CN103986194A - Independent micro-network optimized configuration method and device - Google Patents

Abstract

The invention discloses an independent micro-network optimized configuration method and device. According to the method, aiming at uncertainty of air speed, illumination intensity and load, typical days of different months of one year are constructed by using probability models of the air speed, the illumination intensity and the load, subsequently by utilizing the multi-state system theory, the air speed, the illumination intensity and the load of the typical days are divided in a multi-state manner, then continuous uncertain state basis and probability distribution regulation of the continuous uncertain state basis are converted into a plurality of discrete certainty states to be processed, and thus the characteristics of force output of a fan, the force output of a photovoltaic cell and the load randomness can be directly simulated, the solution difficulty of an optimized configuration model is alleviated, the solved independent micro-network can be more reasonably configured, and the robustness of the wind-light output force and the load fluctuation is high.

Description

A kind of self microgrid Optimal Configuration Method and device

Technical field

The present invention relates to the application of electric power system self microgrid, relate in particular a kind of self microgrid Optimal Configuration Method and device.

Background technology

At present; improve efficiency of energy utilization, improve energy resource structure and become the inevitable choice that solves contradiction between the energy demand growth that day by day highlights in China's economy and social fast development process and energy scarcity, using energy source and environmental protection; for this reason; distributed power generation energy supplying system is accessed to large electrical network with the form of microgrid to be incorporated into the power networks; support each other with large electrical network; make full use of the abundant clean and regenerative resource in various places, to user, provide " green electric power supply " to become the developing direction of China's energy-saving and emission-reduction.

Wherein, microgrid is the system unit consisting of various distributed power sources, load, energy-storage system and control device etc., and it is one can realize the autonomous system that oneself controls, protects and manage, and conventionally the microgrid not being connected with large electrical network is called to self microgrid.

In actual applications, in order to make the operating state of this self microgrid best, conventionally all can be optimized configuration to it, through research, find, existing self microgrid Optimal Configuration Method is all often the related data of wind speed, intensity of illumination and load based on this self microgrid location historical year (8760 hours), and employing intelligent algorithm solves its Optimal Allocation Model as genetic algorithm, particle cluster algorithm etc.Because regenerative resource is as the uncertainty of wind energy and solar energy etc., while having caused self microgrid actual motion, each wind speed, intensity of illumination and load data constantly compares with the data of being obtained historical year that there is some difference, and the system configuration that this difference may cause the optimization of existing self microgrid Optimal Configuration Method to draw is not optimum under actual motion condition.

Summary of the invention

In view of this, the invention provides a kind of self microgrid Optimal Configuration Method and device, solved when existing self microgrid Optimal Configuration Method is optimized configuration and cannot effectively consider the probabilistic technical problem of wind speed, intensity of illumination and load.

For achieving the above object, the invention provides following technical scheme:

A self microgrid Optimal Configuration Method, described method comprises:

Utilize the related data of the self microgrid obtaining historical year interior wind speed, intensity of illumination and load, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively;

The corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in self microgrid location 1 year;

According to pre-conditioned, wind speed, intensity of illumination and the load of typical case's day in each month carried out to state division, and calculate size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state;

According to the facility information of obtained described self microgrid, determine the multiple-objection optimization configuration information of described self microgrid, wherein, described multiple-objection optimization configuration information comprises: optimized variable, optimization aim information, and the operation constraint information of described self microgrid and operation policy information;

The random parent population that generates default population scale, the random value that in described parent population, each individuality comprises all described optimized variables;

Size and corresponding probability of happening according to wind speed, intensity of illumination and load under preset algorithm and all states of described typical case day, to the simulation calculation that iterates of each individuality in described parent population, determine the allocation optimum of described self microgrid.

Preferably, when the equipment of described self microgrid comprises: when blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, described according to the facility information of obtained described self microgrid, determine that the operation constraint information of described self microgrid comprises:

According to the peak load short of electricity probability that obtains predetermined system permission, determine the power-balance constraint information of self microgrid;

According to the unit type of described diesel generator, technical characteristic and economic performance, determine the units limits information of described diesel generator;

According to the unit type of described batteries to store energy equipment, technical characteristic and economic performance, determine the state-of-charge constraint information of described batteries to store energy equipment and discharge and recharge power constraint information.

Preferably, the related data of self microgrid historical year interior wind speed, intensity of illumination and load that described utilization is obtained, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively, comprising:

Utilize the related data of the self microgrid obtaining historical year interior wind speed, intensity of illumination and load, calculate mean value and the standard deviation of each every day in month of each moment wind speed, intensity of illumination and load;

By described each every day in month each the constantly mean value of wind speed and computing formula of the default wind speed probability density function parameter of standard deviation substitution, determine the probability density function of described wind speed;

By described each every day in month each the constantly mean value of intensity of illumination and computing formula of the default intensity of illumination probability density function parameter of standard deviation substitution, determine the probability density function of described intensity of illumination;

By the computing formula of the mean value that described each every day in month, each was loaded constantly and the default load of standard deviation substitution probability density function parameter, determine the probability density function of described load.

The related data of the historical year interior wind speed preferably, obtaining comprises: the incision wind speed v of the blower fan of described self microgrid _ci, rated wind speed v _crwith cut-out wind speed v _co, according to pre-conditioned, the wind speed of typical case's day in each month is carried out to state division, determine size and the probability of happening of the wind speed under each state, comprising:

When the wind speed of determining described typical case day is between described incision wind speed v _ciwith described rated wind speed v _crbetween time, by described incision wind speed v _ciwith described rated wind speed v _crbetween air speed value be divided into the state of the first default number, determine the size of the wind speed under each state;

By the probability density function substitution first state probability of happening formula of described typical case day each wind speed constantly, calculate the probability of happening F of wind speed under each state _w(i), described the first state probability of happening formula is:

$F_W\left(i\right)={\∫}_{[(i-1)/N_W](v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}^{(i/N_W)(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv};$

Wherein, N _wrepresent the described first default number; I=1,2,3 ..., N _w; F (v) represents the probability density function of described typical case day each wind speed constantly;

When the wind speed of determining described typical case day is lower than described incision wind speed v _cior higher than described cut-out wind speed v _cotime, determine that the wind speed under this state is zero;

Using this state as N _w+ 1 state, and by the probability density function substitution second state probability of happening formula of described typical case day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1), wherein, described the second state probability of happening formula is:

