration time M are set _max, convergence precision σ thresh, initialization population position x and speed v simultaneously, and given initial V _opt.VRLA, V _opt.UCnumerical value.

3) judge whether to meet charge and discharge process model, and take correspondence to discharge and recharge strategy.

4) calculate the each cost parameter of hybrid energy-storing station capacity optimization aim function, and calculate each particle fitness value.

5) by each particle fitness value and self particle extreme value and overall example ratio of extreme values, if fitness value is less, upgrade the individual extreme value e of each particle _bestand overall example fitness extreme value g _best.

6) calculate Δ σ ²judge whether to meet the condition of convergence, the described condition of convergence is:

$\underset{t\&RightArrow;\&infin;}{\lim }{\mathrm{\&Delta;\&sigma;}}^2=\mathrm{\&sigma;thresh}$

Δ σ in formula ²for the colony of population or the variable quantity of overall fitness variance, if so, extract current V _opt.VRLA, V _opt.UCbe optimum capacity numerical value; If not, discharge and recharge power and capacity-constrained condition according to hybrid energy-storing power station, upgrade each particle position x and speed v, and repeat step 3)-5).

Position x and the speed v of the each particle of described renewal are carried out according to the following formula:

$v_i^{n+1}={\mathrm{wv}}_i^n+c_1{r}_1(e_{\mathrm{besti}}^n-x_i^n)+c_2{r}_2(g_{\mathrm{best}}-x_i^n)$

$x_i^{n+1}=x_i^n+{\mathrm{gv}}_i^{n+1};$

Wherein, n is current cycle time; C1, c2 are particle weight coefficient; W is inertia weight; R1, r2 are (0,1) interior uniform random number; x _i, v _iit is the Position And Velocity of i dimension particle; x _i ⁿ, v _i ⁿbe respectively current circulation x _i, v _inumerical value; x _i ⁿ⁺¹, v _i ⁿ⁺¹be respectively next circulation x _i, v _irenewal value; G is constraint factor, e _bestibe the individual extreme value of i dimension particle, g _bestfor overall example fitness extreme value.

The invention has the beneficial effects as follows:

Mixing capacity computing method of the present invention, discharging and recharging under strategy guiding of constructed mixed energy storage system, can realize organic configuration of different medium energy storage, and stabilize under the prerequisite of effect in guarantee fluctuation, energy storage is stabilized power offset and VRLA and is discharged and recharged the aspects such as number of times and have obvious optimization castering action, and simultaneously overall cost has small size reduction.

The present invention builds hybrid energy-storing power station with exemplary power type energy storage VRLA and energy type energy storage UC, utilizes its charge and discharge process model that characteristic structure is had complementary advantages separately, is beneficial to the organic operation of coordination realizing between the each medium of mixed energy storage system; Set up capacity planning model taking energy-accumulating power station operating cost and construction cost minimum as target, realized the optimization of capacity.Utilize on-the-spot service data to carry out check analysis, show that the method can effectively reduce VRLA and discharge and recharge number of times, reduce the capacity proportion of UC, performance has further reduced overall cost when effectively lifting and has dropped into, and has effectively verified economy and the feasibility of mixed energy storage system collocation method herein.

Brief description of the drawings

Fig. 1 (a) is side-play amount P schematic diagram;

Fig. 1 (b) is P Δ H curve synoptic diagram;

Fig. 1 (c) is P Δ L curve synoptic diagram;

Fig. 2 is that effect schematic diagram is stabilized in part-time cross section;

Fig. 3 is that decomposition goal discharges and recharges power schematic diagram.

