CN104600727A - Method for configuring capacity of hybrid energy storage in micro-grid based on mathematical statistic and wavelet decomposition algorithm - Google Patents

Abstract

The invention discloses a method for configuring capacity of hybrid energy storage in a micro-grid based on mathematical statistic and a wavelet decomposition algorithm. The method comprises the steps of obtaining a theoretical power demand signal of an energy storage unit according to a small hydropower unit, a photovoltaic unit, a wind power generation unit and load of a micro-grid system, adopting the wavelet decomposition algorithm to analyze the frequency spectrum feature of the theoretical power demand signal, preliminarily determining the dividing frequency fN of hybrid energy storage, obtaining the power and capacity of the hybrid energy storage according to the dividing frequency fN, conducting confidence level and economical evaluation on an energy storage configuration result based on the mathematical statistic, determining the dividing frequency fN of the hybrid energy storage if an evaluation result does not meet the requirement and completing the step if the evaluation result meets the requirement. The method has practical significance on saving of one-time investment and improvement of power supply reliability of the micro-grid.

Description

A kind of capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm

Technical field

The present invention relates to a kind of capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm, belong to micro-capacitance sensor technical field of energy storage.

Background technology

Microgrid, as a kind of novel energy networking supply and administrative skill, facilitates the access of distributed energy resource system, can realize the maximum using of dsm and the existing energy simultaneously.But generally occupy larger proportion owing to having intermittent renewable energy power generation, make micro-grid system bear the ability of disturbance relatively weak.In order to give full play to advantage and the benefit of renewable energy power generation, balance its random fluctuation, maintenance system is stablized, and improves the quality of power supply, provides uninterrupted power supply function etc., just must be equipped with the energy-storage units of certain capacity in systems in which simultaneously.From functional realiey angle, energy storage technology is divided into power-type and energy type two class.Power-type energy storage power density is large, and efficiency for charge-discharge is high, has extended cycle life, and be adapted to very much the occasion of high-power discharge and recharge and cycle charge-discharge, but energy density is relatively on the low side, such as ultracapacitor; Energy type energy storage energy density is large, and efficiency for charge-discharge is low, and cycle life is short, but power density is little, and the adaptability of, high-power discharge and recharge responsive to charge and discharge process and frequent discharge and recharge is strong, such as storage battery.Although energy storage device can play the effect suppressing regenerative resource output-power fluctuation to a certain extent, but single energy storage device is difficult to the requirement meeting power and energy two aspect simultaneously, domestic and international academia proposes to utilize energy type energy storage and power-type energy storage composition mixed energy storage system.Mixed energy storage system has the advantage that power density is large and energy density is high simultaneously, has effectively played the complementary characteristic of each energy storage device.

The energy accumulation capacity configuration announced at present mainly contains: difference replenishment, and fluctuation stabilizes analytic approach, economic performance optimization etc.Difference replenishment is comparatively traditional capacity collocation method, and this collocation method is very simple, does not need the modeling by complexity and calculating; But the method does not consider the dynamic change of energy storage system capacity in actual motion, configuration capacity is often accurate not.Fluctuation is stabilized analytic approach and is mainly carried out distributing rationally of stored energy capacitance according to the effect of stabilizing of energy storage to fluctuating power, and different optimization angles has different capacity collocation methods, causes computational methods complicated.Economic performance optimization mainly sets up the economical operation model of system, sets up target function and constraints, and stored energy capacitance, as one of them optimized variable, adopts different optimized algorithms to be optimized and solves, and causes computational methods complicated equally.

Summary of the invention

For the deficiency that prior art exists, the object of the invention is to provide that a kind of collocation method is simple, configuration capacity based on the capacity collocation method of hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm, achieves safe, reliable, the economical operation of micro-capacitance sensor accurately.

To achieve these goals, the present invention realizes by the following technical solutions:

A kind of capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm of the present invention, specifically comprises following step:

(1) according to the actual measurement sample data of small power station's unit, photovoltaic cells, wind power generation unit and the load in micro-grid system, the theoretical power requirement signal of energy-storage units is obtained;

(2) adopt wavelet decomposition algorithm, analyze the spectrum signature of described theoretical power requirement signal;

(3) according to the spectral decomposition result that step (2) obtains, by observing spectral magnitude size, the boundary frequency f of hybrid energy-storing is tentatively determined _n;

(4) according to described boundary frequency f _n, obtain power and the capacity of hybrid energy-storing; Wherein, power demand signal decomposes rear frequency lower than boundary frequency f _n, compensated by energy type energy storage; Power demand signal decomposes rear frequency higher than boundary frequency f _n, compensated by power-type energy storage;

(5) based on mathematical statistics, confidence level and Economic Evaluation are carried out to the configuration result of hybrid energy-storing capacity, if evaluation result is undesirable, then turns to step (3), if evaluation result meets re-set target, then terminate.

