CN104779630B - A kind of mixed energy storage system capacity collocation method for stabilizing wind power output power fluctuation - Google Patents

Abstract

The invention discloses a kind of mixed energy storage system capacity collocation method for stabilizing wind power output power fluctuation, wind power wave component is extracted using improved moving average method with reference to national wind-electricity integration standard, the state-of-charge of meter and energy storage device distributes fluctuating power using the high-pass filtering method of variable time constant filter, the super-charge super-discharge of energy storage device can be avoided, finally capacity configuration model is set up so that energy storage device Life cycle economy is optimal for object function, compared to traditional method for only considering disposable initial outlay, more meet the actual conditions of energy-storage system longtime running, there is directive significance to Practical Project construction.

Description

A kind of mixed energy storage system capacity collocation method for stabilizing wind power output power fluctuation

Technical field

The invention belongs to micro-capacitance sensor technical field of energy storage, it is related to a kind of mixed energy storage system capacity collocation method, specifically relates to And a kind of mixed energy storage system capacity configuration optimizing method for stabilizing wind power output power fluctuation.

Background technology

With green, cleaning, it is renewable the features such as wind generating technology be widely used in worldwide And development, installed capacity increases year by year, is to solve world today's energy crisis and the effective way of problem of environmental pollution.But wind The power output of force generating system is influenceed by external factors such as natural conditions, with intermittent and randomness, extensive wind Electric grid-connected reliable and stable operation and the quality of power supply by bulk power grid is adversely affected, it is therefore desirable to configure certain capacity Energy-storage system is to stabilize wind power output power fluctuation.

Traditional energy type energy-storage system using battery as representative has higher specific capacity, is adapted to long period yardstick Discharge and recharge, be generally used to meet load power demands.Wind power output power fluctuate except long time scale wave component with Outside, also exist it is substantial amounts of in short-term, the wave component of peak value is, it is necessary to possess fast-response energy and charge-discharge electric power is larger Energy-storage system is stabilized, it is clear that traditional energy-storage system of accumulator can not meet requirement, it is necessary to ultracapacitor Coordinate for the power-type energy-storage system of representative, collectively form mixed energy storage system to carry out regenerative resource output-power fluctuation Stabilize.

Configuration mixed energy storage system capacity three technological difficulties be：(1) extraction of wind power output power wave component； (2) distribution of the wind power output power wave component between different type energy storage device；(3) scientific and reasonable capacity configuration is set up Model.

For technological difficulties (1), conventional method is more based on LPF method, easily cause stabilize component it is excessive so as to So that energy storage system capacity configuration is excessive, economy is poor and can not effectively combine the related wind-electricity integration standard of country；

For technological difficulties (2), conventional method can not count and energy storage device state-of-charge, may make in the assignment procedure Into the super-charge super-discharge of energy storage device；

For technological difficulties (3), conventional method regularly exists necessarily really in the foundation of optimization aim and constraints It is not enough.

The content of the invention

In order to solve above-mentioned technical problem, the present invention proposes a kind of hybrid energy-storing for stabilizing wind power output power fluctuation Power system capacity Optimal Configuration Method.

The technical solution adopted in the present invention is：A kind of mixed energy storage system capacity for stabilizing wind power output power fluctuation is matched somebody with somebody Put method, it is characterised in that comprise the following steps：

Step 1：The real data that active power of wind power field is exported is handled, with improved moving average method and tied National wind-electricity integration standard is closed, extracting in wind power output power needs the wave component that energy-storage system is stabilized；

Step 2：The state-of-charge of meter and each energy storage device, with the high-pass filtering method of variable time constant filter in difference The distribution of wave component is carried out between type energy storage device；

Step 3：Set up the hybrid energy-storing selected topic capacity for the purpose of optimal by mixed energy storage system Life cycle economy Optimal Allocation Model, is constraint bar with the technical characteristic of energy storage device and wind-electricity integration requirement with modified particle swarm optiziation Part, carries out mixed energy storage system capacity and distributes rationally.

Preferably, the national wind-electricity integration standard of improved moving average method and combination described in step 1, extracts wind-powered electricity generation The wave component for needing energy-storage system to be stabilized in power output, it, which implements process, includes following sub-step：

Step 1.1：Smooth grid-connected component is calculated with improved moving average method and needs energy-storage system to carry out power The wave component handled up；

Assuming that sliding window numerical value is N min, and assume that N is described for even number to facilitate, thus obtain t wind-powered electricity generation Grid-connected component and min grades of undulate quantities, as shown in formula (1.1), (1.2), (1.3)：

P_ft=(P_t-(N/2-1)+P_t-(N/2-2)+...+P_t+...+P_t+N/2)/N (1.1)；

P_mt=P_t-P_ft(1.2)；

T=N/2, N/2+1 ..., M-N/2 (1.3)；

In formula：P_tIt is the wind power of actual measurement in t minutes；P_ftIt is grid-connected component；P_mtIt is min grades of wave components；M is measurement Point sum；

Step 2：With reference to the requirement of national wind-electricity integration power swing, improved on the basis of moving average method, for The part for being unsatisfactory for fluctuating change rate is adjusted, and finally gives the compensation power P of mixed energy storage system_HESS；

P_HESS=P_mt+△P (1.4)；

In formula：Δ P is the power being adjusted according to the requirement of wind-powered electricity generation peak power fluctuating change rate.