Embodiment:

Below in conjunction with accompanying drawing and embodiment, the present invention will be further described:

If t moment wind-powered electricity generation unit output power P _w(t) with period reference output power P _ref(t) difference P (t) is:

P(t)=P _W(t)-P _ref(t) （1）

In order further to bring into play blending agent characteristic, utilize wave filter that P (t) is decomposed into high fdrequency component P simultaneously _hand trend component P (t) _l(t), and meet:

P(t)=P _ΔH(t)+P _ΔL(t) （2）

If P _{Δ H}(t) >0, t moment energy-accumulating power station UC is in charged state; Otherwise, in discharge condition; In like manner, if P _{Δ L}(t) >0, t moment energy-accumulating power station VRLA is in charged state; Otherwise, in discharge condition.Charge and discharge process model is divided into following situation from operational objective herein:

(1) fully reduce VRLA and discharge and recharge number of times

Introduce the ultra-short term wind power prediction numerical value of the following 1h of current time, this numerical value is for the judgement of charging and discharging state.Charging and discharging state for following situation VRLA will remain unchanged, and now stabilize task and mainly be completed by UC.If f (t) is P _{Δ L}(t) with null value be related to zone bit and sign and the P of f (t) _{Δ L}(t) identical, in the time meeting f (t-1) f (t) <0:

If f (t) >0 and Δ t _f< Δ t _thr, now S _bat.VRLA=-1, P _discha.VRLA=0;

In formula, Δ t _ffor f keeps the current state duration; Δ t _thrfor duration threshold value; P _diacha.VRLArepresent discharge power and charge and discharge and floating charge state corresponding numerical value 1 ,-1,0. respectively.F in formula (t-1) f (t) <0 represents that P Δ L (t) and null value relation change, generally speaking, the now charging and discharging state change that changes, and current state f (t) >0 should correspond to charged state, and Wen Zhongwei minimizing VRLA discharges and recharges number of times, now introduce and judge f (t) the >0 duration, if be greater than S of threshold value Δ t _bat.VRLAchange, otherwise S _bat.VRLArefusal changes, and equivalent P _diacha.VRLAfor null value.

If f (t) <0 and Δ t _f< Δ t _thr, now S _bat.VRLA=1, P _charge.VRLA=0;

In formula, P _charge.VRLArepresent charge power; S _bat.VRLAfor charging and discharging state mark.In formula, corresponding f (t) is when normal charge becomes discharge condition, S during in conjunction with judgment threshold _bat.VRLArule change.

(2) fully reduce UC capacity

For reducing UC capacity, when VRLA and UC are all while charging and discharging state, UC subtracts that power charges and discharge and VRLA times power charges and discharge; While charging and discharging state inequality, UC is derating power running still, and the corresponding coordination of VRLA charges and discharge power; Work as SOC _vRLAwhile closing on threshold value, UC recovers to discharge and recharge power.Particularly, S is discussed respectively _bat.VRLAbe 1 or-1, S _bat.UCbe 1 or-1 o'clock charging and recharging model:

In above-mentioned four kinds of situations, for reducing UC capacity, VRLA and UC charge and discharge model and are:

P _{discha/charge.UC}=P _ΔH·k （3）

P _{discha/charge.VRLA}=P _ΔL+P _ΔH·（1-k） （4）

In formula, k is power reduction coefficient, for interval (0,1] positive number, for ensureing that UC fills-puts-the cyclic balance state that fills in what continue, needs to ensure k>0; P _{discha/charge.UC}, P _{discha/charge.VRLA}represent respectively UC, VRLA fills or discharge power.Formula (3) and (4) show, UC subtracts power and charges and discharge, and VRLA bears the task of stabilizing of residue overall goal with subordinate coordination role.