In step (1), the actual measurement sample data of described small power station unit, photovoltaic cells, wind power generation unit and load chooses the typical day measured data of micro-capacitance sensor in summer, winter.

In step (2), by selected wavelet basis function, N layer wavelet decomposition is carried out to described theoretical power requirement signal, obtains high fdrequency component and low frequency component.

The method of above-mentioned theory power demand signal being carried out to N layer wavelet decomposition is as follows:

First wavelet decomposition is carried out to theoretical power requirement signal, obtain low frequency coefficient and the high frequency coefficient of signal; Choose low frequency part wherein and carry out wavelet decomposition again on 1/2 yardstick of archeus, until Decomposition order is N; Realize carrying out N layer wavelet decomposition to described theoretical power requirement signal by wavelet analysis tool box in Matlab.

What above-mentioned wavelet basis function was selected is that sym7, N get 6.

In step (4), storage battery is chosen in described energy type energy storage, and ultracapacitor is chosen in described power-type energy storage.

What the power division of hybrid energy-storing adopted is high pass filter, and the computational methods of power division are as follows:

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

In formula, T _hPfor the time constant of high pass filter, time constant is by boundary frequency f _nbe calculated as follows and obtain:

$T_{\mathrm{HP}}=\frac{1}{2\mathrm{\π}{f}_N}---\left(2\right)$

In formula, P _hESSfor hybrid energy-storing power, P _sCfor ultracapacitor power, P _bESSfor storage battery power.

According to the P that power division obtains _bESS[n] and P _sC[n], the respectively capacity of calculating accumulator and ultracapacitor;

Battery capacity computational methods are identical with capacity of super capacitor computational methods; The computational methods of described battery capacity are as follows:

Because meeting in the energy-storage system of accumulator course of work of reality is lossy, the actual charge-discharge electric power instruction of energy-storage system of accumulator can be calculated by following formula:

$P_{\mathrm{BESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{BESS}}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{BESS}}\left[n\right]>0\\ P_{\mathrm{BESS}}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{BESS}}\left[n\right]<0\end{array}\right.---\left(3\right)$

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

After obtaining the actual charge-discharge electric power instruction of energy-storage system of accumulator, the energy-storage system of accumulator calculating each sampled point in a day adds up charge-discharge energy

$E_{\mathrm{BESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{BESS}}\left[n\right]*T_{S}n=\mathrm{1,2},...,N_S---\left(4\right)$

Wherein, T _sfor the sample data sampling period, N _sfor specimen sample point number;

In one day, energy-storage system of accumulator adds up maximum, least energy and is designated as E respectively _{bESS, Max}and E _{bESS, Min}, consider the state-of-charge constraint of energy-storage system of accumulator, the rated capacity obtaining energy-storage system of accumulator is

$E_{\mathrm{BESS}}^R=\frac{E_{\mathrm{BESS},\mathrm{Max}}-E_{\mathrm{BESS},\mathrm{Min}}}{\mathrm{SO}{C}_{\mathrm{Max}}-\mathrm{SO}{C}_{\mathrm{Min}}}---\left(5\right)$

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

The evaluation method of above-mentioned confidence level is as follows:

According to step (2) intermediate frequency spectrum decomposition result, Mathematical Statistics Analysis is carried out to power-type energy storage and energy type energy storage, and the respective probability density curve of matching and cumulative probability distribution curve, obtain the power of power-type energy storage under different confidence level and energy type energy storage; Whether the power of the hybrid energy-storing obtained in determining step (4) and confidence level corresponding to capacity be more than 90%, if more than 90%, then confidence level meets the requirements, otherwise undesirable.

The evaluation method of above-mentioned energy storage economy is as follows:

According to the power of the hybrid energy-storing obtained in step (4) and capacity, obtain the number of ultracapacitor and storage battery, thus calculate the cost of hybrid energy-storing;

The cost of energy storage adopts the one-time investment of energy storage to represent, computing formula is as follows:

f _cost＝n ₁·M _sc+n ₂·M _bess(6)

In formula, n ₁, n ₂for the number of ultracapacitor, storage battery; M _scfor the unit price of ultracapacitor; M _bessfor the unit price of storage battery; The economy of the correspondence of the hybrid energy-storing obtained in determining step (4), under identical confidence level, answers the allocation plan that alternative costs are low.