Preferably, N=15min.

Preferably, described national wind-electricity integration standard is see table 1；

The national wind-electricity integration standard of table 1

Preferably, the high-pass filtering method of the variable time constant filter of utilization described in step 2 is in different type energy storage The distribution of wave component is carried out between equipment, it, which implements process, includes following sub-step：

Step 2.1：With the compensation power P of high-frequency filter equalizer mixed energy storage system_HESSAs shown in formula (2.1)：

Wherein, P_UCAnd P_BESSIt is that ultracapacitor and battery stabilize power, T respectively_UCIt is the filtering of high-pass filter Time constant.

Step 2.2：Time constant filter is adjusted, is modified using fuzzy control theory；Described fuzzy control theory Core content include：

Obfuscation：Input variable in the range of domain is subjected to Fuzzy processing, fuzzy subset and membership function is obtained；

Formulate fuzzy rule：It is fuzzy control sentence by the subjective control decision expression of people, passes through a plurality of fuzzy control language The domination set that sentence is formed under different condition, this is the core of fuzzy control；

Fuzzy reasoning：Fuzzy quantity is handled according to fuzzy rule；

De-fuzzy；The precise control amount that fuzzy quantity is changed into domain；

Step 2.3：Based on above-mentioned fuzzy control theory, when progress hybrid energy-storing stabilizes power swing, it is considered to each energy storage The state-of-charge of equipment, when state-of-charge approaches bound, adjusts time constant filter, corrects the charge and discharge of energy storage device in time Electrical power is instructed, so as to ensure that the state-of-charge of energy storage device is within zone of reasonableness all the time.

Preferably, the implementation process of the fuzzy control described in step 2 is, when energy storage device state-of-charge is close to the upper limit When, if obtaining charge power instruction, regulation time constant filter suitably reduces charge power, it is to avoid overcharge；Work as energy storage device When state-of-charge is close to lower limit, if obtaining discharge power instruction, regulation time constant filter reduces discharge power, it is to avoid mistake Put.

Preferably, the implementation process of the fuzzy control described in step 2 is, with storage battery charge state SoC_BESSWith it is super Level capacitor state-of-charge SoC_UCAs input, using the correction factor k of time constant filter as output；

First to SoC_BESSAnd SoC_UCIt is normalized, obtaining its degree of membership is：

SoC in formula_midFor the median of energy storage device state-of-charge；

By SoC_min≤SoC≤SoC_maxUnderstand ξ_batAnd ξ_ucContinuous domain be respectively [- a, a] and [- b, b], a and b tool Body size is relevant with energy storage device technical characteristic, because the depth of discharge of ultracapacitor is more than battery, therefore b>A and 0<b<1； When energy storage device degree of membership is a or b, capacity saturation is represented, electricity has been completely filled with；When energy storage device degree of membership is-a or-b When, represent that capacity is exhausted, discharge completely；ξ_batAnd ξ_ucFuzzy set be { NB (negative big), ZO (zero), PB (honest) }；Fuzzy Control The output variable of system is the correction factor k of time constant filter, and its discrete domain is [- 1, -0.5,0,0.5,1], and fuzzy set is { NB, NS (negative small), ZO, PS (just small), PB }；

After the fuzzy set and membership function of input quantity and output quantity is established, fuzzy rule is now formulated：

Work as P_HESS>During the instruction that 0, i.e. mixed energy storage system are discharged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) filtering time is suitably tuned up during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal Constant T_UC, increase the discharge power of ultracapacitor, reduce the discharge power of battery；

(3) filtering time is suitably turned down during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal Constant T_UC, reduce the discharge power of ultracapacitor, increase the discharge power of battery；

Work as P_HESS<During the instruction that 0, i.e. mixed energy storage system are charged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) filtering time is suitably turned down during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal Constant T_UC, reduce the charge power of ultracapacitor, increase the charge power of battery；

(3) filtering time is suitably tuned up during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal Constant T_UC, increase the charge power of ultracapacitor, reduce the charge power of battery；

, be to the state-of-charges of two kinds of energy storage devices before state-of-charge judgement is carried out to battery and ultracapacitor It is classified, is divided into S_max、S_high、S_mid、S_lowAnd S_minFive classes；When state-of-charge is in intermediateness, it is not necessary to change Time constant filter；When state-of-charge is relatively low, limitation electric discharge increases charge power；When state-of-charge is higher, limitation is filled Electricity, increases discharge power；

Because battery is different with the technical characteristic of ultracapacitor, the five class state-of-charges of the two also difference, tool Body is categorized as table 2 below；

Table 2：Five class state-of-charges of battery and ultracapacitor

To obtain output quantity time constant filter correction factor k (- 1≤k≤1), it is necessary to which de-fuzzy obtains accurate defeated Go out, de-fuzzy processing is carried out as shown in formula (2.4) using weighted mean method；

In formula：

f_1i(SoC_BESS) it is input quantity SoC_BESSBe subordinate to angle value i-th；f_2j(SoC_UC) it is input quantity SoC_UCJ-th It is subordinate to angle value；

Obtaining revised time constant filter is：

T_UC ^*=(1+k) T_UC (2.5)。

Preferably, the hybrid energy-storing selected topic capacity Optimal Allocation Model described in step 3, its object function and constraint bar Part is specially：

