CN103078340B - Mixed energy storing capacity optimization method for optimizing micro-grid call wire power - Google Patents

Abstract

The invention discloses a mixed energy storing capacity optimization method for optimizing micro-grid call wire power. A fitness function of a genetic algorithm is constructed through a mixed energy storing capacity optimization model and sample data of mixed energy storing total charging/discharging power is obtained; a frequency range is subjected to binary coding to generate a first-generation dividing frequency fP group; active powers PBESS and PSC of a storage battery and a super capacitor of any one individual in the first-generation group are respectively calculated according to the mixed energy storing capacity optimization model; rated capacities of the storage battery and the super capacitor are respectively calculated through the active powers PBESS and PSC of the storage battery and the super capacitor; an engineering period net present value of a mixed energy storing system, the service life of the storage battery and the service life of the super capacitor are obtained according to the rated capacities of the storage battery and the super capacitor and the active powers of the storage battery and the super capacitor; an individual with the highest fitness in the final generation is an optimal individual; and meanwhile, an optimal mixed energy storing capacity combination is obtained. An obtained capacity result is accurate and the estimation of the service life of energy storing equipment is realized.

Description

For optimizing the hybrid energy-storing capacity optimization method of microgrid dominant eigenvalues

Technical field

The present invention relates to the method that in microgrid, mixed energy storage system capacity is optimized, particularly relating to a kind of hybrid energy-storing capacity optimization method for optimizing microgrid dominant eigenvalues.

Background technology

In microgrid, the power output of wind-powered electricity generation and photovoltaic distributed power supply has feature that is intermittent and randomness, load variations often also embodies certain fluctuation, and this brings larger challenge to the stable operation of microgrid, and introducing energy-storage system can address this problem.Energy storage device is divided into power-type energy storage and energy type energy storage two kinds.Require that fast response time, cycle charge discharge electric life are long with the energy-storage system that smooth distribution formula power exports as target, be applicable to the power-type energy storage that use take ultracapacitor as representative; With peak load shifting be target energy-storage system requires to have higher energy capacity, energy storage time is long, be applicable to the energy type energy storage that use take battery as representative.Microgrid is when being incorporated into the power networks, and the peak-valley difference characteristic of load and the fluctuation of distributed power source are usually and deposit.Hybrid energy-storing than single energy storage advantageously, is more suitable for smoothing output simultaneously and peak load shifting, reaches the optimization of micro-grid connection dominant eigenvalues.Meanwhile, by ultracapacitor and battery parallel connection, the discharge and recharge number of times of battery can also be reduced, improve the life-span ^[1].

Existing hybrid energy-storing capacity optimization method generally with once invest with year operation expense for optimization aim.

Realizing in process of the present invention, finding at least there is following shortcoming and defect in prior art:

(1) do not consider energy storage efficiency for charge-discharge and SOC(state-of-charge in detail) constraint, cause capacity less than normal;

(2) lack the assessment to the energy storage device life-span, the actual running results may because certain energy storage device needs repeatedly upgrade and greatly affect economy.So current hybrid energy-storing capacity optimization method is deep not enough, do not reach degree of being practical.

Summary of the invention

The invention provides a kind of hybrid energy-storing capacity optimization method for optimizing microgrid dominant eigenvalues, this method considers the constraint of energy storage efficiency for charge-discharge and SOC, and the capacity result got is accurate; Achieve the assessment to the energy storage device life-span, described below:

For optimizing a hybrid energy-storing capacity optimization method for microgrid dominant eigenvalues, said method comprising the steps of:

(1) be optimization aim to the maximum with mixed energy storage system net present value (NPV), set up and be applied to the battery of micro-grid system and the hybrid energy-storing capacity Optimized model of ultracapacitor;

(2) by the fitness function of hybrid energy-storing capacity Optimized model structure genetic algorithm, and the sample data of the total charge-discharge electric power of hybrid energy-storing is obtained; Binary coding is carried out to frequency range, produces first generation boundary frequency fP population;

(3) according to the active-power P of hybrid energy-storing capacity Optimized model to arbitrary individual calculating accumulator and the ultracapacitor respectively in first generation population _bESSand P _sC;

(4) by the active-power P of battery and ultracapacitor _bESSand P _sCthe rated capacity of calculating accumulator and ultracapacitor respectively;