$F_W(N_W+1)={\∫}_0^{v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv}+{\∫}_{v_{\mathrm{co}}}^{+\∞}f\left(v\right)\mathrm{dv};$

When the wind speed of determining described typical case day is between described rated wind speed v _crwith described cut-out wind speed v _cobetween time, determine that the wind speed under this state is described rated wind speed;

Using this state as N _w+ 2 states, and by the probability density function substitution third state probability of happening formula of described typical case day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2), wherein, described the second state probability of happening formula is:

$F_W(N_W+2)={\∫}_{v_{\mathrm{cr}}}^{v_{\mathrm{co}}}f\left(v\right)\mathrm{dv}.$

The related data of the intensity of illumination of preferably, obtaining comprises: the minimum intensity of illumination G of the photovoltaic cell work of described self microgrid _minwith specified intensity of illumination G _s, according to pre-conditioned, the intensity of illumination of typical case's day in each month is carried out to state division, determine size and the probability of happening of the intensity of illumination under each state, comprising:

Typical case's day intensity of illumination is between described minimum intensity of illumination G shown in determine _minwith specified intensity of illumination G _sbetween time, by described minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween intensity of illumination be divided into the state of the second default number, determine the size of the intensity of illumination under each state;

By probability density function substitution the 4th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under each state _g(j), described the 4th state probability of happening formula is:

$F_G\left(j\right)={\∫}_{[(j-1)/N_G](G_s-G_{\min })+G_{\min }}^{(j/N_G)(G_s-G_{\min })+G_{\min }}f\left(G\right)\mathrm{dG};$

Wherein, N _grepresent the described second default number, j=1,2,3 ..., N _g, represent that second presets the sequence number of a number state; F (G) represents the probability density function of described typical case day each intensity of illumination constantly;

When the intensity of illumination of determining described typical case day is lower than described minimum intensity of illumination G _mintime, determine that described intensity of illumination is zero;

Using this state as N _g+ 1 state, and by probability density function substitution the 5th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 1), described the 5th state probability of happening formula is:

$F_G(N_G+1)={\∫}_0^{G_{\min }}f\left(G\right)\mathrm{dG};$

When determining that the intensity of illumination of described typical case day is not less than specified intensity of illumination G _stime, determine that described intensity of illumination is described specified intensity of illumination G _s;

Using this state as N _g+ 2 states, and by probability density function substitution the 6th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 2), described the 6th state probability of happening formula is:

$F_G(N_G+2)={\∫}_1^{+\∞}f\left(G\right)\mathrm{dG};$

Preferably, described foundation is pre-conditioned, the load of typical case's day in each month is carried out to state division, and calculate the size of the load under each state and corresponding probability of happening, comprising:

Load within 3 standard deviations of distance load mean value is divided into the state of the 3rd default number, determines the size of loading under each state;

Probability density function substitution the 7th state probability of happening formula by described typical case day each load constantly, calculates the probability of happening F loading under each state _l(z), described the 7th state probability of happening formula is:

$F_L\left(z\right)={\∫}_{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·(z-1)/N_L}^{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·z/N_L}f\left(P_L\right){\mathrm{dP}}_L;$

Wherein, N _lrepresent the described the 3rd default number; Z=1,2,3 ..., N _l; f(P _l) represent the probability density function of described typical case day each wind speed constantly, μ _lrepresent described typical case's day each mean value of constantly loading, σ _lrepresent described typical case's day each standard deviation of constantly loading.

Preferably, described size and corresponding probability of happening according to wind speed, intensity of illumination and load under preset algorithm and all states of described typical case day, to the parameter of each optimized variable in the described parent population simulation calculation that iterates, determine the allocation optimum of described self microgrid, comprising:

According to preset algorithm, each individuality in described parent population is carried out to simulation calculating, determine each individual corresponding target function value in described parent population;

According to described target function, described parent population is carried out to quick non-dominated Sorting;

By the crossover and mutation computing to described parent population, determine the progeny population corresponding to described parent population;

Described parent population and described progeny population are merged to the population obtaining and carry out quick non-dominated Sorting, and therefrom select the optimum individuality of default population scale to form new parent population;

Judge whether current iteration number of times reaches default maximum iteration time;

If so, determine that Output rusults is the allocation optimum of described self microgrid;

If not, return describedly according to preset algorithm, each individuality in described parent population is carried out to simulation calculating, determine that in described parent population, each individual corresponding target function value step continues to carry out.

Self microgrid is distributed a device rationally, and described device comprises:

The first determination module, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively;

First builds module, for the corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in microgrid engineering location 1 year;

The first computing module, for according to pre-conditioned, carries out state division to wind speed, intensity of illumination and the load of typical case's day in each month, and calculates size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state;

The second determination module, be used for according to the facility information of obtained described self microgrid, determine the multiple-objection optimization configuration information of described self microgrid, wherein, described multiple-objection optimization configuration information comprises: optimized variable, optimization aim information, and the operation constraint information of described self microgrid and operation policy information;

The first generation module, for the random parent population that generates default population scale, the random value that in described parent population, each individuality comprises all described optimized variables;

The first simulation algorithm model, size and corresponding probability of happening for wind speed, intensity of illumination and load according under preset algorithm and all states of described typical case day, to the parameter of each optimized variable in the described parent population simulation calculation that iterates, determine the optimal solution set that described self microgrid is distributed rationally.

Preferably, when the equipment of described self microgrid comprises: when blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, described the second determination module comprises:

First determines subelement, for the peak load short of electricity probability allowing according to predetermined system, determines the power-balance constraint information of self microgrid;

Second determines subelement, for according to the unit type of described diesel generator, technical characteristic and economic performance, determines the units limits information of described diesel generator;

The 3rd determines subelement, for according to the unit type of described batteries to store energy equipment, technical characteristic and economic performance, determines the state-of-charge constraint information of described batteries to store energy equipment and discharges and recharges power constraint information.

Preferably, shown in, the first determination module comprises:

The first computation subunit, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, calculates mean value and the standard deviation of each every day in month of each moment wind speed, intensity of illumination and load;

The 4th determines subelement, for by described each every day in month each mean value of wind speed and computing formula of the default wind speed probability density function parameter of standard deviation substitution constantly, determines the probability density function of described wind speed;

The 5th determines subelement, for by described each every day in month each mean value of intensity of illumination and computing formula of the default intensity of illumination probability density function parameter of standard deviation substitution constantly, determines the probability density function of described intensity of illumination;

The 6th determines subelement, for each is loaded constantly by described each every day in month mean value and the computing formula of the default load of standard deviation substitution probability density function parameter, determines the probability density function of described load.