Shown in described object function such as formula (3.1)：

In formula：C is the average annual expense of mixed energy storage system；Y is energy-storage system run time；C_ivIt is purchasing into for energy storage device This (investment cost)；M and n are the equipment number of battery and super capacitor respectively；O_batAnd O_ucIt is battery respectively With the unit price of super capacitor；C_dcRefer to the disposal replacement cost (disposal cost) of energy storage device, p_batAnd p_ucIt is to store respectively The replacing batch of battery and super capacitor；C_omRefer to the operation expense (operation＆maintenance of energy storage device Cost), k_batAnd k_ucIt is the maintenance unit price of battery and super capacitor respectively；

Described constraints, including state-of-charge and charge and discharge power constraint in charge and discharge process, such as formula (3.2) institute Show：

In formula:SoC (state of charge) is the state-of-charge of energy storage device, wherein battery and super capacitor State-of-charge excursion is respectively [0.2,1] and [0.1,1]；P_CAnd P_DIt is the charging and discharging power of energy storage device respectively.

Preferably, the improvement particle cluster algorithm described in step 3, it is implemented including following sub-step：

Step 3.1：Initialize whole particle populations, setting iterations, material calculation, computational accuracy, object function；

Step 3.2：The fitness of each particle is calculated in an iterative process, obtains the individual extreme value and the overall situation of each particle Extreme value；

Step 3.3：Flying speed and the present position of each particle are updated by individual extreme value and global extremum；

Inertia weight is wherein introduced into speed more new formula：

In formula,It is flying speeds of the particle i in d dimension spaces after k+1 iteration；ω is inertia weight；c₁ And c₂It is Studying factors；r₁And r₂It is the random number of span (0,1)；p_idIt is the individual optimal value of particle；p_gdIt is particle Colony's optimal value；It is the particle fitness value after k iteration；

It is determined that during inertia weight value, first giving inertia weight coefficient assignment using adaptive weighting method, it is implemented Formula is；

In formula：ω_maxIt is the maximum occurrences of inertia weight；ω_minIt is the minimum value of inertia weight；F is problem to be solved Object function, f_avgIt is the average target value of all particle adaptive values in colony；f_minIt is all particle adaptive values in colony Minimum target value, μ_maxIt is the maximum of inertia weight；

Step 3.4：When reaching iterations or meeting stopping criterion for iteration, terminate iterative process and export optimal solution.

The present invention combines national wind-electricity integration standard and uses improved moving average method low pass wind power wave component, meter And the state-of-charge of energy storage device distributes fluctuating power using variable time constant filter method, can avoid overcharging for energy storage device Put, finally capacity configuration model is set up so that energy storage device Life cycle economy is optimal for object function, compared to traditional Only consider the method for disposable initial outlay, more meet the actual conditions of energy-storage system longtime running, have to Practical Project construction There is directive significance.

Brief description of the drawings

Fig. 1：It is the hybrid energy-storing topology diagram of the embodiment of the present invention；

Fig. 2：It is the flow chart of the present embodiment；

Fig. 3：It is the wind-powered electricity generation real output of the embodiment of the present invention：

Fig. 4：It is the process chart that moving average method extracts wind-electricity integration component and wave component that improves of the present invention；

Fig. 5：The grid-connected component and wave component for being the wind-powered electricity generation real output of the embodiment of the present invention and being obtained after handling, Wherein (a) is wind power actual measured value and the lasting grid-connected component isolated, and (b) is the hybrid energy-storing component isolated；

Fig. 6：It is the fuzzy control flow chart of the present invention；

Fig. 7：Be the embodiment of the present invention mixed energy storage system in battery and super capacitor fluctuating power distribution condition；

Fig. 8：Be the embodiment of the present invention the variable time constant filter method of utilization after different type energy storage device charged shape State situation；

Fig. 9：It is the particle cluster algorithm flow chart of the embodiment of the present invention；

Figure 10：It is that the mixed energy storage system of the embodiment of the present invention stabilizes wind power output power ripple effect figure.

Embodiment

Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the accompanying drawings and embodiment is to this hair It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not For limiting the present invention.

It is the hybrid energy-storing topology diagram of the present embodiment see Fig. 1, mixed energy storage system connects through power inverter PCS It is typical centralized configuration in the exit low-pressure side of wind power plant.

See Fig. 2, the present embodiment is based on electric power checking system circuit, it is proposed that a kind of to stabilize what wind power output power was fluctuated Mixed energy storage system capacity collocation method, comprises the following steps：

Step 1：The real data that active power of wind power field is exported is handled, with improved moving average method and tied National wind-electricity integration standard is closed, extracting in wind power output power needs the wave component that energy-storage system is stabilized；

The wind power plant installed capacity that the present embodiment is chosen is 20M, and real output data are as shown in Figure 3；Fig. 3 is shown The active power output valve (perunit value) that certain installed capacity is surveyed for 2014 for 20MW wind power plant.