(5) mixed energy storage system engineering phase net present value (NPV), the life of storage battery and ultracapacitor life-span is obtained according to the active power of the rated capacity of battery and ultracapacitor, battery and ultracapacitor;

(6) individuality that in last generation, fitness is the highest is optimum individual, obtains optimum hybrid energy-storing combined capacity simultaneously;

Wherein, hybrid energy-storing capacity Optimized model is specially:

f=min(-NPV) (1)

P _HESS=P _BESS+P _SC=P _Agr-P _Net(2)

P _Net＝P _Load-P _DG(3)

${\mathrm{\η}}_d={\mathrm{\η}}_c=\sqrt{\mathrm{\η}}---\left(4\right)$

0<f _P<1/2T _S(5)

SOC _Min≤SOC[n]≤SOC _Max(6)

n＝1,2...,N _S

In formula (1), NPV refers to the net present value (NPV) of mixed energy storage system within the engineering phase; F represents object function;

The power balance equation that formula (2) is microgrid inside and interconnection to formula (3); P _dGfor total active power that distributed power source sends; P _loadfor local load active power; P _hESSfor total active power of mixed energy storage system; P _bESSand P _sCbe respectively the active power of battery and ultracapacitor; P _gridfor the exchange power of micro-grid system and electrical network; P _agrfor interconnection agreement power;

In formula (4), η _d, η _cthe overall efficiency of the discharging efficiency of energy-storage system, charge efficiency and a charging-discharging cycle is respectively with η;

Formula (5) f _pbe the boundary frequency that two kinds of energy storage compensate frequency range, T _sfor the sampling period of sample data;

N in formula (6) _sfor the sampled point number of sample data.

Described according to the active-power P of hybrid energy-storing capacity Optimized model to arbitrary individual calculating accumulator and the ultracapacitor respectively in first generation population _bESSand P _sCbe specially:

$\left\{\begin{array}{c}{P}_{\mathrm{SC}}=P_{\mathrm{HESS}}*\frac{T_{\mathrm{HP}}*s}{T_{\mathrm{HP}}*s+1}\\ P_{\mathrm{BESS}}=P_{\mathrm{HESS}}-P_{\mathrm{SC}}\end{array}\right.---\left(7\right)$

In formula, s is the complex variable of Laplace transform; T _hPfor the time constant of high-pass filter;

$T_{\mathrm{HP}}=\frac{1}{2\mathrm{\&pi;}{f}_P}---\left(8\right).$

The described active-power P by battery and ultracapacitor _bESSand P _sCthe rated capacity of calculating accumulator and ultracapacitor is specially respectively:

With P _{eSS, 0}[n] represents the charge-discharge electric power of certain energy storage to introduce capacity calculation methods;

$P_{\mathrm{ESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{ESS},0}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{ESS},0}\left[n\right]>0\\ P_{\mathrm{ESS},0}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{ESS},0}\left[n\right]<0\end{array}\right.---\left(9\right)$

Wherein, P _eSS[n] to discharge for just, η _dand η _cbe respectively discharging efficiency and the charge efficiency of energy-storage system;

$E_{\mathrm{ESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ESS}}\left[n\right]*T_S,n=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_S---\left(10\right)$

In one day, energy-storage system adds up maximum, least energy and is designated as E respectively _{eSS, Max}and E _{eSS, Min}, rated capacity

$E_{\mathrm{ESS}}^R=\frac{E_{\mathrm{ESS},\mathrm{Max}}-E_{\mathrm{ESS},\mathrm{Min}}}{{\mathrm{SOC}}_{\mathrm{Max}}-{\mathrm{SOC}}_{\mathrm{Min}}}---\left(11\right)$

In formula, SOC _maxand SOC _minrepresent the bound constraint of energy-storage system SOC respectively.

The beneficial effect of technical scheme provided by the invention is: the method is optimization aim to the maximum with mixed energy storage system net present value (NPV), utilizes the boundary frequency of genetic algorithm determination energy type energy storage and power-type energy storage, then calculates the optimum capacity of hybrid energy-storing further.Battery in the method and ultracapacitor model consider efficiency for charge-discharge and the SOC constraint of energy-storage system, and capacity result is more accurate; Its economic model considers projects phase, dissimilar energy storage life-span, more closing to reality engineer applied, and its result is better than existing method.