Known via above-mentioned technical scheme, compared with prior art, the present invention openly provides a kind of self microgrid Optimal Configuration Method and device, the method is for wind speed, the uncertainty of intensity of illumination and load, application wind speed, the Construction of probability model of intensity of illumination and load the typical case day in each month in microgrid location 1 year, and by utilizing multistate system theoretical, by typical case's wind speed of day, intensity of illumination and load carry out multimode division, thereby by continuous nondeterministic statement according to and probability distribution rule be transformed into a plurality of discrete Qualitative states really and process, thereby can exert oneself by direct modeling blowing machine, the photovoltaic cell randomness feature of exerting oneself and load, reduced the difficulty that solves of Optimal Allocation Model, the self micro-grid system configuration that assurance draws is more reasonable, scene is exerted oneself stronger with the robustness of load fluctuation.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skills, do not paying under the prerequisite of creative work, other accompanying drawing can also be provided according to the accompanying drawing providing.

Fig. 1 is the flow chart of a kind of self microgrid of the present invention Optimal Configuration Method;

Fig. 2 is the structural representation that a kind of self microgrid of the present invention is distributed device rationally.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.

In actual applications, self microgrid need to be according to data such as the actual working characteristics of various assemblies in the on-site geographical position of microgrid engineering, geographical conditions, meteorological condition, micro-grid system and user power utilization requirements, determine type and the capacity of this each part of self micro-grid system, and system capacity management strategy parameter, this self microgrid is optimized to configuration, it is worked in the ideal situation as far as possible.

For this reason, existing self microgrid Optimal Configuration Method normally first obtains the facility information of each distributed power source in this self micro-grid system, required Mathematical Modeling is distributed in foundation rationally, again according to different optimization demands, determine the optimization aim of this self micro-grid system, set up corresponding objective optimization model, afterwards, according to this Mathematical Modeling and objective optimization model, set up Optimal Allocation Model, obtain this self micro-grid system location wind speed of in the past a certain year, intensity of illumination and load data, bring default intelligent algorithm into (as genetic algorithm, particle cluster algorithm etc.) solve this Optimal Allocation Model, thereby obtain the allocation optimum of this self micro-grid system.

Yet, find after deliberation, because regenerative resource is as the uncertainty of wind energy, solar energy and power load etc., while having caused self microgrid actual motion, each wind speed, intensity of illumination and load data constantly compares with the data of being obtained historical year that there is some difference, and the system configuration that this difference may cause the optimization of existing self microgrid Optimal Configuration Method to draw is not optimum under actual motion condition.

In order to solve the above-mentioned technical problem existing in existing self microgrid Optimal Configuration Method, the embodiment of the invention discloses a kind of self microgrid Optimal Configuration Method and device, the method is for wind speed, the uncertainty of intensity of illumination and load, application wind speed, the Construction of probability model of intensity of illumination and load the typical case day in each month in self microgrid location 1 year, and by utilizing multistate system theoretical, by typical case's wind speed of day, intensity of illumination and load carry out multimode division, thereby by continuous nondeterministic statement according to and probability distribution rule be transformed into a plurality of discrete Qualitative states really and process, thereby can exert oneself by direct modeling blowing machine, the photovoltaic cell randomness feature of exerting oneself and load, reduced the difficulty that solves of Optimal Allocation Model, guaranteed that the self micro-grid system configuration drawing is more reasonable, scene is exerted oneself stronger with the robustness of load fluctuation.

Embodiment mono-:

As shown in Figure 1, be the flow chart of a kind of self microgrid of the present invention Optimal Configuration Method, the method specifically can comprise:

Step S11: utilize the related data of the self microgrid history year interior wind speed, intensity of illumination and the load that obtain, the corresponding probability density function that in definite this history year, wind speed, intensity of illumination and the load in each every day in month in each moment meets respectively.

The uncertainty of wind speed, intensity of illumination and the power load of the embodiment of the present invention based on self microgrid, sets up respectively corresponding probabilistic model, to carry out subsequent operation, concrete:

One, the probabilistic model of wind speed

The embodiment of the present invention adopts biparametric Weibull (Weibull) distribution function to describe the wind speed distribution characteristics of this self microgrid, and wherein, this Weibull Function expression formula can be:

$f\left(v\right)=\frac{k}{c}\·{\left(\frac{v}{c}\right)}^{k-1}\·\exp [-{\left(\frac{v}{c}\right)}^k]---\left(1\right)$

k＝(σ _W/γ) ^-1.086 (2)

c＝γ/Γ(1+k ^-1) (3)

In above formula, f (v) represents the probability density function of wind speed, and k represents form parameter, and c represents scale parameter, and v represents actual wind speed, and γ represents the mean value of the wind speed in each every day in month in each moment in historical year; σ _wthe standard deviation that represents the wind speed in each every day in month in each moment in historical year; Γ () is gamma (Gamma) function.

For the parameter of this Weibull Function expression formula, the related data of historical year (8760 hours) the interior wind speed of self microgrid that embodiment of the present invention utilization is obtained, calculates mean value γ and the standard deviation sigma of each every day in month of each moment wind speed _wafterwards, entered to preset again the computing formula (being above-mentioned formula (2) and (3)) of wind speed probability density function parameter, afterwards, the above-mentioned formula of acquired results substitution (1) can be determined to the probability density function (i.e. the regularity of distribution of the wind speed in each every day in month in each moment in this history year) of wind speed.

Wherein, for solving of above-mentioned form parameter and scale parameter, can also be by cumulative distribution function matching Weibull curve (being least square method), or estimate with mean wind speed and maximum wind velocity, concrete solution procedure can be with reference to the method for solving of existing Weibull distribution parameters (being form parameter and scale parameter), and the present invention will no longer describe in detail at this.