See Fig. 4, wherein the particular content for improving moving average method is：

The window value of certain numerical value is selected, all numerical value in sliding window are then subjected to arithmetic average calculating, and will Average value is as the numerical value at sliding window midpoint, and last constantly moving window repeats above procedure, until final complete to data Processing obtains result.Wind power output power data are handled with moving average method, wind power can be separated, obtained To smooth grid-connected component and the wave component for needing energy-storage system progress power to handle up.When with moving average method, it is assumed that Sliding window numerical value is N min, and assumes that N is described for even number to facilitate, thus obtain the grid-connected component of t wind-powered electricity generation with Min grades of undulate quantities, as shown in formula (1.1), (1.2), (1.3)：

P_ft=(P_t-(N/2-1)+P_t-(N/2-2)+...+P_t+...+P_t+N/2)/N (1.1)；

P_mt=P_t-P_ft(1.2)；

T=N/2, N/2+1 ..., M-N/2 (1.3)；

In formula：P_tIt is the wind power of actual measurement in t minutes；P_ftIt is grid-connected component；P_mtIt is min grades of wave components；

M is measurement point sum.

Moving average period N is an important parameter, but N selection has certain randomness.N numerical value is too small, then The wave component of wind power is excessively superimposed upon on grid-connected component, causes power swing to become big；N numerical value is too big, then wind power Grid-connected component it is excessively smooth, wave component increases, and causes required stored energy capacitance to greatly increase.Research shows that sliding window takes Suitable for possessing the common load of general characteristic during value 15min；In addition, for the load with greater impact characteristic, then selecting Longer time length (30min).According to the load condition of example of calculation, N=15min is chosen.

The fixed moving average Period Length of selection is conducive to quick calculate to obtain grid-connected component and wave component, but right A small number of fluctuating ranges very big point in wind power output power, it is possible to obtain grid-connected component be still unsatisfactory for requiring, therefore With reference to the requirement of national wind-electricity integration power swing, improved on the basis of moving average method, for being unsatisfactory for fluctuating change The part of rate is adjusted, and finally gives the compensation power P of mixed energy storage system_HESS。

P_HESS=P_mt+△P (1.4)；

In formula：Δ P is the power being adjusted according to the requirement of wind-powered electricity generation peak power fluctuating change rate.

Wherein country is for the wind-electricity integration standard of different scales wind power plant：

With improved moving average method and the national wind-electricity integration standard of combination, extracting in wind power output power needs energy storage The grid-connected component and wave component obtained after the wave component that system is stabilized, final process is as shown in Figure 5.Can be with by Fig. 5 Find out, the lasting grid-connected component isolated has preferable flatness, and wave component is hybrid energy-storing component then show compared with Big fluctuation.

Step 2：The state-of-charge of meter and each energy storage device, with the high-pass filtering method of variable time constant filter in difference The distribution of wave component is carried out between type energy storage device；

The particular content of variable time constant filter method is：

Different according to the frequency height that ultracapacitor and battery stabilize power, a kind of thinking is with high-frequency filtering Device decomposes the compensation power P of mixed energy storage system_HESSAs shown in formula (1.5)：

Wherein, P_UCAnd P_BESSIt is that ultracapacitor and battery stabilize power, T respectively_UCIt is the filtering of high-pass filter Time constant；

When adjusting time constant filter, it is modified using fuzzy control theory.Fuzzy control can be by the subjective control of people Strategy processed is converted into computer language, and its basic procedure is will to be processed into fuzzy quantity after input signal obfuscation, by fuzzy The output that inference system is quantified, so as to be controlled, wherein formulating the core that fuzzy rule is fuzzy control.Fuzzy control Compared with Traditional control, the decision-making of the mankind is simulated by rule base, readily appreciates and implements.

See Fig. 6, the core content of fuzzy control includes：

(1) obfuscation：Input variable in the range of domain is subjected to Fuzzy processing, fuzzy subset is obtained and is subordinate to letter Number；

(2) fuzzy rule is formulated：It is fuzzy control sentence by the subjective control decision expression of people, passes through a plurality of fuzzy control Domination set under sentence formation different condition, this is the core of fuzzy control；

(3) fuzzy reasoning：Fuzzy quantity is handled according to fuzzy rule；

(4) de-fuzzy；The precise control amount that fuzzy quantity is changed into domain.

Based on above-mentioned fuzzy control principle, when progress hybrid energy-storing stabilizes power swing, it is considered to the lotus of each energy storage device Electricity condition, when state-of-charge approaches bound, adjusts time constant filter, the charge-discharge electric power of amendment energy storage device refers in time Order, so as to ensure that the state-of-charge of energy storage device is within zone of reasonableness all the time.The target of fuzzy control is：Work as energy storage device When state-of-charge is close to the upper limit, if obtaining charge power instruction, regulation time constant filter suitably reduces charge power, it is to avoid Overcharge；When energy storage device state-of-charge is close to lower limit, if obtaining discharge power instruction, regulation time constant filter, which reduces, to be put Electrical power, it is to avoid cross and put.Energy storage device charging and discharging state is improved by fuzzy control, the purpose increased the service life is reached.This Text is with storage battery charge state SoC_BESSWith ultracapacitor state-of-charge SoC_UCAs input, with the amendment of time constant filter Coefficient k is output.

First to SoC_BESSAnd SoC_UCIt is normalized, obtaining its degree of membership is：

SoC in formula_midFor the median of energy storage device state-of-charge.