The present invention utilizes the boundary frequency of wavelet decomposition algorithm picks hybrid energy-storing, and then obtains power and the capacity of power-type energy storage and energy type energy storage in hybrid energy-storing; And carry out confidence level assessment in conjunction with mathematical statistics, ensure that the reliability that in micro-capacitance sensor, energy storage is exerted oneself, consider the Financial cost of energy storage simultaneously, be conducive to the economical operation of micro-capacitance sensor.Therefore, this method, for saving one-time investment, improves the power supply reliability of micro-capacitance sensor, has realistic meaning.

Accompanying drawing explanation

Fig. 1 is the capacity collocation method workflow diagram of hybrid energy-storing in micro-capacitance sensor;

Fig. 2 is wavelet decomposition single step schematic flow sheet;

Fig. 3 is three layers of wavelet decomposition schematic diagram.

Embodiment

The technological means realized for making the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with embodiment, setting forth the present invention further.

See Fig. 1, a kind of capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm of the present invention, specifically comprises the steps:

Step (1), according to microgrid apoplexy/light/water and load data, determines the theoretical power requirement of energy-storage units.

First be loaded into the actual measurement sample data of small power station's unit, photovoltaic cells, wind power generation unit and load in micro-grid system, obtain the theoretical power requirement of energy-storage units.Generally choose typical day data of Various Seasonal (summer, winter), determine maximum power capabilities needed for whole typical Japan-China energy storage.

Step (2), adopts wavelet decomposition algorithm, the spectrum signature of the theoretical power requirement signal described in analytical procedure (1).

Wavelet transformation is adopted to decompose theoretical power requirement signal delta P signal.By selected a kind of wavelet basis function, N layer wavelet decomposition is carried out to signal, obtain its high and low frequency component.Select sym7 to be wavelet basis function in the present embodiment, N gets 6.

Signal in the present embodiment is discrete theoretical power requirement signal, therefore needs the wavelet transformation using discrete series.First wavelet transformation is carried out to the signal compared with large scale, then choose low frequency part wherein carry out wavelet transformation again on 1/2 yardstick of archeus.The schematic flow sheet of wavelet decomposition algorithm as shown in Figure 2.Wherein, Lo_D represents low pass filter, and Hi_D represents high pass filter, and ↓ 2 represent with yardstick to be 2 carry out down-sampling.CA ₁for decomposing the approximation coefficient (or claiming low frequency coefficient) obtained, cD ₁for decomposing the detail coefficients (or claiming high frequency coefficient) obtained.Same way, decomposes low frequency part wherein again as signal, and decomposition number of times mentions Decomposition order N before being.The multi-resolution decomposition schematic diagram of signal as shown in Figure 3.

Step (3), according to the spectrum signature described in step (2), namely spectral decomposition result, tentatively determines boundary frequency.

Force signal is gone out and in conjunction with its spectrum signature determination boundary frequency f according to energy storage theory _n.Frequency is lower than f _nenergy storage exert oneself and to be realized by energy type energy storage, frequency is higher than f _nenergy storage exert oneself and to be realized by power-type energy storage.In the present embodiment, ultracapacitor is chosen in power-type energy storage, and storage battery is chosen in energy type energy storage.

Step (4), according to the boundary frequency described in step (3), carries out hybrid energy-storing power division and capacity configuration.

Adopt high pass filter to carry out hybrid energy-storing power division, computational methods are as follows:

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

T in formula _hPfor the time constant of high pass filter.This time constant is by boundary frequency f _nbe calculated as follows and obtain:

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

P in formula _hESSfor hybrid energy-storing power, P _sCfor ultracapacitor power, P _bESSstorage battery power.

According to the P that power division obtains _bESS[n] and P _sC[n], can distinguish the capacity of calculating accumulator and ultracapacitor.The capacity calculation methods of two kinds of energy storage is general, below only with P _eSS[n] represents the charge-discharge electric power instruction of certain energy storage to introduce capacity calculation methods.