Two, the probabilistic model of intensity of illumination

Find after deliberation, the intensity of illumination of certain period (as one hour or several hours) interior self microgrid meets beta (Beta) and distributes, and the expression formula of the probability density function of this intensity of illumination can be:

$f\left(G\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}\·{\left(\frac{G}{G_{\max }}\right)}^{\mathrm{\α}-1}\·{(1-\frac{G}{G_{\max }})}^{\mathrm{\β}-1}---\left(4\right)$

$\mathrm{\α}={\mathrm{\μ}}_G[\frac{{\mathrm{\μ}}_G(1-{\mathrm{\μ}}_G)}{{{\mathrm{\σ}}_G}^2}-1]---\left(5\right)$

$\mathrm{\β}=(1-{\mathrm{\μ}}_G)\·[\frac{{\mathrm{\μ}}_G(1-{\mathrm{\μ}}_G)}{{{\mathrm{\σ}}_G}^2}-1]---\left(6\right)$

In above formula, G and G _maxrepresent respectively actual intensity of illumination and maximum intensity of illumination (being specified intensity of illumination) in certain period; α and β are the form parameter that Beta distributes; μ _gand σ _grepresent respectively illumination mean value and standard deviation in this period; Γ () is gamma function.

As the above analysis, solving of the density probability function parameter of intensity of illumination is also the average value mu that first calculates each moment intensity of illumination interior each every day in month in this history year _gand standard deviation sigma _g, then by the above-mentioned formula of its substitution (5) and (6), determine the form parameter that beta distributes, and then substitution formula (4), obtain the probability density function of intensity of illumination.

Three, the probabilistic model of load

Find after deliberation, the approximate Normal Distribution of fluctuation of the load of self microgrid, thereby the expression formula of the probability density function of this load can be:

$f\left(P_L\right)=\frac{1}{\sqrt{2\mathrm{\π}}{\mathrm{\σ}}_L}\·\exp [-\frac{{(P-{\mathrm{\μ}}_L)}^2}{{{2\mathrm{\σ}}_L}^2}]---\left(7\right)$

In formula, μ _land σ _lthe mean value and the standard deviation that represent respectively the load in each every day in month in each moment in this history year; P represents actual load.

In like manner, solve the probability density function parameter of the load in each every day in month in each moment in historical year, first will be according to the related data of certain historical year internal loading of this obtained self microgrid, obtain accordingly mean value and the standard deviation of the load in each every day in month in each moment in this history year, by the default load of its substitution new probability formula, be formula (7), determine the probability density function of load.

In sum, the solution procedure of the probabilistic model of wind speed, intensity of illumination and load, all that to take 1h (unit: hour) be time step, first obtaining this self microgrid is the related data of wind speed, intensity of illumination and the load of 8760h in known certain historical year, calculate accordingly again mean value and the standard deviation of wind speed per hour interior each every day in month in this history year, intensity of illumination and load, the computing formula of the probability density function parameter that substitution is corresponding, thus determine the probability density function of wind speed, intensity of illumination and load.

Step S12: the corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in self microgrid location 1 year.

In the practical application of self microgrid, due to wind speed, often all there is certain uncertainty in intensity of illumination and power load, thereby, wind speed at existing self microgrid Optimal Configuration Method based on certain historical year, the related data of intensity of illumination and load, when this self microgrid is optimized to configuration, will inevitably affect it and distribute effect rationally, in order to address this problem, the embodiment of the present invention is determined wind speed by said method, the probabilistic model of intensity of illumination and load, and then construct accordingly the typical case's day that represents each month in this history year, afterwards, only need be based on each wind speed constantly of this typical case's day, the related data of intensity of illumination and load, this self microgrid is optimized to configuration, for the distributing rationally of self microgrid of current actual motion established reliable basis.

Step S13: according to pre-conditioned, wind speed, intensity of illumination and the load of typical case's day in each month are carried out to state division, determine size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state.

In actual applications, because the distribution trend of wind speed, intensity of illumination and the load in each month is not all not etc., need to carry out different disposal for the actual conditions of different typical case day, and because wind speed directly determines exerting oneself of the exerting oneself of blower fan of this self microgrid, photovoltaic cell that intensity of illumination directly determines this self microgrid, so, the embodiment of the present invention can be carried out multimode modeling by wind speed, the intensity of illumination of the typical case's day to each month, complete the multimode modeling that blower fan is exerted oneself, photovoltaic cell is exerted oneself, concrete:

One, the multimode modeling of wind speed

In embodiments of the present invention, within obtained self microgrid history year, the related data of wind speed comprises: the incision wind speed v of the blower fan of this self microgrid _ci, rated wind speed v _crwith cut-out wind speed v _cotime, the multimode modeling detailed process of wind speed can be as follows:

1), when the wind speed of determining typical case's day is between this incision wind speed v _ciwith this rated wind speed v _crbetween time, will cut wind speed v _ciwith rated wind speed v _crbetween air speed value be divided into the state of the first default number, determine the size of the wind speed under each state.Now, if the first default number is designated as to N _w, the discretization step-length of the wind speed of dividing is (v _cr-v _ci)/N _w, so, the wind speed v (i) under each state can be approximately:

v(i)＝[(i-1/2)/N _W](v _cr-v _ci)+v _ci (8)

Afterwards, by wind speed probability density function f (v) (being formula (1)) the substitution first state probability of happening formula in this typical case's day in each moment, calculate the probability of happening F of wind speed under each state _w(i), i=1,2,3 ..., N _w, this first state probability of happening formula can be:

$F_W\left(i\right)={\∫}_{[(i-1)/N_W(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}^{(i/N_W)(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv}---\left(9\right)$

2), when the wind speed of determining typical case's day is lower than incision wind speed v _cior higher than cut-out wind speed v _cotime, because blower fan is now output as 0, the wind speed under this state can be thought and equals zero, if this state is designated as to N _w+ 1 state, now, can, by probability density function f (v) the substitution second state probability of happening formula of typical case's day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1), wherein, the second state probability of happening formula can be:

$F_W(N_W+1)={\∫}_0^{v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv}+{\∫}_{v_{\mathrm{co}}}^{+\∞}f\left(v\right)\mathrm{dv}---\left(10\right)$

3), when the wind speed of determining typical case's day is between rated wind speed v _crwith cut-out wind speed v _cobetween time, blower fan will be exported with rated power, the wind speed under this state can be thought and equals rated wind speed v so _crif this state is designated as to N _w+ 2 states, by probability density function f (v) the substitution third state probability of happening formula of typical case's day each wind speed constantly, can calculate N so _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2), this second state probability of happening formula is:

$F_W(N_W+2)={\∫}_{v_{\mathrm{cr}}}^{v_{\mathrm{co}}}f\left(v\right)\mathrm{dv}---\left(11\right)$

Learn in sum, according to the concrete affiliated scope of the wind speed of typical case's day, carry out different states and divide, then adopt corresponding state probability of happening formula, calculate the probability of happening of wind speed under each state, in order to subsequent calculations, call.