Strategy difference is:

I. work as S _bat.VRLA=1 and S _bat.UC=1, and accumulator times power charges to SOC _vRLA>=SOC _vRLA.maxtime, k=1; Now:

If a. SOC _uC>=SOC _uC.max, accumulator system completely fills, and occurs abandoning wind,

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)>0；

If b. P _charge.VRLA>P _{charge.VRLA.max}, electric capacity promotes the charging of k value, works as P _{Δ H}+ P _{Δ L}>P _{charge.VRLA.max}+ P _{charge.UC.max}time, there is abandoning wind,

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.VRLA.max}-P _{charge.UC.max}；

II. work as S _bat.VRLA=-1 and S _bat.UC=1, P _discha.VRLAsubtract power discharge, and P _{discha.VRLA.min}=0 o'clock,

If a. P _discha.VRLA=P _{discha.VRLA.min}, UC promotes the charging of k value, works as SOC _uC>SOC _uC.maxtime, there is abandoning wind,

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)<0；

If b. UC charge power exceedes P _{charge.UC.max},, there is abandoning wind in charge power deficiency,

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.UC.max}；

III. work as S _bat.VRLA=1 and S _bat.UC=-1, P _charge.VRLAsubtract power charging, and P _{charge.VRLA.min}=0 o'clock,

If a. P _charge.VRLA=P _{charge.VRLA.min}, UC promotes the electric discharge of k value, works as SOC _uC<SOC _uC.mintime, underpower is stabilized in electric discharge,

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL(t)>0；

If b. UC discharge power exceedes P _{discha.UC.max}, underpower is stabilized in electric discharge,

P _lack=|P _ΔH(t)+P _ΔL(t)-P _{discha.UC.max}|；

IV. work as S _bat.VRLA=-1 and S _bat.UC=-1, VRLA times power is discharged to SOC _vRLA<SOC _vRLA.mintime, k=1; Now,

If a. SOC _uC<SOC _uC.min, underpower is stabilized in electric discharge,

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL(t)<0；

If b. | P _{Δ H}(t)+P _{Δ L}(t) | >P _{charge.VRLA.max}+ P _{charge.UC.max}, underpower is stabilized in electric discharge,

P _lack=|P _ΔH(t)+P _ΔL(t)|-P _{charge.VRLA.max}-P _{charge.UC.max}；

Wherein, P _{charge.VRLA.max}and P _{discha.VRLA.min}be respectively maximum charge power and the minimum discharge power of accumulator; P _{charge.UC.max}and P _{discha.UC.max}be respectively electric capacity maximum charge power and maximum discharge power; S _bat.VRLAand S _bat.UCbe respectively charging, electric discharge and the floating charge state mark of accumulator and electric capacity, respectively corresponding numerical value 1 ,-1,0; SOC _uC.minand SOC _uC.maxbe respectively SOC operation minimum value and the maximal value of UC; P _abadonand P _lackbe respectively and abandon wind power and stabilize discharge power vacancy value.

Objective function:

With construction cost and the minimum optimization aim function that builds of operating cost summation, construction cost and mixing capacity are proportional, operating cost is on a declining curve in mixing capacity increase situation, and therefore this objective function is to ask for the optimum capacity under construction cost and operating cost balance.Operating cost comprises abandons eolian and desired output punishment cost, wherein abandoning eolian loses because mixed energy storage system completely fills the windage loss of abandoning causing, desired output punishment cost cannot meet and stabilizes the punishment that target is introduced because stored energy capacitance is discharged to minimum, and corresponding calculating is suc as formula shown in (5):

$\begin{array}{c}C={\mathrm{\&rho;}}_{\mathrm{VRLA}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{VRLA}}+{\mathrm{\&rho;}}_{\mathrm{UC}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{UC}+}\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{VRLA}}+V_{\mathrm{VRLA}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{VRLA}.\max })+\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{UC}}-V_{\mathrm{UC}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{UC}.\max })+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{VRLA}}(t-1)-V_{\mathrm{VRLA}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{VRLA}.\max }\mathrm{\&Delta;t})+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{UC}}(t-1)-V_{\mathrm{UC}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{UC}.\max }\mathrm{\&Delta;t})\end{array}---\left(5\right)$

In formula, ρ _vRLA, ρ _uCbe respectively VRLA and UC unit capacity construction cost; T by the window length of extraction service data; β is eolian of abandoning of unit capacity; α is not by reaching the unit punishment cost of stabilizing the scarce capacity of target; Posit is for getting positive function, and its implication is y=Posit(x), in the time of x>0, y=x, and in the time of x≤0, y=0; V _vRLA(t-1), V _uC(t-1) be corresponding previous moment capacity value; Δ t is sampling interval; P _{charge.VRLA.max}, P _{charge.UC.max}be respectively the maximum charge power of respective media; V _vRLA.min, V _uC.minbe respectively the minimum discharge capacity of corresponding medium, corresponding with SOC numerical value; P _{discha.VRLA.max}, P _{discha.UC.max}be respectively corresponding maximum discharge power.