Accompanying drawing explanation

Fig. 1 be micro-grid system and with external electrical network connection layout;

Fig. 2 is distributed power source, load and net load power;

Fig. 3 is net load and interconnection agreement power;

Fig. 4 is genetic algorithm searching process;

Fig. 5 is mixed energy storage system charge-discharge electric power;

Fig. 6 is the flow chart of hybrid energy-storing capacity optimization method.

Detailed description of the invention

For making the object, technical solutions and advantages of the present invention clearly, below embodiment of the present invention is described further in detail.

101: be optimization aim to the maximum with mixed energy storage system net present value (NPV), set up and be applied to the battery of micro-grid system and the hybrid energy-storing capacity Optimized model of ultracapacitor;

Wherein, micro-grid system inside comprises photovoltaic and/or blower fan distributed power supply, ultracapacitor and accumulator hybrid energy-storing system and local load, and microgrid is connected with outside public electric wire net by an interconnection, as shown in Figure 1.In Fig. 1, total active power that distributed power source sends is P _dG, local load active power is P _load, total active power of mixed energy storage system is P _hESS, wherein the active power of battery and ultracapacitor is respectively P _bESSand P _sC, the exchange power of micro-grid system and electrical network is P _grid, be microgrid dominant eigenvalues.

Because distributed power source has intermittence and fluctuation, the local load power of microgrid often has again larger peak-valley difference characteristic, causes micro-grid connection interconnection to there is relatively high power fluctuation.Therefore dispatching of power netwoks department generally presets power and the direction of microgrid and interconnecting ties in 1 day, namely determines the interconnection agreement power P of microgrid _agr ^[2].P _agrusually the net load power P without micro-grid system during energy storage is got _nethour level mean value, and according to the tie-line power transmission upper limit and whether allow power send to row revise.The control objectives of mixed energy storage system is microgrid dominant eigenvalues P _gridby the net load power P of micro-grid system before introducing energy storage _netbe compensated for as the agreement power P preset _agr.

In order to by microgrid dominant eigenvalues P _gridbe compensated for as preset protocol power P _agr, this method is optimization aim to the maximum with mixed energy storage system net present value (NPV), and consider the power-balance of microgrid and interconnection and efficiency, the SOC characteristic of energy-storage system, the hybrid energy-storing capacity Optimized model setting up battery and ultracapacitor is as follows:

f=min(-NPV) (1)

P _HESS=P _BESS+P _SC=P _Agr-P _Net(2)

P _Net＝P _Load-P _DG(3)

${\mathrm{\&eta;}}_d={\mathrm{\&eta;}}_c=\sqrt{\mathrm{\&eta;}}---\left(4\right)$

0<f _P<1/2T _S(5)

SOC _Min≤SOC[n]≤SOC _Max(6)

n=1,2...,N _S

In formula (1), NPV refers to the net present value (NPV) of mixed energy storage system within the engineering phase; F represents object function.

The power balance equation that formula (2) is microgrid inside and interconnection to formula (3).

In formula (4), η _d, η _cthe overall efficiency of the discharging efficiency of energy-storage system, charge efficiency and a charging-discharging cycle is respectively with η.

Formula (5) adopts the method for frequency division to carry out power division to hybrid energy-storing, f from frequency domain angle _pbe the boundary frequency that two kinds of energy storage compensate frequency range, T _sfor the sampling period of sample data, 1/2T _sfor Nyquist sampling frequency.

Formula (6) is in simulation process, and the SOC of energy storage can not be out-of-limit, N _sfor the sampled point number of sample data.

This method selects boundary frequency f _pfor optimized variable, namely with f _pfor boundary, HFS fluctuation is compensated by ultracapacitor, and low frequency part is compensated by battery.F _pdetermine the power division of hybrid energy-storing, capacity requirement and economic index, thus determine whole hybrid energy-storing volume solutions.Relative to directly selecting two kinds of stored energy capacitances to be optimized, it is advantageous that and can carry out mating of power curve vibration frequency and dissimilar energy storage device performance better.