Two, the multimode modeling of intensity of illumination

The related data of the self microgrid intensity of illumination of historical year that in embodiments of the present invention, device obtains can comprise: the minimum intensity of illumination G of the photovoltaic cell work of this self microgrid _minwith specified intensity of illumination G _s, for typical case, in a few days the state division of each intensity of illumination constantly can be as follows so:

1), when typical case day intensity of illumination between minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween time, by described minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween intensity of illumination be divided into the state of the second default number, determine the size of the intensity of illumination under each state.

Now, if the second default number is designated as to N _g, so, the intensity of illumination G under each state (j) can be approximated to be:

G(j)＝[(j-1/2)/N _G](G _s-G _min)+G _min (12)

By probability density function F (G) (being formula (4)) substitution the 4th state probability of happening formula of the intensity of illumination in this typical case's day in each moment, calculate the probability of happening F of intensity of illumination under each state _g(j), j=1,2,3 ..., N _g, wherein, the 4th state probability of happening formula is:

$F_G\left(j\right)={\∫}_{[(j-1)/N_G](G_s-G_{\min })+G_{\min }}^{(j/N_G)(G_s-G_{\min })+G_{\min }}f\left(G\right)\mathrm{dG}---\left(13\right)$

2), when the intensity of illumination of determining typical case's day is lower than minimum intensity of illumination G _mintime, now, it is zero that the photovoltaic cell of self microgrid is exerted oneself, the intensity of illumination under this state also can be thought and equal zero so, and if this state is designated as to N _g+ 1 state, by probability density function f (G) substitution the 5th state probability of happening formula of the intensity of illumination in this typical case's day in each moment, calculates the probability of happening F of intensity of illumination under this state _g(N _g+ 1), wherein, the 5th state probability of happening formula is:

$F_G(N_G+1)={\∫}_0^{G_{\min }}f\left(G\right)\mathrm{dG}---\left(14\right)$

3), when determining that the intensity of illumination of described typical case day is not less than specified intensity of illumination G _stime, now, because photovoltaic cell is that the intensity of illumination under this state equals specified intensity of illumination G with rated power output _sif this state is designated as to N _g+ 2 states, so by probability density function f (G) substitution the 6th state probability of happening formula of the intensity of illumination in this typical case's day in each moment, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 2), described the 6th state probability of happening formula is:

$F_G(N_G+2)={\∫}_1^{+\∞}f\left(G\right)\mathrm{dG}---\left(15\right)$

Three, the multimode modeling of load

Due to the approximate Normal Distribution of fluctuation of load, the scope of approximately 99.3% numeric distribution within 3 standard deviations of range averaging value, therefore, the embodiment of the present invention can be supposed each P that constantly loads of this typical case's day _l(t) be distributed in apart from this average value mu of constantly loading _l(t) and 3 standard deviation sigma _l(t) scope within, i.e. P _l(t) ∈ [μ _l(t)-3 σ _l(t), μ _l(t)+3 σ _l(t)] (16)

By each load sharing constantly within the scope of this, be the state of the 3rd default number, the mean value that this is loaded constantly, standard deviation and the 3rd default number substitution P _l(z)=μ _l-3 σ _l+ 6 σ _l(z-1/2)/N _l, calculate the big or small P loading under each state _l(i), afterwards, by this typical case probability density function f (P of each load constantly in a few days _l) substitution the 7th state probability of happening formula, thereby obtain the probability of happening of loading under each state, wherein, the 7th state probability of happening formula is:

$F_L\left(z\right)={\∫}_{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·(z-1)/N_L}^{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·z/N_L}f\left(P_L\right){\mathrm{dP}}_L---\left(17\right)$

In sum, the embodiment of the present invention adopt multistate system theory to typical case day each blower fan is exerted oneself constantly, photovoltaic cell is exerted oneself and load carries out multimode modeling, thereby according to its corresponding probability distribution rule, change continuous nondeterministic statement into a plurality of discrete Qualitative states really and carry out subsequent treatment, avoid setting up stochastic model, reduced the difficulty of distributing rationally.

Step S14: according to the facility information of obtained self microgrid, determine the multiple-objection optimization configuration information of this self microgrid.

Wherein, facility information comprises: the technical characteristic of unit type, equipment and economic performance; This multiple-objection optimization configuration information can comprise: optimized variable, optimization aim information, and the operation constraint information of self microgrid and operation policy information.

In embodiments of the present invention, the equipment of self microgrid can comprise: blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, in actual motion, each equipment has fixing model, and its technical characterictic of the equipment of different model and economical specific be also different, so, in the distributing rationally of self microgrid, needing the variable of optimizing is the quantity of the equipment of certain model, as the diesel engine generator of 2 100kW (unit: kilowatt), rather than the rated capacity of equipment, otherwise, as the diesel engine generator economic parameters with 100kW is optimized, obtaining result is to need diesel engine generator rated capacity 164kW, obviously, there is not the diesel engine generator of 164kW, so, after determining the model of equipment, its technical characteristic is (as rated capacity, range of operation etc.) and economic performance (as operating cost) also just determine thereupon.

Based on this, the optimized variable of the multiple-objection optimization allocation models that the embodiment of the present invention is constructed can comprise: blower fan number of units N _wT, photovoltaic battery panel number N _pV, storage battery number N _bATwith diesel engine generator number of units N _dE, this optimized variable X can be expressed as: X=[N _wTn _pVn _bATn _dE].

It should be noted that, for the requirement (being maximum quantity and minimum number quantitative limitation) of above-mentioned each number of devices, can determine according to actual microgrid requirement of engineering, the present invention is not limited in any way this.

Wherein, the embodiment of the present invention, when self microgrid being optimized to configuration, according to different optimization demands, is determined the optimization aim of this self microgrid, thus the target information of being optimized.In actual applications, optimization aim can comprise economy target and feature of environmental protection target etc. conventionally.

In embodiments of the present invention, select interior total clean ready-made (the total net present cost of life cycle management of self microgrid, abbreviation NPC) desired value is as economy target, comprise the desired value of all costs and benefits in the life cycle (being k) of self microgrid, now can utilize discount rate (being r) that future cash flow is converted to present worth.Wherein, become the desired value C (k) of this part to comprise: initial outlay expense desired value C _i, renewal of the equipment expense desired value C _r(k), operation and maintenance cost desired value C _mand fuel cost desired value C (k) _f(k); The desired value I of income section (k) can comprise: remanent value of equipment after self microgrid end-of-life, and based on this, this NPC can be:

$\mathrm{NPC}=\underset{k=1}{\overset{k}{\mathrm{\Σ}}}\frac{C\left(k\right)-I\left(k\right)}{{(1+r)}^k}---\left(18\right)$

C(k)＝C _I+C _R(k)+C _M(k)+C _F(k) (19)

Thereby in self microgrid is distributed rationally, optimization aim can be: f ₁(X)=min (NPC), X is the optimized variable of this self microgrid, min represents minimum operation.

