CN104092230B - Method for achieving optimal configuration of capacity of energy storage equipment in islanding mode of regional power grid comprising DG - Google Patents

Abstract

The invention discloses a method for achieving optimal configuration of the capacity of energy storage equipment in the islanding mode of a regional power grid comprising a DG. The method comprises the steps of monitoring output power data and load data of the DG at each time point, and fitting the probability distribution function of the output power data and the probability distribution function of the load data respectively; establishing a control strategy for charging and discharging of the energy storage equipment, and obtaining a control rule for the output power of the energy storage equipment; establishing the dynamic semi-invariant of the DG and the load, and obtaining the probability distribution of the charging and discharging power of the energy storage equipment; establishing a configuration optimization model with the minimization of the capacity of the energy storage equipment as the target and the charged state of the energy storage equipment as the inequality constraint condition, and working out the configuration capacity of the energy storage equipment. According to the method, the dynamic semi-invariant is introduced to constrain the probability distribution of the output power of the energy storage equipment within a certain confidence coefficient, the control strategy for the charging and discharging power of the energy storage equipment is introduced to guarantee that the energy storage equipment does not operate in the depleted state or the saturated state for a long time and the capacity of the energy storage equipment is fully utilized, and therefore the power quality and the economy can be guaranteed in the islanding mode of the regional power grid.

Description

Containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode

Technical field

The present invention relates to energy storage device capacity field, more particularly, to one containing DG (distributed power source) regional power grid The Optimal Configuration Method of energy storage device capacity under island mode, the method is particularly well-suited to failure condition and solves containing distributed power source Stable operation and economy problems under regional power grid island mode, it is to avoid energy storage device capacity is exhausted or saturated.

Background technology

In the case of permanent fault, it is in isolated island containing distributed power source (Distributed Generation, DG) distribution During the method for operation, by distributed power source is regulated and controled, and under meeting the premise such as power supply quality and workload demand, it is achieved be System islet operation.Under islet operation pattern, energy storage device, as main control unit, is exerted oneself according to photovoltaic, blower fan is exerted oneself and bears Lotus situation carries out comprehensive regulation, relies on energy storage device to maintain voltage and the frequency of micro-grid system.But, due to energy storage device Cost is of a relatively high, and how economical and efficient ground carries out choosing of stored energy capacitance is still the difficult point that current engineering is actual.

Owing to wind energy turbine set, exerting oneself of photovoltaic plant have stochastic volatility, in Operation of Electric Systems, load is the most all becoming Change, significantly power swing occurs the most suddenly, it is desirable to make the output of wind-powered electricity generation, photovoltaic completely may be used by energy storage device Control, the most uneconomical the most unrealistic, that is take 100% to meet constraints to configure hybrid accumulator, then its cost is by pole Big increase.If now taking a certain confidence level to meet constraints, by more actual application value.

The essence of chance constrained programming is to consider uncertain factor to a certain extent, by fully meet in tradition optimization Constraints softens the probability being to meet constraints higher than a certain confidence level.If the method that probability can be used, it is possible to By the DG given exert oneself and the probability distribution of load obtains the probability distribution situation of energy storage device output, then using they as Probability constraints, makes object function reach optimum, obtains the system optimal method of operation.Existing is to use same probability density characteristics Describe distributed power source to exert oneself and load variations, and actual DG exert oneself and the wave characteristic of load can the most dynamically change, The probability nature in each moment may be different.

Summary of the invention

Present invention aim at providing one to hold containing the energy storage device under DG (distributed power source) regional power grid island mode The collocation method of amount, is used for solving regional power grid island mode stable operation and economy problems in the case of permanent fault, this The collocation method of invention is reliability and the economy guaranteeing isolated power grid with certain confidence level, takes into account the energy storage device longevity simultaneously Life.