By SoC_min≤SoC≤SoC_maxUnderstand ξ_batAnd ξ_ucContinuous domain be respectively [- a, a] and [- b, b], a and b tool Body size is relevant with energy storage device technical characteristic, because the depth of discharge of ultracapacitor is more than battery, therefore b>A and 0<b<1. When energy storage device degree of membership is a or b, capacity saturation is represented, electricity has been completely filled with；When energy storage device degree of membership is-a or-b When, represent that capacity is exhausted, discharge completely.ξ_batAnd ξ_ucFuzzy set be { NB (negative big), ZO (zero), PB (honest) }.Fuzzy Control The output variable of system is the correction factor k of time constant filter, and its discrete domain is [- 1, -0.5,0,0.5,1], and fuzzy set is { NB, NS (negative small), ZO, PS (just small), PB }.

After the fuzzy set and membership function of input quantity and output quantity is established, fuzzy rule is now formulated：

Work as P_HESS>During the instruction that 0, i.e. mixed energy storage system are discharged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) filtering time is suitably tuned up during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal Constant T_UC, increase the discharge power of ultracapacitor, reduce the discharge power of battery；

(3) filtering time is suitably turned down during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal Constant T_UC, reduce the discharge power of ultracapacitor, increase the discharge power of battery；

Work as P_HESS<During the instruction that 0, i.e. mixed energy storage system are charged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) filtering time is suitably turned down during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal Constant T_UC, reduce the charge power of ultracapacitor, increase the charge power of battery；

(3) filtering time is suitably tuned up during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal Constant T_UC, increase the charge power of ultracapacitor, reduce the charge power of battery；

, be to the state-of-charges of two kinds of energy storage devices before state-of-charge judgement is carried out to battery and ultracapacitor It is classified, is divided into S_max、S_high、S_mid、S_lowAnd S_minFive classes.When state-of-charge is in intermediateness, it is not necessary to change Time constant filter；When state-of-charge is relatively low, limitation electric discharge increases charge power；When state-of-charge is higher, limitation is filled Electricity, increases discharge power.Because battery is different with the technical characteristic of ultracapacitor, the five class state-of-charges of the two are also Difference, is specifically categorized as：

To obtain output quantity time constant filter correction factor k (- 1≤k≤1), it is necessary to which de-fuzzy obtains accurate defeated Go out, de-fuzzy processing is carried out as shown in formula (1.8) using weighted mean method herein.

In formula：

f_1i(SoC_BESS) it is input quantity SoC_BESSBe subordinate to angle value i-th；

f_2j(SoC_UC) it is input quantity SoC_UCBe subordinate to angle value j-th.

Obtaining revised time constant filter is：

T_UC ^*=(1+k) T_UC(1.9)；

Battery and super capacitor stabilize power and state-of-charge as schemed in mixed energy storage system after power distribution 7th, shown in 8.As shown in fig. 7, after power distribution, in the course of work of mixed energy storage system, ultracapacitor passes through More frequently the fluctuating power of high fdrequency component has been stabilized in discharge and recharge, and battery is then stabilized the fluctuating power component compared with low frequency, kept away Frequently discharge and recharge is exempted from.As shown in figure 8, the high-pass filtering method of the variable time constant filter based on fuzzy control, it is to avoid The super-charge super-discharge of energy storage device so that its state-of-charge remains at the rational numerical value of comparison, is conducive to subsequent time Fluctuating power is stabilized.

Step 3：Set up the hybrid energy-storing selected topic capacity for the purpose of optimal by mixed energy storage system Life cycle economy Optimal Allocation Model, is constraint bar with the technical characteristic of energy storage device and wind-electricity integration requirement with modified particle swarm optiziation Part, carries out mixed energy storage system capacity and distributes rationally.

The object function and constraints of wherein capacity Optimal Allocation Model be specially：

Optimization aim is used as using the average annual expense minimum of mixed energy storage system.Longtime running and storage in view of micro-grid system Energy system needs to carry out necessary maintenance and replacing, obtains shown in object function such as formula (1.10)：

In formula：C is the average annual expense of mixed energy storage system；Y is energy-storage system run time；C_ivIt is purchasing into for energy storage device This (investment cost)；M and n are the equipment number of battery and super capacitor respectively；O_batAnd O_ucIt is battery respectively With the unit price of super capacitor；C_dcRefer to the disposal replacement cost (disposal cost) of energy storage device, p_batAnd p_ucIt is to store respectively The replacing batch of battery and super capacitor；C_omRefer to the operation expense (operation＆maintenance of energy storage device Cost), k_batAnd k_ucIt is the maintenance unit price of battery and super capacitor respectively.

The technical characteristic constraint of energy storage device, including state-of-charge and charge and discharge power constraint in charge and discharge process, such as formula (1.11) shown in：

In formula:SoC (state of charge) is the state-of-charge of energy storage device, wherein battery and super capacitor State-of-charge excursion is respectively [0.2,1] and [0.1,1]；P_CAnd P_DIt is the charging and discharging power of energy storage device respectively.