And for above-mentioned feature of environmental protection target, along with the raising of China to the attention degree of environmental issue, the embodiment of the present invention can (be CO by carbon dioxide ₂) discharge capacity desired value as another target of optimizing.Various in practical application, in above-mentioned each equipment of self microgrid, can produce CO ₂power supply only have diesel engine generator, therefore, the present invention can be by the year diesel-fuel consumption desired value Q of the diesel engine generator of this self microgrid _diebe multiplied by its gas discharging coefficient as CO ₂discharge capacity desired value that is:

$E_{{\mathrm{co}}_2}={\mathrm{\α}}_{{\mathrm{co}}_2}\·Q_{\mathrm{die}}---\left(20\right)$

Therefore, another optimization aim of self microgrid is

In the above embodiment of the present invention, take above-mentioned optimization aim as guidance, for the operation constraint information of determined self microgrid, can comprise the units limits information of power-balance constraint information, diesel generator and the state-of-charge constraint information of batteries to store energy equipment and discharge and recharge power constraint information equipment constraint information.

Wherein, for determining of the units limits information of this self microgrid diesel engine generator, while being worth lower than certain due to the operate power when diesel engine generator, its unit power fuel consumption can be larger, and the useful life that can reduce this diesel engine generator, therefore, in the practical application of the embodiment of the present invention, before diesel engine generator is come into operation, and determine after unit type, technical characteristic and the economic performance of diesel engine generator used, first the minimum operate power P of this diesel engine generator will be set _{g_min}thereby, make the real output P of this diesel engine generator _gbe positioned at this minimum operate power P _{g_min}with rated power P _genbetween, therefore, the units limits condition of diesel engine generator is:

P _{g_min}≤P _g≤P _gen (21)

In addition, find after deliberation, distributing rationally in process of self microgrid, due to the impact that useful life of batteries to store energy equipment (BESS) can be subject to it to discharge and recharge the degree of depth, discharge and recharge number of times and discharge and recharge the factors such as watt level, and, unordered use also can shorten the useful life of storage battery, thereby affects the security of operation of whole self microgrid, and has increased production cost.Therefore,, in order to ensure the useful life of this storage battery, the embodiment of the present invention is provided with certain constraint to the use of storage battery, concrete:

1), the state-of-charge of BESS (state of charge, SOC) constraint

After unit type, technical characteristic and the economic performance of batteries to store energy equipment of determining self microgrid, the actual needs of incorporation engineering, determines the maximum state-of-charge SOC of the permission of this batteries to store energy equipment _maxwith minimum state-of-charge SOC _min, thereby, in the process of distributing rationally, make the actual state-of-charge of this batteries to store energy equipment be positioned at maximum state-of-charge SOC _maxwith minimum state-of-charge SOC _minbetween, that is: SOC _min≤ SOC≤SOC _max.

Thereby the charged constraint information that installs determined batteries to store energy equipment comprises: maximum state-of-charge SOC _maxwith minimum state-of-charge SOC _min.

2), BESS's discharges and recharges power constraint

In embodiments of the present invention, for fear of BESS, under small-power, charge, and reduce the number of times that discharges and recharges of BESS, extend its useful life, need batteries to store energy equipment at the minimum charge power P of its permission _{bATch_min}on charge, in addition, also need to meet following condition:

P _{BATch_min}≤P _BATch≤P _{BATch_max} (22)

P _BATdisch≤P _{BATdisch_max} (23)

In formula, P _bATchand P _bATdischbe respectively actual charge power and the discharge power of BESS; P _{bATch_max}and P _{bATch_min}be respectively maximum charge power and minimum charge power that BESS allows, belong to the power constraint information that discharges and recharges of this batteries to store energy equipment.

In addition, in self microgrid is distributed rationally, also need to consider power-balance constraint, exerting oneself to meet load needs to make typical in a few days each power-supply device (being above-mentioned blower fan, photovoltaic battery panel, storage battery and diesel engine generator) constantly, guarantee self microgrid reliable power supply, the embodiment of the present invention can be used year load short of electricity probability (loss of power supply probability, a LPSP) f _lPSPcharacterize the power supply reliability of this self microgrid, wherein,

$f_{\mathrm{LPSP}}=\underset{t=1}{\overset{N}{\mathrm{\Σ}}}{P}_{\mathrm{Loss}}\left(t\right)/\underset{t=1}{\overset{N}{\mathrm{\Σ}}}{P}_L\left(t\right)---\left(24\right)$

In formula, P _loss(t) represent constantly the load desired value of vacancy of t, P _l(t) represent the desired value of the load that t need to meet constantly; Hop count when N is emulation total, in the embodiment of the present invention, because being take 1h as time step, and carries out simulation calculation to typical case corresponding to certain historical year 12 month day, thereby, hop count N=12*24=288 during emulation total.

Based on above-mentioned analysis, the power-balance constraint information of this self microgrid that the embodiment of the present invention is finally determined, i.e. this self microgrid power supply reliability constraints, is specifically as follows:

f _LPSP≤f _Lmax (25)

In formula, f _lmaxrepresent the maximum short of electricity probability that this self microgrid allows.

Yet, self microgrid distribute the sequential emulation that often needs to carry out a plurality of periods rationally, for reasonably coordinating the demand of exerting oneself to meet day part load of each power supply in each emulation period micro-grid system, often need to design a set of rational operation strategy and coordinate each power supply and exert oneself.Thereby, when carrying out emulation to system, the embodiment of the present invention can follow following operation strategy: as blower fan generated output P _wTand photovoltaic cell capable of generating power power P (t) _pV(t) sum is not less than load power demand P _l(t), time, need blower fan and photovoltaic battery panel to exert oneself, and diesel engine generator and batteries to store energy equipment are not worked; And work as excess power, be greater than P _{bATch_min}time, this blower fan and photovoltaic cell can accumulators energy storage device charging, now, when the residual capacity of charge power or batteries to store energy equipment is out-of-limit, this batteries to store energy equipment will be with P _{bATch_max}charge, remainder is consumed or is directly abandoned by controllable load; When excess power is not more than P _{bATch_min}time, this blower fan and photovoltaic cell can not charge by accumulators energy storage device, and now, surplus power (being excess power) can be consumed or directly be abandoned by controllable load.