It is a kind of containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode, specifically that the present invention provides Comprise the following steps:

1) P that exerts oneself of each moment wind energy turbine set is monitored_w, the P that exerts oneself of photovoltaic plant_PVAnd load is P_load, and simulate each From probability density function；

2) control strategy setting up energy storage device discharge and recharge obtains the control rule of energy storage device output；

3) set up DG and the dynamic cumulant of load, obtain energy storage device charge-discharge electric power confidence level；

4) foundation is minimised as object function with energy storage device capacity, and energy storage device state-of-charge is as inequality constraints Optimal Allocation Model, and solve the capacity of energy storage device.

The fluctuation of wind energy, solar energy and electric load has certain randomness, but contains in its random fluctuation and have necessarily Regularity, therefore output of wind electric field P_w, photovoltaic plant exerts oneself P_PVAnd load P_loadCan be by various definitiveness regular basis The corresponding stochastic volatility of upper superposition sets up the probabilistic model of dynamic random variable.

The most concrete process is:

1) P that exerts oneself of each moment wind energy turbine set is monitored_w, the P that exerts oneself of photovoltaic plant_PVAnd load is P_load, and simulate it Probability density function be respectively as follows:

Speed-variable frequency-constant wind-driven generator can be obtained by formula (1) to exert oneself P_wProbability density function:

P_w(t)=P_b(t)+Δ_b (1)

In formula: P_bT function based on (), is the wind speed in each moment the exerting oneself of wind-driven generator in the case of expected value, Δ_bThe random fluctuation composition comprised in exerting oneself for speed-variable frequency-constant wind-driven generator；

Photovoltaic plant can be obtained by formula (2) to exert oneself P_PVProbability density function:

P_PV(t)=P_sun(t)-Δ_sun (2)

In formula: P_sunT function based on (), is the solar energy in each moment the exerting oneself of photovoltaic plant in the case of expected value, Stochastic variable Δ_sunRepresent the atmosphere inhibition to solar irradiation；

Load Probability density fonction can be obtained by formula (3):

P_load(t)=P_l(t)+Δ_l (3)

In formula: P_lT () is the basic function of daily load curve；Δ_lRandom fluctuation composition for load.

2) set up the control strategy of energy storage device discharge and recharge, the energy state of energy storage device is divided into 3 intervals, including Between nonclient area, normal operation interval and vigilance task interval i.e. energy storage device be easily accessible exhausted or saturated, it controls plan Slightly it is respectively as follows:

(21) when the state-of-charge of energy storage device is in working area, the difference of DG and load determines the discharge and recharge of energy storage device Power；

(22) when the state-of-charge exhaustion state of energy storage device, energy storage device is guided to reduce discharge power；When energy storage sets Standby state-of-charge is in saturation, takes to abandon wind measure, prevents energy storage device from overcharging.

3) set up the dynamic cumulant of DG and load, obtain the probability distribution of energy storage device charge-discharge electric power；Set up The moment of the orign of DG and load is obtained by formula (4) with the relation of cumulant, then by the reference function of distributed power source Yu load Obtain by formula (5) with the probability distribution that Gram-charlier progression obtains energy storage device charge-discharge electric power:

Moment of the random variable and the relation such as formula (4) of cumulant:

$\begin{array}{c}{K}_1={\mathrm{\α}}_1\\ K_2={\mathrm{\α}}_2-{\mathrm{\α}}_1^2\\ K_3={\mathrm{\α}}_3-3{\mathrm{\α}}_1{\mathrm{\α}}_2+2{\mathrm{\α}}_1^2\\ k_4={\mathrm{\α}}_4-3{\mathrm{\α}}_2^2-4{\mathrm{\α}}_1{\mathrm{\α}}_3+12{\mathrm{\α}}_1^2{\mathrm{\α}}_3-6{\mathrm{\α}}_1^4\end{array}---\left(4\right)$

In formula, K_vFor the v rank cumulant of stochastic variable, v=1,2,3,4, α_iFor the i rank moment of the orign of stochastic variable, i= 1,2,3,4；

By Gram-Charlier progression, the distribution function of stochastic variable is expressed as by normal random variable all-order derivative group The progression become, wherein the coefficient of progression is then made up of each rank cumulant of this stochastic variable, thus obtains energy storage device and fills The probability density function of discharge power can be obtained by formula (5):