For the capacity configuration model that the mixed energy storage system Life cycle economy set up is optimal, with battery list Body number m and super capacitor monomer number n is as optimizing solution amount, and meter and wind-electricity integration standard and energy storage device technical characteristic are about Beam, is solved using particle cluster algorithm, and process chart is as shown in figure 9, it specifically includes following steps：

Step 3.1：Initialize whole particle populations, setting iterations, material calculation, computational accuracy, object function；

Step 3.2：The fitness of each particle is calculated in an iterative process, obtains the individual extreme value and the overall situation of each particle Extreme value；

Step 3.3：Flying speed and the present position of each particle are updated by individual extreme value and global extremum；

Inertia weight is wherein introduced into speed more new formula：

In formula,It is flying speeds of the particle i in d dimension spaces after k+1 iteration；ω is inertia weight；c₁ And c₂It is Studying factors；r₁And r₂It is the random number of span (0,1)；p_idIt is the individual optimal value of particle；p_gdIt is particle Colony's optimal value；It is the particle fitness value after k iteration；

It is determined that during inertia weight value, first giving inertia weight coefficient assignment using adaptive weighting method, it is implemented Formula is；

In formula：ω_maxIt is the maximum occurrences of inertia weight；ω_minIt is the minimum value of inertia weight；F is problem to be solved Object function, f_avgIt is the average target value of all particle adaptive values in colony；f_minIt is all particle adaptive values in colony Minimum target value, μ_maxIt is the maximum of inertia weight；

Step 3.4：When reaching iterations or meeting stopping criterion for iteration, terminate iterative process and export optimal solution.

Battery and super capacitor parameter needed for calculating process are：

Wherein, it is C for rated capacity_bat(Ah) battery, when port voltage is U_bat, electric current is I_batWhen, battery Shown in energy and the power such as formula (1.12) of energy-storage system：

It is C for capacitance_ucSuper capacitor, when terminal voltage be U_uc, electric current is I_ucWhen, super capacitor energy-storage system Shown in energy and power such as formula (1.13)：

In calculating process, the uniform units of energy are MWh, and the uniform units of power are MW.

Configuration result is：

The effect that mixed energy storage system stabilizes wind power output power fluctuation is as shown in Figure 10.As seen from Figure 10, configure After mixed energy storage system, the output-power fluctuation of wind power plant is stabilized well, and grid-connected power is smoother and meets simultaneously Network mark is accurate.

It should be appreciated that the part that this specification is not elaborated belongs to prior art.

It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, therefore it can not be considered to this The limitation of invention patent protection scope, one of ordinary skill in the art is not departing from power of the present invention under the enlightenment of the present invention Profit is required under protected ambit, can also be made replacement or be deformed, each fall within protection scope of the present invention, this hair It is bright scope is claimed to be determined by the appended claims.

Claims (7)

1. a kind of mixed energy storage system capacity collocation method for stabilizing wind power output power fluctuation, it is characterised in that including following Step：

Step 1：The real data that active power of wind power field is exported is handled, with improved moving average method and state is combined Family's wind-powered electricity generation Grid-connection standards, extracting in wind power output power needs the wave component that energy-storage system is stabilized；

Described national wind-electricity integration standard is see table 1；

The national wind-electricity integration standard of table 1

The described national wind-electricity integration standard of improved moving average method and combination, extracting in wind power output power needs energy storage system The wave component that system is stabilized, it, which implements process, includes following sub-step：

Step 1.1：Smooth grid-connected component is calculated with improved moving average method and needs energy-storage system progress power to handle up Wave component；

Assuming that sliding window numerical value is N min, and assume that N is described for even number to facilitate, thus obtain t wind-powered electricity generation and Net component and min grades of undulate quantities, as shown in formula (1.1), (1.2), (1.3)：

P_ft=(P_t-(N/2-1)+P_t-(N/2-2)+...+P_t+...+P_t+N/2)/N (1.1)；

P_mt=P_t-P_ft(1.2)；

T=N/2, N/2+1 ..., M-N/2 (1.3)；

In formula：P_tIt is the wind power of actual measurement in t minutes；P_ftIt is grid-connected component；P_mtIt is min grades of wave components；M is that measurement point is total Number；

Step 2：With reference to the requirement of national wind-electricity integration power swing, improved on the basis of moving average method, for discontented The part of sufficient fluctuating change rate is adjusted, and finally gives the compensation power P of mixed energy storage system_HESS；

P_HESS=P_mt+△P (1.4)；

In formula：Δ P is the power being adjusted according to the requirement of wind-powered electricity generation peak power fluctuating change rate；

Step 2：The state-of-charge of meter and each energy storage device, with the high-pass filtering method of variable time constant filter in different type The distribution of wave component is carried out between energy storage device；

Step 3：Set up the hybrid energy-storing selected topic capacity optimization for the purpose of optimal by mixed energy storage system Life cycle economy Allocation models, with modified particle swarm optiziation, with the technical characteristic of energy storage device and wind-electricity integration requirement for constraints, enters Row mixed energy storage system capacity is distributed rationally.

2. the mixed energy storage system capacity collocation method according to claim 1 for stabilizing wind power output power fluctuation, it is special Levy and be：N=15min.