Can be found out by objective function, abandon eolian and desired output punishment cost and form by two parts, one is capacity factor, descendingly out-of-limitly cannot effectively stabilize fluctuation because off-capacity causes completely to fill to abandon wind or discharge; It two is power factor (PF), has eliminated this factor impact though stabilize separately target when certain, because the strategy that discharges and recharges of harmonizing causes actual stabilizing in wave process, still have charge and discharge underpower cause abandoning wind and stabilize under powered may.

Constraint condition: constraint condition mainly comprises and discharges and recharges power constraint and capacity-constrained.

Discharge and recharge power constraint:

-P _discha.max＜P(t)＜P _charge.max （6）

In formula, P _charge.max, P _discha.maxbe respectively maximum corresponding to respective media and discharge and recharge power.

Capacity-constrained:

$\left\{\begin{array}{c}{V}_{\min }\&le;V\left(t\right)\&le;V_{\max }\\ V_{\mathrm{VRLA}}\left(t\right)>V_{\mathrm{VRB}}\left(t\right)\end{array}\right.---\left(7\right)$

In formula, capacity V(t) variation should be limited in each medium max cap. V _maxwith minimum capacity V _minbetween.

Derivation algorithm and step:

Employing counting yield is higher, the good PSO algorithm of convergence property solves calculating, and specific algorithm and step are as follows:

1) extract wind energy turbine set service data time window length T and corresponding data thereof, stabilize target and decompose and determine target P separately _{Δ L}and P _{Δ H};

2) population dimension D, maximum iteration time M are set _max, convergence precision σ _thresh, initialization population position x and speed x simultaneously, and given initial V _opt.VRLA, V _opt.UCnumerical value;

3) judge whether to meet charge and discharge process model, and take correspondence to discharge and recharge strategy;

4) calculate each cost parameter by formula (2), and calculate each particle fitness value;

5) by each particle fitness value and self particle extreme value and overall example ratio of extreme values, if fitness value is less, upgrade the individual extreme value e of each particle _bestand overall example fitness extreme value g _best;

6) judge whether current calculating meets the condition of convergence, if extract current V _opt.VRLA, V _opt.UCbe optimum capacity numerical value; Retrain according to formula (6,7) if not, by the each particle position x of Policy Updates shown in formula (5) and speed v, and repeating step 3-5.

$v_i^{n+1}={\mathrm{wv}}_i^n+c_1{r}_1(e_{\mathrm{besti}}^n-x_i^n)+c_2{r}_2(g_{\mathrm{best}}-x_i^n)---\left(8\right)$

$x_i^{n+1}=x_i^n+{\mathrm{gv}}_i^{n+1}---\left(9\right)$

Wherein n is current cycle time; c ₁, c ₂for particle weight coefficient; W is inertia weight; r ₁, r ₂for (0,1) interior uniform random number; x _i, v _iit is the Position And Velocity of i dimension particle; G is constraint factor.

With the validity of on-the-spot wind energy turbine set actual operating data checking this paper method, calculate hybrid energy-storing capacity according to this paper hybrid energy-storing station capacity planing method, this wind field power samples frequency is 5 minutes.Capacity adopts equivalence value, and UC capacity is equivalent to the capacity taking VRLA as benchmark, is calculated as follows equivalent total volume:

$V=V_{\mathrm{Opt}.\mathrm{VRLA}}+\frac{{\mathrm{\&rho;}}_{\mathrm{VRB}}\&CenterDot;V_{\mathrm{OPt}.\mathrm{UC}}}{{\mathrm{\&rho;}}_{\mathrm{VRLA}}}.$

This wind field installed capacity 90MW, to annual service data, determines desired output for objective self-adapting and stabilizes target and decomposition goal based on power offset variance is minimum, as shown in Fig. 1 (a)-(c).