$\begin{array}{c}f\left(x\right)={\∫}_x^{\mathrm{\∞}}N\left(x\right)dx+\frac{g_3}{3!}{N}^2\left(x\right)-\frac{g_4}{4!}{N}^{\left(3\right)}\left(x\right)-\frac{g_5}{5!}{N}^{\left(4\right)}\left(x\right)-\frac{g_6+10g_3^2}{6!}{N}^{\left(5\right)}\left(x\right)+\\ \frac{g_7+35g_3{g}_4}{7!}{N}^{\left(6\right)}\left(x\right)-\frac{g_8+56g_3{g}_5+35g_4^2}{8!}{N}^{\left(7\right)}\left(x\right)+\mathrm{...}\end{array}---\left(5\right)$

In formula, g_m=K_m/σ^m, m=3,4 ..., 8 ..., K_mFor the m rank cumulant of stochastic variable, σ^mFor stochastic variable mark The m power of quasi-difference；N^(γ)(x) (γ=1,2 ..., 4 ...) it is the γ order derivative of Standard Normal Distribution, x is random change Amount.

4) foundation is minimised as object function with energy storage device capacity, and energy storage device state-of-charge is the excellent of inequality constraints Change allocation models, specifically by following acquisition:

It is minimised as object function, such as formula (6) with equipment stored energy capacitance:

F=minS (7)

In formula, S is respectively energy storage device rated capacity；

Using the state-of-charge of energy storage device as inequality constraints, such as formula (8):

$\underset{\&OverBar;}{Soc}<Soct<\stackrel{\&OverBar;}{Soc}$

$Soct=\frac{E^{ini}+\underset{r=1}{\overset{t-1}{\&Sigma;}}({\mathrm{\&eta;}}_{ch}{P}_{chr}-\frac{1}{{\mathrm{\&eta;}}_{dis}}{P}_{disr})\mathrm{\&Delta;}t}{\stackrel{\&OverBar;}{E}}---\left(8\right)$

In formula:SocWithBound for energy storage device state-of-charge；Soct is the charged shape of energy storage device t State；η_ch, η_disIt is respectively energy storage device efficiency for charge-discharge；P_chr, P_disrIt is respectively charge-discharge electric power；For the specified appearance of energy storage device Amount Δ t is adjacent two the moment time differences of energy storage device discharge and recharge, EⁱⁿⁱFor the initial capacity of energy storage device, r is that energy storage device fills In first moment of electric discharge, t is the t moment of energy storage device discharge and recharge..

Compared with prior art, technical solution of the present invention provides the benefit that: the present invention provide containing distributed electrical source region The Optimal Configuration Method of energy storage device capacity under the electrical network island mode of territory, is by introducing dynamic cumulant, by energy storage device The probability distribution of output constrains under certain confidence level, and it is real to introduce energy storage device charge-discharge electric power control strategy Time make energy storage device neither long-play in exhausted or saturation, the capacity of energy storage device can be made full use of again, it is ensured that The quality of power supply under regional power grid island mode and economy.

Accompanying drawing explanation

Fig. 1 is the energy storage device collocation method flow chart of the present invention.

Fig. 2 is the control strategy figure of energy storage device.

Fig. 3 is the probability density of energy storage device charge-discharge electric power.

Fig. 4 is the cumulative probability of energy storage device charge-discharge electric power.

Fig. 5 is the projection in XZ plane of the energy storage device charge-discharge electric power dynamic probability density.

Fig. 6 is the projection in XZ plane of the energy storage device charge-discharge electric power dynamic accumulative probability.

Fig. 7 is energy storage device charge-discharge electric power and state-of-charge.

Detailed description of the invention

Accompanying drawing being merely cited for property explanation, it is impossible to be interpreted as limitation of the present invention；

In order to the present embodiment is more preferably described, some parts of accompanying drawing have omission, zoom in or out, and do not represent actual product Size；

To those skilled in the art, in accompanying drawing, some known features and explanation thereof may be omitted is to be appreciated that 's.