3. the mixed energy storage system capacity collocation method according to claim 1 for stabilizing wind power output power fluctuation, it is special Levy and be：The high-pass filtering method of the variable time constant filter of utilization described in step 2 enters between different type energy storage device The distribution of row wave component, it, which implements process, includes following sub-step：

Step 2.1：With the compensation power P of high-frequency filter equalizer mixed energy storage system_HESSAs shown in formula (2.1)：

$\left\{\begin{array}{c}{P}_{UC}=P_{HESS}*\frac{T_{UC}s}{T_{UC}s+1}\\ P_{BESS}=P_{HESS}-P_{UC}\end{array}\right.---\left(2.1\right)$

Wherein, P_UCAnd P_BESSIt is that ultracapacitor and battery stabilize power, T respectively_UCIt is the filtering time of high-pass filter Constant；

Step 2.2：Time constant filter is adjusted, is modified using fuzzy control theory；The core of described fuzzy control theory Intracardiac appearance includes：

Obfuscation：Input variable in the range of domain is subjected to Fuzzy processing, fuzzy subset and membership function is obtained；

Formulate fuzzy rule：It is fuzzy control sentence by the subjective control decision expression of people, passes through a plurality of fuzzy control sentence shape Domination set under into different condition, this is the core of fuzzy control；

Fuzzy reasoning：Fuzzy quantity is handled according to fuzzy rule；

De-fuzzy；The precise control amount that fuzzy quantity is changed into domain；

Step 2.3：Based on above-mentioned fuzzy control theory, when progress hybrid energy-storing stabilizes power swing, it is considered to each energy storage device State-of-charge, when state-of-charge approaches bound, adjust time constant filter in time, correct the charge and discharge electric work of energy storage device Rate is instructed, so as to ensure that the state-of-charge of energy storage device is within zone of reasonableness all the time.

4. the mixed energy storage system capacity collocation method according to claim 3 for stabilizing wind power output power fluctuation, it is special Levy and be：The implementation process of fuzzy control described in step 2 is, when energy storage device state-of-charge is close to the upper limit, if obtaining Charge power is instructed, then adjusts time constant filter and suitably reduce charge power, it is to avoid overcharge；When energy storage device state-of-charge connects During nearly lower limit, if obtaining discharge power instruction, regulation time constant filter reduces discharge power, it is to avoid crosses and puts.

5. the mixed energy storage system capacity collocation method according to claim 3 for stabilizing wind power output power fluctuation, it is special Levy and be：The implementation process of fuzzy control described in step 2 is, with storage battery charge state SoC_BESSWith ultracapacitor lotus Electricity condition SoC_UCAs input, using the correction factor k of time constant filter as output；

First to SoC_BESSAnd SoC_UCIt is normalized, obtaining its degree of membership is：

${\mathrm{\&xi;}}_{bat}=\frac{{\mathrm{SoC}}_{BESS}-{\mathrm{SoC}}_{mid}}{{\mathrm{SoC}}_{mid}}---\left(2.2\right);$

${\mathrm{\&xi;}}_{uc}=\frac{{\mathrm{SoC}}_{UC}-{\mathrm{SoC}}_{\mathrm{mi}d}}{{\mathrm{SoC}}_{mid}}---\left(2.3\right);$

SoC in formula_midFor the median of energy storage device state-of-charge；

By SoC_min≤SoC≤SoC_maxUnderstand ξ_batAnd ξ_ucContinuous domain be respectively [- a, a] and [- b, b], a and b's is specific big It is small relevant with energy storage device technical characteristic, because the depth of discharge of ultracapacitor is more than battery, therefore b>A and 0<b<1；Work as storage When energy equipment degree of membership is a or b, capacity saturation is represented, electricity has been completely filled with；When energy storage device degree of membership is-a or-b, table Show that capacity is exhausted, discharge completely；ξ_batAnd ξ_ucFuzzy set be { NB, ZO, PB }；When the output variable of fuzzy control is filtering Between constant correction factor k, its discrete domain is [- 1, -0.5,0,0.5,1], and fuzzy set is { NB, NS, ZO, PS, PB }；Wherein NB represents negative big, and ZO represents that zero, PB represents honest, and NS represents negative small, and PS represents just small；

After the fuzzy set and membership function of input quantity and output quantity is established, fuzzy rule is now formulated：

Work as P_HESS>During the instruction that 0, i.e. mixed energy storage system are discharged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) time constant filter is suitably tuned up during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal T_UC, increase the discharge power of ultracapacitor, reduce the discharge power of battery；

(3) time constant filter is suitably turned down during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal T_UC, reduce the discharge power of ultracapacitor, increase the discharge power of battery；

Work as P_HESS<During the instruction that 0, i.e. mixed energy storage system are charged, the following experience of fuzzy rule Main Basiss is formulated：

(1) if the state-of-charge of battery and ultracapacitor is median, time constant filter keeps constant；

(2) time constant filter is suitably turned down during ultracapacitor state-of-charge bigger than normal if the state-of-charge of battery is less than normal T_UC, reduce the charge power of ultracapacitor, increase the charge power of battery；

(3) time constant filter is suitably tuned up during ultracapacitor state-of-charge less than normal if the state-of-charge of battery is bigger than normal T_UC, increase the charge power of ultracapacitor, reduce the charge power of battery；

Before state-of-charge judgement is carried out to battery and ultracapacitor, the state-of-charge of two kinds of energy storage devices is carried out Classification, is divided into S_max、S_high、S_mid、S_lowAnd S_minFive classes；When state-of-charge is in intermediateness, it is not necessary to change filtering Time constant；When state-of-charge is relatively low, limitation electric discharge increases charge power；When state-of-charge is higher, limitation charging increases Big discharge power；