As blower fan generated output P _wTand photovoltaic cell capable of generating power power P (t) _pV(t) sum is less than load power demand P _l(t) time, if batteries to store energy equipment can meet workload demand, power shortage is preferentially discharged to provide by this batteries to store energy equipment, and diesel engine generator is not worked; If wind-light storage cannot meet workload demand, this batteries to store energy equipment will not discharge, and open diesel engine generator, meet workload demand; If this diesel engine generator is operated under rated power, still cannot meet workload demand, then make this batteries to store energy equipment electric discharge; If this diesel engine generator is operated in rated power, make the electric discharge of batteries to store energy equipment all cannot meet workload demand simultaneously, now there is electricity shortage, can allow the non-important load of cut-off parts.

Therefore, the embodiment of the present invention can be take the above-mentioned information of distributing rationally as guidance according to the obtained self microgrid related datas such as wind speed, intensity of illumination and load in historical time, determines the quantity of optimum various power-supply devices.

Step S15: the random parent population that generates default population scale, the random value that in this parent population, each individuality comprises all optimized variables.

Before execution step S15, each that need read above-mentioned each typical case of calculating day be wind speed, intensity of illumination and load size and the corresponding probability of happening thereof under each state constantly in month, and hop count, population scale N and maximum iteration time t during the emulation set of operator total _maxetc. parameter.

Wherein, population scale N and maximum iteration time t _maxbe some parameters of NAGA-II algorithm, N gets the numerical value between 30～200, t conventionally _maxget the numerical value between 100～300, when embodiment of the present invention practical application, can repeatedly debug the suitable N of rear selection and t by computing staff _max.

In embodiments of the present invention, adopt random fashion to generate the parent population P of default population scale, obviously, each individuality in this parent population P comprises above-mentioned optimized variable.

Step S16: according to size and the corresponding probability of happening of wind speed, intensity of illumination and load under preset algorithm and all states of described typical case day, to the simulation calculation that iterates of each individuality in described parent population, determine the optimal solution set of this self microgrid.

In embodiments of the present invention, preset algorithm can be NAGA-II algorithm.After definite parent population P, can be according to size and the corresponding probability of happening of wind speed, intensity of illumination and load under this algorithm and each state of the typical case who calculates day, each individuality in parent population is carried out to simulation calculation, determine each individual corresponding optimization target values, be above-mentioned economy desired value and feature of environmental protection desired value, then carry out quick non-dominated Sorting, to parent population P intersect, mutation operation, obtain corresponding progeny population Q, afterwards, parent population P is merged with corresponding progeny population Q, obtain a new population R _t, now, by the population R new to this _tcarry out after quick non-dominated Sorting, select the individuality of wherein optimum default population scale number, form new parent population P,, now completed iterative process one time, according to this alternative manner circulation, carry out, until reach maximum iteration time t _maxtime, the result of output is the optimal solution set of distributing rationally of this self microgrid, and each individuality in optimal solution set comprises: optimum blower fan number of units, photovoltaic battery panel number, storage battery number and diesel engine generator number of units.

In sum, the Optimal Configuration Method of the self microgrid that the embodiment of the present invention provides is for wind speed, the uncertainty of intensity of illumination and load, application wind speed, the Construction of probability model of intensity of illumination and load the typical case day in each month in certain historical year, make the present invention only according to each month typical case day wind speed, the related data of intensity of illumination and load is carried out follow-up distributing rationally, and utilize multistate system theoretical, by typical case's wind speed of day, intensity of illumination and load carry out multimode division, thereby by continuous nondeterministic statement according to and probability distribution rule be transformed into a plurality of discrete Qualitative states really and process, thereby can exert oneself by direct modeling blowing machine, the photovoltaic cell randomness feature of exerting oneself and load, reduced the difficulty that solves of Optimal Allocation Model, guaranteed that the system configuration obtaining is more reasonable, scene is exerted oneself stronger with the robustness of load fluctuation.

Embodiment bis-:

As shown in Figure 2, for a kind of self microgrid of the present invention, distribute the structural representation of device rationally, this device can comprise:

The first determination module S21, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively.

In embodiments of the present invention, the first determination module S21 can comprise:

The first computation subunit, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, calculates mean value and the standard deviation of each every day in month of each moment wind speed, intensity of illumination and load;

The 4th determines subelement, for by described each every day in month each mean value of wind speed and computing formula of the default wind speed probability density function parameter of standard deviation substitution constantly, determines the probability density function of described wind speed;

The 5th determines subelement, for by described each every day in month each mean value of intensity of illumination and computing formula of the default intensity of illumination probability density function parameter of standard deviation substitution constantly, determines the probability density function of described intensity of illumination;

The 6th determines subelement, for each is loaded constantly by described each every day in month mean value and the computing formula of the default load of standard deviation substitution probability density function parameter, determines the probability density function of described load.

First builds module S22, for the corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in self microgrid location 1 year.

The first computing module S23, for according to pre-conditioned, carries out state division to wind speed, intensity of illumination and the load of typical case's day in each month, and calculates size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state.

In obtained historical year, the related data of wind speed comprises: the incision wind speed v of the blower fan of described self microgrid _ci, rated wind speed v _crwith cut-out wind speed v _co, the first computing module S23 can comprise:

The first state is divided subelement, for the wind speed determining described typical case day between described incision wind speed v _ciwith described rated wind speed v _crbetween time, by this incision wind speed v _ciwith rated wind speed v _crbetween air speed value be divided into the state of the first default number, determine the size of the wind speed under each state.

The second computation subunit, for by the probability density function substitution first state probability of happening formula of the wind speed in this typical case's day in each moment, calculates the probability of happening F of wind speed under each state _w(i), this first state probability of happening formula is:

$F_W\left(i\right)={\∫}_{[(i-1)/N_W(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}^{(i/N_W)(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv};$

Wherein, N _wrepresent the first default number; I=1,2,3 ..., N _w; F (v) represents the probability density function of described typical case day each wind speed constantly.

The 8th determines subelement, for the wind speed when determining described typical case day lower than described incision wind speed v _cior higher than described cut-out wind speed v _cotime, determine that the wind speed under this state is zero.