With embodiment, technical scheme is described further below in conjunction with the accompanying drawings.

Embodiment 1

IEEE33 node energy storage device collocation method containing DG carries out instance analysis below, and wherein, rated capacity is respectively For 2MW, 4 wind energy turbine set of 2MW, 1MW, 1MW are respectively at node 13, and 15,29 rated capacities are that 3 photovoltaic plants of 1.2MW divide Not in node 17,21,32 access, and rated capacity is that the small power station of 2MW accesses in node 1.Energy storage device charge efficiency η_chFor 0.95, discharging efficiency η_disIt is 0.9；The state-of-charge upper limitIt is 0.7, state-of-charge lower limitSOCIt is 0.3；Energy storage device Initial capacity EⁱⁿⁱGeneral for rated capacity S；

In conjunction with the configuration flow figure of Fig. 1, the configuration side of energy storage device under the regional power grid Isolate model containing distributed power source Method comprises the following steps:

1) P that exerts oneself of each moment wind energy turbine set is monitored_w, the P that exerts oneself of photovoltaic plant_PVAnd load is P_load, its probability density Function is respectively as follows:

Output of wind electric field P_wProbability density function:

$f\left(v\right)=\frac{k}{c}{\left(\frac{v}{c}\right)}^{k-1}\exp \&lsqb;-{\left(\frac{v}{c}\right)}^k\&rsqb;---\left(8\right)$

In formula: v is wind speed；K > 0 is form parameter；C > 0 is scale parameter；

In engineering, wind speed v and the P that exerts oneself of wind energy turbine set_wBetween relation such as formula

$P_w=\left\{\begin{array}{cc}0& v\&le;v_i\\ k_{1}v+k_2& v_i\&le;v\&le;v_r\\ P_r& v_r\&le;v\&le;v_0\\ 0& v_0\&le;v\end{array}\right.---\left(9\right)$

Can be shown that wind energy turbine set is gained merit the probability density function exerted oneself by formula (8) and formula (9):

$f\left(p_w\right)=F^{\&prime;}\left(p_w\right)=\exp \&lsqb;-{\left(\frac{p_w-k_2}{k_1\mathrm{\&beta;}}\right)}^{\mathrm{\&alpha;}}\&rsqb;\frac{\mathrm{\&alpha;}}{k_1\mathrm{\&beta;}}{\left(\frac{p_w-k_2}{k_1\mathrm{\&beta;}}\right)}^{\mathrm{\&alpha;}-1}---\left(10\right)$

The P that exerts oneself of photovoltaic plant_PVProbability density function is obeyed Beta and is distributed such as formula (11)

$f\left(P_{PV}\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{P_{PV}}{R_m}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{P_{PV}}{Rm})}^{\mathrm{\&beta;}-1}---\left(11\right)$

In formula, R_mFor square formation output, α and β is Beta profile shape parameter；P_PVPower for photovoltaic output；

Load fluctuation has uncertainty, describes such as formula (12) with normal distribution:

P_i∈N(μ_1i,σ_1i) (12)

In formula, P_iFor the random burden with power of node i, μ_1iFor the random burden with power average of node i, σ_1iFor node i Random burden with power standard variance；

2) set up the control strategy of energy storage device discharge and recharge, the energy state of energy storage device is divided into 3 intervals, including Between nonclient area, normal operation interval and vigilance task interval i.e. energy storage device be easily accessible exhausted or saturated, as accompanying drawing 3 its Control strategy is respectively as follows:

(21) it is in when the state-of-charge of energy storage deviceSCO2WithBetween when being normal working area, DG and load Difference determines the charge-discharge electric power of energy storage device；

(22) it is in when the state-of-charge of energy storage deviceSCO1WithBetween be exhausted state, energy storage device reduces to be put Electrical power；

(23) it is in when the state-of-charge of energy storage deviceWithBetween be saturation, take to abandon wind measure, Prevent energy storage device from overcharging.