The sample data of this model chooses distributed power source, the load active power data of typical case's day, and the sampling period was advisable by 10 minutes with 1 minute, and calculation of capacity time scale is fixed as 1 day.In simulation process, to think in year that every day is all identical with this typical case's day.In practical application, carry out capacity optimization with the microgrid service data of some day, be usually difficult to be applicable to annual planning.Multiple months typical days or season typical case's day data can be chosen, repeat repeatedly capacity optimization.In all results, the capability value that maximum, mean value or other most typical cases are in a few days enough can be selected according to the actual requirements ^[3].

102: by the fitness function of hybrid energy-storing capacity Optimized model structure genetic algorithm, and obtain the sample data of the total charge-discharge electric power of hybrid energy-storing; Binary coding is carried out to frequency range, produces first generation boundary frequency f _ppopulation;

During specific implementation, because this model has very strong non-linear, this method adopts genetic algorithm to solve, and totally solves flow process and sees Fig. 2.The sample data of the total charge-discharge electric power of hybrid energy-storing is obtained by formula (2).Object function in formula (1) is the fitness function of genetic algorithm.Afterwards binary coding is carried out to the frequency range shown in formula (5), and produce first generation boundary frequency f _pwhole individualities of population.

103: according to the active-power P of hybrid energy-storing capacity Optimized model to arbitrary individual calculating accumulator and the ultracapacitor respectively in first generation population _bESSand P _sC;

Batteries to store energy capacity is large, but to need to avoid frequent discharge and recharge life-extending as far as possible; Ultracapacitor energy storage capacity is little, but allows discharge and recharge number of times high, and charge-discharge velocity is fast.The two different characteristic determines low frequency and the HFS that battery and ultracapacitor should compensate the instruction of hybrid energy-storing general power respectively.According to this principle, adopt high-pass filter to carry out hybrid energy-storing power division, computational methods are as follows:

$\left\{\begin{array}{c}{P}_{\mathrm{SC}}=P_{\mathrm{HESS}}*\frac{T_{\mathrm{HP}}*s}{T_{\mathrm{HP}}*s+1}\\ P_{\mathrm{BESS}}=P_{\mathrm{HESS}}-P_{\mathrm{SC}}\end{array}\right.---\left(7\right)$

In formula, s is the complex variable of Laplace transform; T _hPfor the time constant of high-pass filter, this time constant is by boundary frequency f _pcalculate by formula (8):

$T_{\mathrm{HP}}=\frac{1}{2\mathrm{\&pi;}{f}_P}---\left(8\right)$

F _pwith T _hPnumerically that it is relative to T one to one _hPadvantage be can more intuitively from frequency domain angle determination hybrid energy-storing compensation range separately.At total active-power P _hESSresult of spectrum analysis in, by battery compensate 0 ~ f _pfrequency component, ultracapacitor compensate f _p~ 1/2T _sfrequency component.

Assuming that the power data sampling period of blower fan, photovoltaic, load is T _s, unit is second, total N in a day _sindividual sampled point.Total active power of hybrid energy-storing is P _hESS[n], n=1,2 ..., N _s.The active-power P of ultracapacitor can be calculated by formula (8) _sC[n] and corresponding storage battery active power power P _bESS[n].

104: by the active-power P of battery and ultracapacitor _bESSand P _sCthe rated capacity of calculating accumulator and ultracapacitor respectively;

The capacity calculation methods of two kinds of energy storage is general, below only with P _{eSS, 0}[n] represents the charge-discharge electric power of certain energy storage to introduce capacity calculation methods ^[3].Owing to having certain loss in the energy-storage system course of work of reality, the actual charge-discharge electric power P of energy-storage system _eSS[n] can be calculated by following formula:

$P_{\mathrm{ESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{ESS},0}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{ESS},0}\left[n\right]>0\\ P_{\mathrm{ESS},0}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{ESS},0}\left[n\right]<0\end{array}\right.---\left(9\right)$

Wherein, P _eSS[n] to discharge for just, η _dand η _cbe respectively discharging efficiency and the charge efficiency of energy-storage system.

Within the whole sample data cycle, the actual charge-discharge electric power P of energy storage _eSSthe maximum of [n] absolute value is the maximum charge-discharge electric power that energy-storage system should possess, i.e. rated power.