Owing to having certain loss in the energy-storage system course of work of reality, the actual charge-discharge electric power instruction of energy-storage system can be calculated by following formula:

$P_{\mathrm{BESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{BESS}}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{BESS}}\left[n\right]>0\\ P_{\mathrm{BESS}}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{BESS}}\left[n\right]<0\end{array}\right.---\left(3\right)$

Wherein, P _eSS[n] is 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, energy storage actual charge-discharge electric power instruction P _eSSthe maximum of [n] absolute value is the maximum charge-discharge electric power that energy-storage system should possess, i.e. rated power.

After obtaining the actual charge-discharge electric power instruction of energy-storage system, the energy-storage system that can calculate each sampled point in a day adds up charge-discharge energy.

$E_{\mathrm{BESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{BESS}}\left[n\right]*T_{S}n=\mathrm{1,2},...,N_S---\left(4\right)$

In formula, T _sfor the sample data sampling period, N _sfor specimen sample point number.

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.

$E_{\mathrm{ESS}}^R=\frac{E_{\mathrm{ESS},\mathrm{Max}}-E_{\mathrm{ESS},\mathrm{Min}}}{\mathrm{SO}{C}_{\mathrm{Max}}-\mathrm{SO}{C}_{\mathrm{Min}}}---\left(5\right)$

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

Step (5), based on mathematical statistics and energy storage economy, energy storage configuration is evaluated, if evaluation result is undesirable, then repeat step (3), (4), (5), until evaluation result meets re-set target.

Confidence level and Economic Evaluation are carried out to energy storage configuration result.Specific as follows:

1. confidence level evaluation.According to step (2) intermediate frequency spectrum analysis result, Mathematical Statistics Analysis is carried out to power-type and energy type storage component, and the respective probability density curve of matching and cumulative probability distribution curve, obtain the power of power-type under different confidence level and energy type energy storage.The energy storage power that larger confidence level is corresponding and capacity, can meet micro-capacitance sensor reliability service needs.Whether the confidence level corresponding with capacity of the power of the hybrid energy-storing therefore obtained in determining step (4) be more than 90%, if more than 90%, then confidence level meets the requirements, otherwise undesirable.

2. energy storage Economic Evaluation.According to the power of the hybrid energy-storing obtained in step (4) and capacity, the number of ultracapacitor and storage battery can be obtained, thus calculate the cost of hybrid energy-storing.The cost of energy storage adopts the one-time investment of energy storage to represent, computing formula is as follows:

f _{cos t}＝n ₁·M _sc+n ₂·M _bess(6)

In formula, n ₁, n ₂for the number of ultracapacitor, storage battery; M _scfor the unit price of ultracapacitor; M _bessfor the unit price of storage battery.

The operation of micro-capacitance sensor also will meet cost-effectiveness requirement, the economy that the hybrid energy-storing therefore obtained in determining step (4) is corresponding simultaneously, under identical confidence level, answers the allocation plan that alternative costs are lower.

More than show and describe general principle of the present invention and principal character and advantage of the present invention.The technical staff of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and specification just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection range is defined by appending claims and equivalent thereof.

Claims (10)

1., based on a capacity collocation method for hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm, it is characterized in that, specifically comprise following step:

(1) according to the actual measurement sample data of small power station's unit, photovoltaic cells, wind power generation unit and the load in micro-grid system, the theoretical power requirement signal of energy-storage units is obtained;

(2) adopt wavelet decomposition algorithm, analyze the spectrum signature of described theoretical power requirement signal;

(3) according to the spectral decomposition result that step (2) obtains, by observing spectral magnitude size, the boundary frequency f of hybrid energy-storing is tentatively determined _n;

(4) according to described boundary frequency f _n, obtain power and the capacity of hybrid energy-storing; Wherein, power demand signal decomposes rear frequency lower than boundary frequency f _n, compensated by energy type energy storage; Power demand signal decomposes rear frequency higher than boundary frequency f _n, compensated by power-type energy storage;

(5) based on mathematical statistics, confidence level and Economic Evaluation are carried out to the configuration result of hybrid energy-storing capacity, if evaluation result is undesirable, then turns to step (3), if evaluation result meets re-set target, then terminate.

2. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 1, is characterized in that,

In step (1), the actual measurement sample data of described small power station unit, photovoltaic cells, wind power generation unit and load chooses the typical day measured data of micro-capacitance sensor in summer, winter.

3. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 1, is characterized in that,

In step (2), by selected wavelet basis function, N layer wavelet decomposition is carried out to described theoretical power requirement signal, obtains high fdrequency component and low frequency component.

4. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 3, is characterized in that,

The method of described theoretical power requirement signal being carried out to N layer wavelet decomposition is as follows:

First wavelet decomposition is carried out to theoretical power requirement signal, obtain low frequency coefficient and the high frequency coefficient of signal; Choose low frequency part wherein and carry out wavelet decomposition again on 1/2 yardstick of archeus, until Decomposition order is N; Realize carrying out N layer wavelet decomposition to described theoretical power requirement signal by wavelet analysis tool box in Matlab.

5. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 3, is characterized in that,

What described wavelet basis function was selected is that sym7, N get 6.

6. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 1, it is characterized in that, in step (4), storage battery is chosen in described energy type energy storage, and ultracapacitor is chosen in described power-type energy storage.

7. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 6, is characterized in that,

What the power division of hybrid energy-storing adopted is high pass filter, and the computational methods of power division are as follows:

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

In formula, T _hPfor the time constant of high pass filter, time constant is by boundary frequency f _nbe calculated as follows and obtain:

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

In formula, P _hESSfor hybrid energy-storing power, P _sCfor ultracapacitor power, P _bESSfor storage battery power.

8. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 7, is characterized in that,

According to the P that power division obtains _bESS[n] and P _sC[n], the respectively capacity of calculating accumulator and ultracapacitor;

Battery capacity computational methods are identical with capacity of super capacitor computational methods; The computational methods of described battery capacity are as follows:

Because meeting in the energy-storage system of accumulator course of work of reality is lossy, the actual charge-discharge electric power instruction of energy-storage system of accumulator can be calculated by following formula:

$P_{\mathrm{BESS}}\left[n\right]=\left\{\begin{array}{cc}{P}_{\mathrm{BESS}}\left[n\right]/{\mathrm{\&eta;}}_d& P_{\mathrm{BESS}}\left[n\right]>0\\ P_{\mathrm{BESS}}\left[n\right]*{\mathrm{\&eta;}}_c& P_{\mathrm{BESS}}\left[n\right]<0\end{array}\right.---\left(3\right)$

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

After obtaining the actual charge-discharge electric power instruction of energy-storage system of accumulator, the energy-storage system of accumulator calculating each sampled point in a day adds up charge-discharge energy

$E_{\mathrm{BESS}}\left[n\right]=\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_{\mathrm{BESS}}\left[n\right]*T_S,n=\mathrm{1,2}\&CenterDot;\&CenterDot;\&CenterDot;,N_S---\left(4\right)$

Wherein, T _sfor the sample data sampling period, N _sfor specimen sample point number;

In one day, energy-storage system of accumulator adds up maximum, least energy and is designated as E respectively _{bESS, Max}and E _{bESS, Min}, consider the state-of-charge constraint of energy-storage system of accumulator, the rated capacity obtaining energy-storage system of accumulator is

$E_{\mathrm{BESS}}^R=\frac{E_{\mathrm{BESS},\mathrm{Max}}-E_{\mathrm{BESS},\mathrm{Min}}}{{\mathrm{SOC}}_{\mathrm{Max}}-{\mathrm{SOC}}_{\mathrm{Min}}}---\left(5\right)$

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

9. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 6, is characterized in that,

The evaluation method of described confidence level is as follows:

According to step (2) intermediate frequency spectrum decomposition result, Mathematical Statistics Analysis is carried out to power-type energy storage and energy type energy storage, and the respective probability density curve of matching and cumulative probability distribution curve, obtain the power of power-type energy storage under different confidence level and energy type energy storage; Whether the power of the hybrid energy-storing obtained in determining step (4) and confidence level corresponding to capacity be more than 90%, if more than 90%, then confidence level meets the requirements, otherwise undesirable.

10. the capacity collocation method based on hybrid energy-storing in the micro-capacitance sensor of mathematical statistics and wavelet decomposition algorithm according to claim 9, is characterized in that,

The evaluation method of described energy storage economy is as follows:

According to the power of the hybrid energy-storing obtained in step (4) and capacity, obtain the number of ultracapacitor and storage battery, thus calculate the cost of hybrid energy-storing;

The cost of energy storage adopts the one-time investment of energy storage to represent, computing formula is as follows:

f _cost＝n ₁·M _sc+n ₂·M _bess(6)

In formula, n ₁, n ₂for the number of ultracapacitor, storage battery; M _scfor the unit price of ultracapacitor; M _bessfor the unit price of storage battery; The economy of the correspondence of the hybrid energy-storing obtained in determining step (4), under identical confidence level, answers the allocation plan that alternative costs are low.