3) set up the dynamic cumulant of DG and load, and obtain energy storage device charge and discharge according to Gram-Charlier progression The probability distribution of electrical power；

Each rank moment of the orign such as formula (13) of output of wind electric field

${\mathrm{\&alpha;}}_{wind}^v=\underset{-\mathrm{\&infin;}}{\overset{+\mathrm{\&infin;}}{\&Integral;}}{P}_w^{v}f\left(P_w\right){\mathrm{dP}}_w---\left(13\right)$

Each rank moment of the orign such as formula (14) that photovoltaic is exerted oneself

${\mathrm{\&alpha;}}_{PV}^v=\underset{-\mathrm{\&infin;}}{\overset{+\mathrm{\&infin;}}{\&Integral;}}{P}_{PV}^{v}f\left(P_{PV}\right){\mathrm{dP}}_{PV}---\left(14\right)$

The each rank cumulant such as formula being asked wind energy turbine set and photovoltaic to exert oneself by each rank moment of the orign exerted oneself of wind energy turbine set and photovoltaic (4):

$\begin{array}{c}{K}_1={\mathrm{\&alpha;}}_1\\ K_2={\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1^2\\ K_3={\mathrm{\&alpha;}}_3-3{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2+2{\mathrm{\&alpha;}}_1^2\\ k_4={\mathrm{\&alpha;}}_4-3{\mathrm{\&alpha;}}_2^2-4{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_3+12{\mathrm{\&alpha;}}_1^2{\mathrm{\&alpha;}}_3-6{\mathrm{\&alpha;}}_1^4\end{array}---\left(4\right)$

In formula, K_vThe v rank cumulant exerted oneself for wind energy turbine set and photovoltaic, v=1,2,3,4, α_iI rank for stochastic variable are former Point square, i=1,2,3,4；

For the injecting power of normal distribution, its single order cumulant is equal to expected value, and second order cumulant is that normal state is divided

The variance of cloth, three rank are 0 to seven rank cumulant, such as formula (15)

γ₁=α₁

γ₂=σ² (15)

γ₃=γ₄=γ₅=γ₆=γ₇=0

In formula, γ_vV rank cumulant for load；

By Gram-Charlier progression, the distribution function of stochastic variable is expressed as by normal random variable all-order derivative group The progression become, wherein the coefficient of progression is then made up of each rank cumulant of this stochastic variable, thus obtains energy storage device and fills The probability density function of discharge power can be obtained by formula (5):

$\begin{array}{c}f\left(x\right)={\&Integral;}_x^{\mathrm{\&infin;}}N\left(x\right)dx+\frac{g_3}{3!}{N}^2\left(x\right)-\frac{g_4}{4!}{N}^{\left(3\right)}\left(x\right)-\frac{g_5}{5!}{N}^{\left(4\right)}\left(x\right)-\frac{g_6+10g_3^2}{6!}{N}^{\left(5\right)}\left(x\right)+\\ \frac{g_7+35g_3{g}_4}{7!}{N}^{\left(6\right)}\left(x\right)-\frac{g_8+56g_3{g}_5+35g_4^2}{8!}{N}^{\left(7\right)}\left(x\right)+\mathrm{...}\end{array}---\left(5\right)$

In formula, g_m=K_m/σ^m, m=3,4 ..., 8, K_mFor the m rank cumulant of stochastic variable, σ^mFor stochastic variable standard deviation M power；N^(γ)(x) (γ=1,2 ..., 4) it is the γ order derivative of Standard Normal Distribution, x is energy storage device charge and discharge electric work Rate stochastic variable.