Obtain the actual charge-discharge electric power P of energy-storage system _eSSafter [n], the energy-storage system that can calculate each sampled point in a day adds up charge-discharge energy E _eSS[n]:

$E_{\mathrm{ESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ESS}}\left[n\right]*T_S,n=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_S---\left(10\right)$

In one day, energy-storage system adds up maximum, least energy and is designated as E respectively _{eSS, Max}and E _{eSS, Min}.Consider the state-of-charge constraint of energy-storage system, the rated capacity of energy-storage system can be obtained

$E_{\mathrm{ESS}}^R=\frac{E_{\mathrm{ESS},\mathrm{Max}}-E_{\mathrm{ESS},\mathrm{Min}}}{{\mathrm{SOC}}_{\mathrm{Max}}-{\mathrm{SOC}}_{\mathrm{Min}}}---\left(11\right)$

In formula, SOC _maxand SOC _minrepresent the bound constraint of energy-storage system SOC respectively.

This capacity considers the cycle efficieny of energy storage in actual motion and can not completely be full of the situation of putting, and is to reach the minimum capacity of expection needed for Compensation Objectives.

105: the active power according to the rated capacity of battery and ultracapacitor, battery and ultracapacitor obtains mixed energy storage system engineering phase net present value (NPV), the life of storage battery and ultracapacitor life-span;

In hybrid energy-storing capacity optimization method, different stored energy capacitance schemes and discharge and recharge strategy will cause the different life-spans, and the too short energy storage device caused was changed and also will directly be affected the economy of energy-storage system the life-span.For addressing this problem, this method adopts net present value (NPV) economic evaluation model, and the life-span computation model establishing energy storage is to calculate replacing construction and the number of times of energy storage.The calculating of mixed energy storage system engineering phase net present value (NPV) is divided into three steps:

1) the cash flow sequence in the computational engineering phase

At engineering phase N _pin year, assuming that the life-span of certain energy storage device is L _eSSyear, build unit price and be respectively C with renewal unit price _eSSand R _eSS, unit is unit/kWh, and operation maintenance unit price is OM _eSS, unit is unit/(kWh*).With income for just, then first this cash flow sequence of building up of energy storage device is:

$\mathrm{Cap}\left[i\right]=\left\{\begin{array}{cc}-C_{\mathrm{ESS}}*E_{\mathrm{ESS}}^R& i=0\\ 0& i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_P\end{array}\right.---\left(12\right)$

Engineering phase N _pin year, the life-span is L _eSSthe energy storage device in year experiences k renewal altogether, has

k=N _p/L _ESS(13)

Then energy storage device renewal cost cash flow sequence is

$\mathrm{Rep}\left[i\right]=\left\{\begin{array}{cc}-R_{\mathrm{ESS}}*E_{\mathrm{ESS}}^R& i=(\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,k)*L_{\mathrm{ESS}}\\ 0& i\&NotEqual;(\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,k)*L_{\mathrm{ESS}}\end{array}\right.---\left(14\right)$

Energy storage device operation expense and residual value sequence are respectively:

$\mathrm{OM}\left[i\right]=\left\{\begin{array}{cc}-{\mathrm{OM}}_{\mathrm{ESS}}*E_{\mathrm{ESS}}^R& i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_P\\ 0& i=0\end{array}\right.---\left(15\right)$

$\mathrm{Sal}\left[i\right]=\left\{\begin{array}{cc}{R}_{\mathrm{ESS}}*E_{\mathrm{ESS}}^R*\frac{N_P-k{L}_{\mathrm{ESS}}}{L_{\mathrm{ESS}}}& i=N_P\\ 0& i\&NotEqual;N_P\end{array}\right.---\left(16\right)$

Then the cash flow sequence of energy storage device can be calculated by following formula

NCT _ESS[i]=Cap[i]+Rep[i]+OM[i]+Sal[i] (17)

i=0,1,…,N _p

If the cash flow sequence of battery and ultracapacitor is respectively NCF _bESS[i] and NCF _sC[i], micro-grid system purchases strategies cash flow sequence is NCF _grid[i].Then the cash flow sequence of mixed energy storage system is

NCF[i]=NCF _BESS[i]+NCF _SC[i]+NCF _Grid[i] (18)

i=0,1,…,N _p

2) consider the time value (discount rate) of capital, calculate conversion cash flow sequence

DCF[i]=NCF[i]*(1+F _Dis) ^-i(19)

i＝0,1,…,N _p

In formula, F _disfor discount rate.