Based on this, in conjunction with discharging and recharging strategy and capacity optimization method, calculate the configuration of best stored energy capacitance according to derivation algorithm and step, result of calculation as shown in Table 1:

Form 1 result of calculation

In form, power excursion variance is used for weighing the degrees of offset of stabilizing between rear output power and desired output; N is the charging and discharging state conversion times of VRLA.The optimum equivalent capacity of mixed energy storage system is 30.201MWh herein, and target function value is 381.9; Comparatively speaking, the optimum capacity of single UC system is 31.766MWh, and its target function value is 363.3.Can find out, from economy angle, herein to compare Single Medium capacity lower for the selected capacity of mixed energy storage system, and optimum value taking total cost as objective function is also relatively little, and when showing performance boost, mixed energy storage system has further been optimized its economy.

Stabilize effect aspect, compare Single Medium system, mixed energy storage system power excursion variance χ declines 9.5%, it is out-of-limit and cause that its reason is in single energy-accumulating medium that its side-play amount mainly discharges and recharges power by it, and in commingled system owing to stabilizing the decomposition of target, its separately medium bear limit value and discharge and recharge power and relatively reduce, play thus the χ that totally stabilizes result and present downtrending; And discharging and recharging aspect number of times at VRLA, mixed energy storage system possesses the ability of remarkable reduction N, it decreases by 65.5%, its reason be mixed energy storage system discharge and recharge that strategy pays the utmost attention to VRLA discharge and recharge in short-term change over condition, this situation is taked to the auxiliary method of stabilizing of UC.For the benefit of show, that extracts certain hour cross section stabilizes effect, and as shown in Figure 2, what this stabilized process discharges and recharges power as shown in Figure 3.

As seen from Figure 2, selected time cross-section to stabilize effect remarkable; Discharge and recharge power by Fig. 3 in must this interface, interval not out-of-limit.Comprehensive above-mentioned simulating, verifying can obtain, the mixing capacity computing method of carrying herein, discharging and recharging under strategy guiding of constructed mixed energy storage system, can realize organic configuration of different medium energy storage, and stabilize under the prerequisite of effect in guarantee fluctuation, energy storage is stabilized power offset and VRLA and is discharged and recharged the aspects such as number of times and have obvious optimization castering action, and simultaneously overall cost has small size reduction.

By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendments that creative work can make or distortion still in protection scope of the present invention.

Claims (10)

1. determine a method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, comprise the following steps:

(1) to reduce accumulator cell charging and discharging number of times or to reduce capacitance setting up hybrid energy-storing power station charge and discharge process model as target;

(2) build hybrid energy-storing station capacity optimization aim function taking hybrid energy-storing power plant construction cost and operating cost summation minimum as target, and set up hybrid energy-storing power station and discharge and recharge constraint condition and the capacity-constrained condition of power;

(3) meeting under the condition of hybrid energy-storing power station charge and discharge process model, selecting PSO algorithm to solve calculating to hybrid energy-storing station capacity optimization aim function, determining the optimum capacity numerical value in hybrid energy-storing power station.

2. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, in described step (1) taking reduce accumulator cell charging and discharging number of times as the hybrid energy-storing power station charge and discharge process model of target as:

If P (t) is t moment wind-powered electricity generation unit output power P _w(t) with period reference output power P _ref(t) difference:

P(t)=P _W(t)-P _ref(t)

P (t) is decomposed into high fdrequency component P _{Δ H}and trend component P (t) _{Δ L}(t), and meet:

P(t)=P _ΔH(t)+P _ΔL(t)

If f (t) is P _{Δ L}(t) with null value be related to zone bit and sign and the P of f (t) _{Δ L}(t) identical, in the time meeting f (t-1) f (t) <0:

If f (t) >0 and Δ t _f< Δ t _thr, S _bat.VRLA=-1, P _discha.VRLA=0;

If f (t) <0 and Δ t _f< Δ t _thr, S _bat.VRLA=1, P _charge.VRLA=0;

In formula, f (t-1) f (t) <0 represents that P Δ L (t) and null value relation change; Δ t _ffor the duration of f (t) maintenance current state; Δ t _thrfor duration threshold value; P _diacha.VRLArepresent battery discharging power, P _charge.VRLArepresent charge in batteries power; S _bat.VRLAfor charge in batteries, electric discharge and floating charge state mark, corresponding numerical value 1 ,-1,0 respectively.

3. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, in described step (1) taking reduce capacitance as the hybrid energy-storing power station charge and discharge process model of target as:

If P (t) is t moment wind-powered electricity generation unit output power P _w(t) with period reference output power P _ref(t) difference:

P(t)=P _W(t)-P _ref(t)

P (t) is decomposed into high fdrequency component P _{Δ H}and trend component P (t) _{Δ L}(t), and meet:

P(t)=P _ΔH(t)+P _ΔL(t)

P _{discha/charge.UC}=P _ΔH(t)·k

P _{discha/charge.VRLA}=P _ΔL(t)+P _ΔH(t)·(1-k)；

In formula, k is power reduction coefficient, for interval (0,1] positive number, for ensureing that electric capacity fills-puts-the cyclic balance state that fills in what continues, needs to ensure k>0; P _charge.UCand P _discha.UCbe respectively the charging and discharging power of electric capacity; P _charge.VRLAand P _discha.VRLArepresent respectively the charging and discharging power of accumulator.

4. a kind ofly as claimed in claim 2 or claim 3 determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, the control strategy of described hybrid energy-storing power station charge and discharge process model is:

(I) works as S _bat.VRLA=1 and S _bat.UC=1, and accumulator times power charges to SOC _vRLA>=SOC _vRLA.maxtime, k=1; Now:

If a. SOC _uC>=SOC _uC.max, accumulator system completely fills, and occurs abandoning wind, abandons wind power to be:

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)>0；

If b. P _charge.VRLA>P _{charge.VRLA.max}, electric capacity promotes the charging of k value, works as P _{Δ H}+ P _{Δ L}>P _{charge.VRLA.max}+ P _{charge.UC.max}time, there is abandoning wind, abandon wind power and be:

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.VRLA.max}-P _{charge.UC.max}；

(II) works as S _bat.VRLA=-1 and S _bat.UC=1, P _discha.VRLAsubtract power discharge, and P _{discha.VRLA.min}=0 o'clock,

If a. P _discha.VRLA=P _{discha.VRLA.min}, UC promotes the charging of k value, works as SOC _uC>SOC _uC.maxtime, there is abandoning wind, abandon wind power and be:

P _abadon=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)>0,P _ΔL(t)<0；

If b. UC charge power exceedes P _{charge.UC.max},, there is abandoning wind in charge power deficiency, abandons wind power to be:

P _abadon=P _ΔH(t)+P _ΔL(t)-P _{charge.UC.max}；

Wherein, S _bat.VRLAand S _bat.UCbe respectively charging, electric discharge and the floating charge state mark of accumulator and electric capacity, respectively corresponding numerical value 1 ,-1,0; SOC _vRLA.maxand SOC _uC.maxbe respectively the SOC operation maximal value of VRLA and UC; P _charge.UCand P _discha.UCbe respectively the charging and discharging power of electric capacity; P _charge.VRLAand P _discha.VRLArepresent respectively the charging and discharging power of accumulator; P _{charge.UC.max}for the maximum charge power of accumulator; P _{charge.VRLA.max}and P _{discha.VRLA.min}represent respectively maximum charge power and the minimum discharge power of accumulator.