The dynamic probability the density profile such as accompanying drawing 4 and dynamic accumulative probability that thus obtain energy storage device charge-discharge electric power divide Butut such as accompanying drawing 5, by charge-discharge electric power under available energy storage device 10% and 90% confidence level of figure, by under the two confidence level Energy storage charge-discharge electric power is sued for peace and is taken the expected value such as table 1 being all worth to each moment charge-discharge electric power of energy storage device:

The expected value of table 1 each moment charge-discharge electric power of energy storage device

4) foundation is minimised as object function with energy storage device capacity, and energy storage device state-of-charge is the excellent of inequality constraints Change allocation models, specifically by following acquisition:

It is minimised as object function, such as formula (6) with equipment stored energy capacitance:

F=minS (6)

In formula, S is respectively energy storage device rated capacity；

Using the state-of-charge of energy storage device as inequality constraints, such as formula (16):

$\underset{\&OverBar;}{Soc}<Soct<\stackrel{\&OverBar;}{Soc}$

$Soct=\frac{E^{ini}+\underset{r=1}{\overset{t-1}{\&Sigma;}}({\mathrm{\&eta;}}_{ch}{P}_{chr}-\frac{1}{{\mathrm{\&eta;}}_{dis}}{P}_{disr})\mathrm{\&Delta;}t}{S}---\left(16\right)$

In formula:SocWithBound for energy storage device state-of-charge；η_ch, η_disIt is respectively energy storage device charge and discharge

Electrical efficiency；P_chr, P_disrIt is respectively charge-discharge electric power；

It is 0.937MWh by formula (6) and this capacity S obtaining energy storage device of formula (16), and obtains energy storage device

The charge-discharge electric power in each moment with state-of-charge such as accompanying drawing 7.

Obviously, the above embodiment of the present invention is only for clearly demonstrating example of the present invention, and is not right The restriction of embodiments of the present invention.For those of ordinary skill in the field, the most also may be used To make other changes in different forms.Here without also cannot all of embodiment be given exhaustive.All at this Any amendment, equivalent and the improvement etc. made within the spirit of invention and principle, should be included in the claims in the present invention Protection domain within.

Claims (4)

1. one kind containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode, it is characterised in that include with Lower step:

1) P that exerts oneself of each moment wind energy turbine set is monitored_w, the P that exerts oneself of photovoltaic plant_PVAnd load is P_load, and it is each to simulate it respectively From probability density function；

2) control strategy setting up energy storage device discharge and recharge obtains the control rule of energy storage device output；

3) set up DG and the dynamic cumulant of load, obtain energy storage device charge-discharge electric power confidence level；

4) foundation is minimised as object function with energy storage device capacity, and energy storage device state-of-charge is as the optimization of inequality constraints Allocation models, and solve the capacity of energy storage device；

Described DG exerts oneself and the moment of the orign of load is obtained by formula (4) with the relation of cumulant, then by the benchmark of DG Yu load Function and Gram-charlier progression obtain the probability distribution such as formula (5) of energy storage device charge-discharge electric power,

Moment of the random variable and the relation such as formula (4) of cumulant:

$\begin{array}{c}{K}_1={\mathrm{\&alpha;}}_1\\ K_2={\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1^2\\ K_3={\mathrm{\&alpha;}}_3-3{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2+2{\mathrm{\&alpha;}}_1^2\\ K_4={\mathrm{\&alpha;}}_4-3{\mathrm{\&alpha;}}_2^2-4{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_3+12{\mathrm{\&alpha;}}_1^2{\mathrm{\&alpha;}}_3-6{\mathrm{\&alpha;}}_1^4\end{array}---\left(4\right)$

In formula, K_vFor the v rank cumulant of stochastic variable, v=1,2,3,4, α_iFor the i rank moment of the orign of stochastic variable, i=1,2, 3,4；

By Gram-Charlier progression, the distribution function of stochastic variable is expressed as being made up of normal random variable all-order derivative Progression, wherein the coefficient of progression is then made up of each rank cumulant of this stochastic variable, and thus energy storage charge-discharge electric power is general Rate density fonction can be obtained by formula (5):

$\begin{array}{c}f\left(x\right)={\&Integral;}_x^{\mathrm{\&infin;}}N\left(x\right)dx+\frac{g_3}{3!}{N}^{\left(2\right)}\left(x\right)-\frac{g_4}{4!}{N}^{\left(3\right)}\left(x\right)-\frac{g_5}{5!}{N}^{\left(4\right)}\left(x\right)-\frac{g_6+10g_3^2}{6!}{N}^{\left(5\right)}\left(x\right)+\\ \frac{g_7+35g_3{g}_4}{7!}{N}^{\left(6\right)}\left(x\right)-\frac{g_8+56g_3{g}_5+35g_4^2}{8!}{N}^{\left(7\right)}\left(x\right)+...\end{array}---\left(5\right)$