Because battery is different with the technical characteristic of ultracapacitor, the five class state-of-charges of the two also difference, specific point Class is table 2 below；

Table 2：Five class state-of-charges of battery and ultracapacitor

To obtain output quantity time constant filter correction factor k (- 1≤k≤1), it is necessary to de-fuzzy is accurately exported, adopt De-fuzzy processing is carried out with weighted mean method as shown in formula (2.4)；

$k=\frac{\underset{i}{\&Sigma;}\underset{j}{\&Sigma;}{f}_{1i}\left({\mathrm{SoC}}_{BESS}\right)f_{2j}\left({\mathrm{SoC}}_{UC}\right)k_{ij}}{{\mathrm{\&Sigma;\&Sigma;f}}_{1i}\left({\mathrm{SoC}}_{BESS}\right)f_{2j}\left({\mathrm{SoC}}_{UC}\right)}---\left(2.4\right)$

In formula：

f_1i(SoC_BESS) it is input quantity SoC_BESSBe subordinate to angle value i-th；f_2j(SoC_UC) it is input quantity SoC_UCJ-th of degree of membership Value；k_ijFor with input quantity f_1i(SoC_BESS) and f_2j(SoC_UC) corresponding output quantity；

Obtaining revised time constant filter is：

T_UC ^*=(1+k) T_UC (2.5)。

6. the mixed energy storage system capacity collocation method according to claim 1 for stabilizing wind power output power fluctuation, it is special Levy and be：Hybrid energy-storing selected topic capacity Optimal Allocation Model described in step 3, its object function and constraints are specially：

Shown in described object function such as formula (3.1)：

$\left\{\begin{array}{c}\min C=\frac{C_{iv}+C_{om}+C_{dc}}{Y}\\ C_{iv}=m*O_{bat}+n*O_{uc}\\ C_{om}=Y(m*k_{bat}+n*k_{uc})\\ C_{dc}=p_{bat}(m*O_{bat})+p_{uc}(n*O_{uc})\end{array}\right.---\left(3.1\right);$

In formula：C is the average annual expense of mixed energy storage system；Y is energy-storage system run time；C_ivIt is the acquisition cost of energy storage device；m It is the equipment number of battery and super capacitor respectively with n；O_batAnd O_ucIt is the unit price of battery and super capacitor respectively；C_dcRefer to The disposal replacement cost of energy storage device, p_batAnd p_ucIt is the replacing batch of battery and super capacitor respectively；C_omRefer to energy storage device Operation expense, k_batAnd k_ucIt is the maintenance unit price of battery and super capacitor respectively；

Described constraints, including state-of-charge and charge and discharge power constraint in charge and discharge process, as shown in formula (3.2)：

$\left\{\begin{array}{c}{\mathrm{SoC}}_{\min }\&le;SoC\&le;{\mathrm{SoC}}_{\max }\\ 0\&le;P_C\&le;P_{C.\max }\\ 0\&le;P_D\&le;P_{D.\max }\end{array}\right.---\left(3.2\right);$

In formula:SoC is the state-of-charge of energy storage device, and the state-of-charge excursion of wherein battery and super capacitor is respectively [0.2,1] and [0.1,1]；P_CAnd P_DIt is the charging and discharging power of energy storage device respectively.

7. the mixed energy storage system capacity collocation method according to claim 6 for stabilizing wind power output power fluctuation, it is special Levy and be：Modified particle swarm optiziation described in step 3, it is implemented including following sub-step：

Step 3.1：Initialize whole particle populations, setting iterations, material calculation, computational accuracy, object function；

Step 3.2：The fitness of each particle is calculated in an iterative process, obtains the individual extreme value of each particle and global pole Value；

Step 3.3：Flying speed and the present position of each particle are updated by individual extreme value and global extremum；

Inertia weight is wherein introduced into speed more new formula：

$v_{id}^{k+1}={\mathrm{wv}}_{id}^k+c_1{r}_1\left(p_{id}-z_{id}^k\right)+c_2{r}_2\left(p_{gd}-z_{id}^k\right)---\left(4.1\right);$

In formula,It is flying speeds of the particle i in d dimension spaces after k+1 iteration；ω is inertia weight；c₁And c₂ It is Studying factors；r₁And r₂It is the random number of span (0,1)；p_idIt is the individual optimal value of particle；p_gdIt is the group of particle Body optimal value；It is the particle fitness value after k iteration；

It is determined that during inertia weight value, first giving inertia weight coefficient assignment using adaptive weighting method, it implements formula For：

$\mathrm{\&omega;}=\left\{\begin{array}{cc}{\mathrm{\&omega;}}_{\min }+\frac{\left({\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{\min }\right)*\left(f-f_{\min }\right)}{f_{avg}-f_{\min }},& f\&le;f_{avg}\\ {\mathrm{\&mu;}}_{\max },& f>f_{avg}\end{array}\right.---\left(4.2\right);$

In formula：ω_maxIt is the maximum occurrences of inertia weight；ω_minIt is the minimum value of inertia weight；F is the mesh of problem to be solved Scalar functions, f_avgIt is the average target value of all particle adaptive values in colony；f_minIt is the minimum of all particle adaptive values in colony Desired value, μ_maxIt is the maximum of inertia weight；

Step 3.4：When reaching iterations or meeting stopping criterion for iteration, terminate iterative process and export optimal solution.