3) according to conversion cash flow sequence, mixed energy storage system engineering phase net present value (NPV) is calculated

$\mathrm{NPV}=\underset{0}{\overset{N_P}{\mathrm{\&Sigma;}}}\mathrm{DCF}\left[i\right]---\left(20\right)$

Adopt the formula (13) of economic evaluation model, (14), need the life-span of calculating accumulator and ultracapacitor respectively, method is as follows:

1) life of storage battery calculates

Adopt the rain flow method calculating accumulator life-span.A circular in definition starts charging next time after discharging into a certain depth value, then under certain depth of discharge, the permission cycle-index of energy storage device is

$C_{F,i}=a_1+a_2{e}^{a_3{\mathrm{DOD}}_i}+a_4{e}^{a_5{\mathrm{DOD}}_i}---\left(21\right)$

In formula, C _{f, i}for cycle-index, parameter [a ₁..., a ₅] determined by energy storage device cycle-index corresponding under different depth of discharge, without unit, DOD is depth of discharge.Experiencing the energy storage device life-span that multiple depth of discharge consumes altogether is

$D=\underset{i=1}{\overset{N_{\mathrm{CD}}}{\mathrm{\&Sigma;}}}{C_{F,i}}^{-1}---\left(22\right)$

N _cDrepresent different depth of discharge numbers.The then life of storage battery

$L_{\mathrm{BESS}}=\frac{1}{D\&times;(8760\&times;3600/T_S)}---\left(23\right)$

2) ultracapacitor life formula is as follows:

L _SC=N _Total/N _PY(24)

N in formula _totalfor ultracapacitor allows charge and discharge cycles number of times, N _pYfor year cycle-index, can be obtained by emulation.

During specific implementation, to the individual repeated execution of steps 103-step 105 of other in first generation population, obtain corresponding numerical value, until all individualities in the first generation all calculate complete, produce offspring flocks through preferred, hereditary and variation again, repeat above-mentioned calculating, iteration ends is in predetermined algebraically.

106: the individuality that in last generation, fitness is the highest is optimum individual, obtain optimum hybrid energy-storing combined capacity simultaneously.

In practical application, energy storage can be made to be operated in good working order according to the above-mentioned optimum hybrid energy-storing combined capacity calculated, extend the life-span of energy type energy storage, avoid the frequent updating of energy storage device.

The feasibility of a kind of hybrid energy-storing capacity optimization method for optimizing microgrid dominant eigenvalues that the embodiment of the present invention provides is described with concrete example below:

This method is verified with Tianjin ecological city one micro-grid system typical case in winter day actual operating data.This micro-grid system mainly comprises: monocrystalline and polycrystalline photovoltaic, altogether 30kW; Illumination and charging pile load, altogether 15kW.Outfit lithium ion battery and ultracapacitor optimize the agreement power realizing microgrid interconnection.

Photovoltaic power, load power data sampling period are 5min.Net load is calculated, namely without dominant eigenvalues during energy storage, as shown in Figure 2 according to distributed power source and load power.

Find out from accompanying drawing 2, net load power P _netroughly within the scope of-5kW to 15kW, fluctuation is comparatively large, and net load peak and low ebb appear at 16:00 ~ 22:00 and 9:00 ~ 14:00 respectively.Setting microgrid does not allow superior electrical network to send power, and tie-line power transmission maximum is 10kW, can think that the local net load lower limit of microgrid and upper limit threshold value are 0kW and 10kW.The Compensation Objectives of hybrid energy-storing is the power swing stabilizing interconnection hour level, carries out peak load shifting to the power exceeding net load threshold value simultaneously, makes the net load power P after energy storage compensates _netfor interconnection agreement power P _agr.Net load power P _netwith interconnection agreement power P _agras shown in Figure 3.Can find out from accompanying drawing 3, interconnection agreement power P _agrbe equivalent to two time periods net load power P _netpeak load shifting.

Think total active-power P that the distributed power source of annual every day sends _dG, load active-power P _load, interconnection agreement power P _agrand energy storage charge status is all identical with this typical case's day.Every kWh electricity price is: 1.1648 yuan, peak, low ebb 0.3778 yuan, 0.7603 yuan at ordinary times.The price of ultracapacitor and lithium battery is as shown in table 1

Table 1 energy storage economy data

Formula (21) and the middle energy storage life parameter of formula (24) are in table 2

Table 2 energy storage life parameter

In order to extend the energy storage life-span, the SOC bound of setting lithium battery and ultracapacitor is respectively 100% and 40%.Energy-storage system overall efficiency is 88%.The energy-storage system engineering phase for economic evaluation is set to 20 years.