In formula, g_m=K_m/σ^m, m=3,4 ..., 8 ..., K_mFor the m rank cumulant of stochastic variable, σ^mFor stochastic variable standard deviation M power；N^(γ)(x) (γ=1,2 ..., 4 ...) it is the γ order derivative of Standard Normal Distribution, x is stochastic variable.

The most according to claim 1 containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode, its Be characterised by, described step 1) the P that exerts oneself of wind energy turbine set_w, the P that exerts oneself of photovoltaic plant_PVAnd load P_loadCan be by following acquisition:

Speed-variable frequency-constant wind-driven generator can be obtained by formula (1) to exert oneself P_wProbability density function:

P_w(t)=P_b(t)+Δ_b (1)

In formula: P_bT function based on (), is the wind speed in each moment the exerting oneself of wind-driven generator in the case of expected value, Δ_bFor The random fluctuation composition that speed-variable frequency-constant wind-driven generator comprises in exerting oneself；

Photovoltaic plant can be obtained by formula (2) to exert oneself P_PVProbability density function:

P_PV(t)=P_sun(t)-Δ_sun (2)

In formula: P_sunT function based on (), is the solar energy in each moment the exerting oneself, at random of photovoltaic plant in the case of expected value Variable Δ_sunRepresent the atmosphere inhibition to solar irradiation；

Load Probability density fonction can be obtained by formula (3):

P_load(t)=P_l(t)+Δ_l (3)

In formula: P_lT () is the basic function of daily load curve；Δ_lRandom fluctuation composition for load.

The most according to claim 1 containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode, its Be characterised by, the energy state of energy storage device be divided into 3 intervals, including between nonclient area, normal operation interval and police Guarding against operation interval i.e. energy storage device and be easily accessible exhausted or saturated, its control strategy is respectively as follows:

(21) when the state-of-charge of energy storage device is in working area, the difference of DG and load determines the charge and discharge electric work of energy storage device Rate；

(22) when the state-of-charge exhaustion state of energy storage device, energy storage device discharge power is reduced；

(23) it is in saturation when the state-of-charge of energy storage device, takes to abandon wind measure, prevent energy storage device from overcharging.

The most according to claim 1 containing the Optimal Configuration Method of energy storage device capacity under DG regional power grid island mode, its Being characterised by, set up and be minimised as object function with energy storage device capacity, energy storage device state-of-charge is as inequality constraints, tool Body is by following acquisition:

It is minimised as object function, such as formula (6) with equipment stored energy capacitance:

F=minS (6)

In formula, S is energy storage device rated capacity；

Using the confidence level of energy storage device charge-discharge electric power as inequality constraints, such as formula (7):

$\begin{array}{c}\underset{\&OverBar;}{Soc}<Soct<\stackrel{\&OverBar;}{Soc}\\ Soct=\frac{E^{\mathrm{in}i}+\underset{r=1}{\overset{t-1}{\mathrm{\&Sigma;}}}({\mathrm{\&eta;}}_{ch}{P}_{chr}-\frac{1}{{\mathrm{\&eta;}}_{dis}}{P}_{disr})\mathrm{\&Delta;}t}{\stackrel{\&OverBar;}{E}}\end{array}---\left(7\right)$

In formula:SocWithFor the bound of energy storage device state-of-charge, Soct is the state-of-charge of energy storage device t；η_ch, η_disIt is respectively energy storage device efficiency for charge-discharge；P_chr, P_disrIt is respectively charge-discharge electric power；For energy storage device rated capacity, Δ t For adjacent two the moment time differences of energy storage device discharge and recharge, EⁱⁿⁱFor the initial capacity of energy storage device, r is energy storage device discharge and recharge First moment, t is the t moment of energy storage device discharge and recharge.