5. a kind ofly as claimed in claim 2 or claim 3 determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, the control strategy of described hybrid energy-storing power station charge and discharge process model is:

(I) works as S _bat.VRLA=1 and S _bat.UC=-1, P _charge.VRLAsubtract power charging, and P _{charge.VRLA.min}=0 o'clock,

If a. P _charge.VRLA=P _{charge.VRLA.min}, UC promotes the electric discharge of k value, works as SOC _uC<SOC _uC.mintime, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL ₍t)>0；

If b. UC discharge power exceedes P _{discha.UC.max}, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=|P _ΔH(t)+P _ΔL(t)-P _{discha.UC.max}|；

(II) works as S _bat.VRLA=-1 and S _bat.UC=-1, VRLA times power is discharged to SOC _vRLA<SOC _vRLA.mintime, k=1; Now,

If a. SOC _uC<SOC _uC.min, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=P _ΔH(t)+P _ΔL(t)，P _ΔH(t)<0,P _ΔL(t)<0；

If b. | P _{Δ H}(t)+P _{Δ L}(t) | >P _{charge.VRLA.max}+ P _{charge.UC.max}, underpower is stabilized in electric discharge, stabilizes discharge power vacancy value and is:

P _lack=|P _ΔH(t)+P _ΔL(t)|-P _{charge.VRLA.max}-P _{charge.UC.max}；

Wherein, P _{charge.VRLA.max}and P _{discha.VRLA.min}be respectively maximum charge power and the minimum discharge power of accumulator; P _{charge.UC.max}and P _{discha.UC.max}be respectively electric capacity maximum charge power and maximum discharge power; S _bat.VRLAand S _bat.UCbe respectively charging, electric discharge and the floating charge state mark of accumulator and electric capacity, respectively corresponding numerical value 1 ,-1,0; SOC _uC.minand SOC _uC.maxbe respectively SOC operation minimum value and the maximal value of UC; P _abadonand P _lackbe respectively and abandon wind power and stabilize discharge power vacancy value.

6. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, in described step (2), hybrid energy-storing station capacity optimization aim function is:

$\begin{array}{c}C={\mathrm{\&rho;}}_{\mathrm{VRLA}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{VRLA}}+{\mathrm{\&rho;}}_{\mathrm{UC}}\&CenterDot;V_{\mathrm{Opt}.\mathrm{UC}+}\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{VRLA}}+V_{\mathrm{VRLA}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;L}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{VRLA}.\max })+\\ \mathrm{\&beta;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-(V_{\mathrm{Opt}.\mathrm{UC}}-V_{\mathrm{UC}}(t-1)))+\mathrm{Posit}(P_{\mathrm{\&Delta;H}}\left(t\right)\mathrm{\&Delta;t}-P_{\mathrm{ch}\arg e.\mathrm{UC}.\max })+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{VRLA}}(t-1)-V_{\mathrm{VRLA}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;L}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{VRLA}.\max }\mathrm{\&Delta;t})+\\ \mathrm{\&alpha;}\&CenterDot;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-(V_{\mathrm{UC}}(t-1)-V_{\mathrm{UC}.\min }))+\mathrm{Posit}\left(\right|P_{\mathrm{\&Delta;H}}\left(t\right)|\mathrm{\&Delta;t}-P_{\mathrm{discha}.\mathrm{UC}.\max }\mathrm{\&Delta;t})\end{array}$

In formula, ρ _vRLA, ρ _vRBbe respectively accumulator and unit of capacity capacity construction cost; T by the window length of extraction service data; β is eolian of abandoning of unit capacity; α is not by reaching the unit punishment cost of stabilizing the scarce capacity of target; Posit is for getting positive function, and its implication is y=Posit(x), when x>0, y=x; X≤0 o'clock, y=0; V _vRLA(t-1), V _uC(t-1) be corresponding previous moment capacity value; Δ t is sampling interval; P _{charge.VRLA.max}, P _{charge.UC.max}be respectively the maximum charge power of accumulator and electric capacity; V _vRLA.min, V _uC.minbe respectively the minimum discharge capacity of accumulator and electric capacity, corresponding with SOC numerical value; P _{discha.VRLA.max}, P _{discha.UC.max}be respectively the maximum discharge power of accumulator and electric capacity, V _opt.VRLAand V _opt.UCbe respectively the optimum capacity of VRLA and UC.

7. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, in described step (2), hybrid energy-storing power station discharges and recharges the constraint condition of power and is:

-P _discha.max＜P(t)＜P _charge.max；

In formula, P _charge.max, P _discha.maxbe respectively the maximum that accumulator or electric capacity are corresponding and discharge and recharge power, P(t) for accumulator or electric capacity corresponding discharge and recharge power.

8. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, in described step (2), hybrid energy-storing station capacity constraint condition is:

$\left\{\begin{array}{c}{V}_{\min }\&le;V\left(t\right)\&le;V_{\max }\\ V_{\mathrm{VRLA}}\left(t\right)>V_{\mathrm{VRB}}\left(t\right)\end{array}\right.;$

In formula, hybrid energy-storing station capacity V(t) variation should be limited in the max cap. V of accumulator and electric capacity _maxwith minimum capacity V _minbetween.

9. as claimed in claim 1ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, the step of selecting PSO algorithm to solve calculating to hybrid energy-storing station capacity optimization aim function in described step (3) is:

1) extract wind energy turbine set service data time window length T and corresponding data thereof, stabilize target and decompose and determine target charging and discharging state separately;

2) population dimension D, maximum iteration time M are set _max, convergence precision σ thresh, initialization population position x and speed v simultaneously, and given initial V _opt.VRLA, V _opt.UCnumerical value;

3) judge whether to meet charge and discharge process model, and take correspondence to discharge and recharge strategy;

4) calculate the each cost parameter of hybrid energy-storing station capacity optimization aim function, and calculate each particle fitness value;

5) by each particle fitness value and self particle extreme value and overall example ratio of extreme values, if fitness value is less, upgrade the individual extreme value e of each particle _bestand overall example fitness extreme value g _best;

6) calculate Δ σ ²judge whether to meet the condition of convergence, the described condition of convergence is:

$\underset{t\&RightArrow;\&infin;}{\lim }{\mathrm{\&Delta;\&sigma;}}^2=\mathrm{\&sigma;thresh}$

Δ σ in formula ²for the colony of population or the variable quantity of overall fitness variance, if so, extract current V _opt.VRLA, V _opt.UCbe optimum capacity numerical value; If not, discharge and recharge power and capacity-constrained condition according to hybrid energy-storing power station, upgrade each particle position x and speed v, and repeat step 3)-5).

10. as claimed in claim 9ly a kind ofly determine method for the hybrid energy-storing station capacity of stabilizing wind power fluctuation, it is characterized in that, position x and the speed v of the each particle of described renewal are carried out according to the following formula:

$v_i^{n+1}={\mathrm{wv}}_i^n+c_1{r}_1(e_{\mathrm{besti}}^n-x_i^n)+c_2{r}_2(g_{\mathrm{best}}-x_i^n)$

$x_i^{n+1}=x_i^n+{\mathrm{gv}}_i^{n+1};$

Wherein n is current cycle time; C1, c2 are particle weight coefficient; W is inertia weight; R1, r2 are (0,1) interior uniform random number; x _i, v _iit is the Position And Velocity of i dimension particle; x _i ⁿ, v _i ⁿbe respectively current circulation x _i, v _inumerical value; x _i ⁿ⁺¹, v _i ⁿ⁺¹be respectively next circulation x _i, v _irenewal value; G is constraint factor, e _bestibe the individual extreme value of i dimension particle, g _bestfor overall example fitness extreme value.