Sampling period T _sfor 5min, be 0 ~ 0.00167Hz by the compensating frequency scope that formula (5) known hybrid energy-storing is total.Next step will determine boundary frequency f _p, be below adopt genetic algorithm to determine f _pprocess.

Setting genetic algorithm initial value is as follows: chromosome length is 16, and maximum algebraically is 50, and Population Size is 20.The Optimizing Flow of capacity shown in 6 calculates with reference to the accompanying drawings, obtains genetic process as shown in Figure 4.

From accompanying drawing 4, system Grade cut-off can be obtained through hereditary optimizing and be-35.68 ten thousand yuan.Now corresponding hybrid energy-storing boundary frequency is 0.00006Hz.Namely lithium battery compensates the low frequency component of 0 ~ 0.00006Hz, and when compensating the high fdrequency component of 0.00006 ~ 0.00167Hz by ultracapacitor, energy-storage system economy is optimum.

Can calculate lithium battery capacity by formula (11) is 6.48kW and 50.38kWh, and capacity of super capacitor is 5.27kW and 5.35kWh.The life-span that can be calculated lithium battery and ultracapacitor by formula (23) and formula (24) is respectively 23.76 and 14.42.

Mixed energy storage system this typical case's day charge-discharge electric power as shown in Figure 5.From accompanying drawing 5, because ultracapacitor compensate for the high fdrequency component of demand power, lithium battery only has 1 degree of depth discharge and recharge every day, can extend its service life well.

Bibliography

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[2] fourth is bright, Xu Ningzhou, Bi Rui. the research [J] of load side novel battery energy-accumulating power station dynamic property. and Electric Power Automation Equipment, 2011,31 (5): 1-7.

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It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1., for optimizing a hybrid energy-storing capacity optimization method for microgrid dominant eigenvalues, it is characterized in that, said method comprising the steps of:

(1) be optimization aim to the maximum with mixed energy storage system net present value (NPV), set up and be applied to the battery of micro-grid system and the hybrid energy-storing capacity Optimized model of ultracapacitor;

(2) by the fitness function of hybrid energy-storing capacity Optimized model structure genetic algorithm, and the sample data of the total charge-discharge electric power of hybrid energy-storing is obtained; Binary coding is carried out to frequency range, produces first generation boundary frequency f _ppopulation;

(3) according to the active-power P of hybrid energy-storing capacity Optimized model to arbitrary individual calculating accumulator and the ultracapacitor respectively in first generation population _bESSand P _sC;

(4) by the active-power P of battery and ultracapacitor _bESSand P _sCthe rated capacity of calculating accumulator and ultracapacitor respectively;

(5) mixed energy storage system engineering phase net present value (NPV), the life of storage battery and ultracapacitor life-span is obtained according to the active power of the rated capacity of battery and ultracapacitor, battery and ultracapacitor;

(6) to the individual repeated execution of steps of other in first generation population (3)-step (5), obtain corresponding numerical value, until all individualities in the first generation all calculate complete, offspring flocks is produced again through preferred, hereditary and variation, repeat above-mentioned calculating, iteration ends is in predetermined algebraically; The individuality that in last generation, fitness is the highest is optimum individual, obtains optimum hybrid energy-storing combined capacity simultaneously;

Wherein, hybrid energy-storing capacity Optimized model is specially:

f＝min(-NPV) (1)

P _HESS＝P _BESS+P _SC＝P _Agr-P _Net(2)

P _Net＝P _Load-P _DG(3)

${\mathrm{\&eta;}}_d={\mathrm{\&eta;}}_c=\sqrt{\mathrm{\&eta;}}---\left(4\right)$

0＜f _P＜1/2T _S(5)

$\begin{array}{c}{\mathrm{SOC}}_{\mathrm{Min}}\&le;\mathrm{SOC}\left[n\right]\&le;{\mathrm{SOC}}_{\mathrm{Max}}\\ n=\mathrm{1,2}...,N_S\end{array}---\left(6\right)$

In formula (1), NPV refers to the net present value (NPV) of mixed energy storage system within the engineering phase; F represents object function;

The power balance equation that formula (2) is microgrid inside and interconnection to formula (3); P _dGfor total active power that distributed power source sends; P _loadfor local load active power; P _hESSfor total active power of mixed energy storage system; P _bESSand P _sCbe respectively the active power of battery and ultracapacitor; P _netfor the net load power of micro-grid system; P _agrfor interconnection agreement power;

In formula (4), η _d, η _cthe overall efficiency of the discharging efficiency of energy-storage system, charge efficiency and a charging-discharging cycle is respectively with η;

Formula (5) f _pbe the boundary frequency that two kinds of energy storage compensate frequency range, T _sfor the sampling period of sample data;

N in formula (6) _sfor the sampled point number of sample data; SOC _maxand SOC _minrepresent the bound constraint of energy-storage system SOC respectively;

Wherein, the calculating of mixed energy storage system engineering phase net present value (NPV) is specially:

1) the cash flow sequence in the computational engineering phase;

2) consider the time value of capital, calculate conversion cash flow sequence;

3) according to conversion cash flow sequence, mixed energy storage system engineering phase net present value (NPV) is calculated;

Wherein, the calculating in the life of storage battery and ultracapacitor life-span is specially:

1) life of storage battery calculates:

The life of storage battery

$L_{\mathrm{BESS}}=\frac{1}{D\&times;(8760\&times;3600/T_S)}$

Wherein, energy storage device life-span of altogether consuming for the multiple depth of discharge of experience of D;

2) the ultracapacitor life-span calculates:

L _SC＝N _Total/N _PY

N in formula _totalfor ultracapacitor allows charge and discharge cycles number of times, N _pYfor year cycle-index.

2. a kind of hybrid energy-storing capacity optimization method for optimizing microgrid dominant eigenvalues according to claim 1, it is characterized in that, described according to the active-power P of hybrid energy-storing capacity Optimized model to arbitrary individual calculating accumulator and the ultracapacitor respectively in first generation population _bESSand P _sCbe specially:

$\left\{\begin{array}{c}{P}_{\mathrm{SC}}=P_{\mathrm{HESS}}*\frac{T_{\mathrm{HP}}*s}{T_{\mathrm{HP}}*s+1}\\ P_{\mathrm{BESS}}=P_{\mathrm{HESS}}-P_{\mathrm{SC}}\end{array}\right.---\left(7\right)$

In formula, s is the complex variable of Laplace transform; T _hPfor the time constant of high-pass filter;

$T_{\mathrm{HP}}=\frac{1}{2\mathrm{\&pi;}{f}_P}---\left(8\right).$

3. a kind of hybrid energy-storing capacity optimization method for optimizing microgrid dominant eigenvalues according to claim 1, is characterized in that, the described active-power P by battery and ultracapacitor _bESSand P _sCthe rated capacity of calculating accumulator and ultracapacitor is specially respectively:

With P _{eSS, 0}[n] represents the charge-discharge electric power of certain energy storage to introduce capacity calculation methods;

The actual charge-discharge electric power of energy-storage system $P_{\mathrm{ESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{ESS},0}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{ESS},0}\left[n\right]>0\\ P_{\mathrm{ESS},0}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{ESS},0}\left[n\right]<0\end{array}\right.---\left(9\right)$

Wherein, P _eSS[n] to discharge for just, η _dand η _cbe respectively discharging efficiency and the charge efficiency n=1 of energy-storage system, 2 ..., N _s;

$E_{\mathrm{ESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ESS}}\left[n\right]*T_{S}n=\mathrm{1,2},...,N_S---\left(10\right)$

In one day, energy-storage system adds up maximum, least energy and is designated as E respectively _{eSS, Max}and E _{eSS, Min}, rated capacity

$E_{\mathrm{ESS}}^R=\frac{E_{\mathrm{ESS},\mathrm{Max}}-E_{\mathrm{ESS},\mathrm{Min}}}{{\mathrm{SOC}}_{\mathrm{Max}}-{\mathrm{SOC}}_{\mathrm{Min}}}---\left(11\right)$

In formula, SOC _maxand SOC _minrepresent the bound constraint of energy-storage system SOC respectively.