The 3rd computation subunit, for using this state as N _w+ 1 state, and by the probability density function substitution second state probability of happening formula of the wind speed in this typical case's day in each moment, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1).

Wherein, the second state probability of happening formula is:

$F_W(N_W+1)={\∫}_0^{v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv}+{\∫}_{v_{\mathrm{co}}}^{+}f\left(v\right)\mathrm{dv};$

The 9th determines subelement, for the wind speed when determining described typical case day between described rated wind speed v _crwith described cut-out wind speed v _cobetween time, determine that the wind speed under this state is described rated wind speed.

The 4th computation subunit, for using this state as N _w+ 2 states, and by the probability density function substitution third state probability of happening formula of described typical case day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2).

Wherein, the second state probability of happening formula is:

$F_W(N_W+2)={\∫}_{v_{\mathrm{cr}}}^{v_{\mathrm{co}}}f\left(v\right)\mathrm{dv}.$

In like manner, the related data when obtained intensity of illumination comprises: the minimum intensity of illumination G of the photovoltaic cell work of described self microgrid _minwith specified intensity of illumination G _stime, the first computing module S23 can also comprise:

Second divides subelement, for typical case's shown in determine day intensity of illumination between described minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween time, by this minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween intensity of illumination be divided into the state of the second default number, determine the size of the intensity of illumination under each state.

The 5th computation subunit, for by probability density function substitution the 4th state probability of happening formula of typical case's day each intensity of illumination constantly, calculates the probability of happening F of intensity of illumination under each state _g(j).

Wherein, the 4th state probability of happening formula can be:

$F_G\left(j\right)={\∫}_{[(j-1)/N_G](G_S-G_{\min })+G_{\min }}^{(j/N_G)(G_S-G_{\min })+G_{\min }}f\left(G\right)\mathrm{dG};$

In formula, N _grepresent the second default number, j=1,2,3 ..., N _g, represent that second presets the sequence number of a number state; F (G) represents the probability density function of typical case's day each intensity of illumination constantly;

The tenth determines subelement, for the intensity of illumination when determining this typical case's day lower than described minimum intensity of illumination G _mintime, determine that this intensity of illumination is zero.

The 6th computation subunit, for using this state as N _g+ 1 state, and by probability density function substitution the 5th state probability of happening formula of the intensity of illumination in this typical case's day in each moment, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 1).

Wherein, the 5th state probability of happening formula can be:

$F_G(N_G+1)={\∫}_0^{G_{\min }}f\left(G\right)\mathrm{dG};$

The 11 determines subelement, for the intensity of illumination when definite this typical case's day, is not less than specified intensity of illumination G _stime, determine that this intensity of illumination is specified intensity of illumination G _s.

The 7th computation subunit, for using this state as N _g+ 2 states, and by probability density function substitution the 6th state probability of happening formula of typical case's day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 2).

Wherein, the 6th state probability of happening formula can be:

$F_G(N_G+2)={\∫}_1^{+\∞}f\left(G\right)\mathrm{dG}.$

In addition, this first computing module S23 can also comprise:

The 3rd divides subelement: for the load within 3 standard deviations of distance load mean value being divided into the state of the 3rd default number, determine the size of loading under each state.

In embodiments of the present invention, can be according to P _l(z)=μ _l-3 σ _l+ 6 σ _l(z-1/2)/N _ldetermine the size of loading under each state.

The 8th computation subunit: for by probability density function substitution the 7th state probability of happening formula of typical case's day each load constantly, calculate the probability of happening F loading under each state _l(z).

Wherein, the 7th state probability of happening formula can be:

$F_L\left(z\right)={\∫}_{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·(z-1)/N_L}^{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·z/N_L}f\left(P_L\right){\mathrm{dP}}_L;$

N _lrepresent the described the 3rd default number; Z=1,2,3 ..., N _l; f(P _l) represent the probability density function of described typical case day each wind speed constantly, μ _lrepresent described typical case's day each mean value of constantly loading, σ _lrepresent described typical case's day each standard deviation of constantly loading.

The second determination module S24, be used for according to the facility information of obtained described self microgrid, determine the multiple-objection optimization configuration information of described self microgrid, wherein, described multiple-objection optimization configuration information comprises: optimized variable, optimization aim information, and the operation constraint information of described self microgrid and operation policy information.

In embodiments of the present invention, when the equipment of described self microgrid comprises: when blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, described the second determination module S24 comprises:

First determines subelement, for the peak load short of electricity probability allowing according to predetermined system, determines the power-balance constraint information of self microgrid;

Second determines subelement, for according to the unit type of described diesel generator, technical characteristic and economic performance, determines the units limits information of described diesel generator;

The 3rd determines subelement, for according to the unit type of described batteries to store energy equipment, technical characteristic and economic performance, determines the charged constraint information of described batteries to store energy equipment and discharges and recharges power constraint information.

The first generation module S25, for the parent population that the random generation of described optimized variable is preset to population scale.

Wherein, optimized variable can comprise: the number of units of the blower fan of self microgrid, the number of photovoltaic battery panel, the number of storage battery and the number of units of diesel engine generator.

The first simulation algorithm model S26, size and corresponding probability of happening for wind speed, intensity of illumination and load according under preset algorithm and all states of described typical case day, each individuality in described parent population is carried out to simulation calculation, the optimal solution set that shown in determining, self microgrid is distributed rationally.

In embodiments of the present invention, this first simulation algorithm model S26 can comprise:

The 7th determines subelement, for according to preset algorithm, each individuality in described parent population is carried out to simulation calculating, determines described each individual corresponding target function value;

The first sequence subelement, for according to described target function, carries out quick non-dominated Sorting to described parent population;

The first operator unit, for by the crossover and mutation computing to described parent population, determines the progeny population corresponding to described parent population;

The first chooser unit, carries out quick non-dominated Sorting for described parent population and described progeny population are merged to the population obtaining, and therefrom selects the optimum individuality of default population scale to form new parent population;

The first judgment sub-unit, for judging whether current iteration number of times reaches default maximum iteration time, if so, determines that Output rusults is the allocation optimum of described self microgrid.

The embodiment of the present invention is for wind speed, the uncertainty of intensity of illumination and load, application wind speed, the Construction of probability model of intensity of illumination and load the typical case day in each month in certain historical year, make the present invention only according to each month typical case day wind speed, the related data of intensity of illumination and load is carried out follow-up distributing rationally, and by utilizing multistate system theoretical, by typical case's wind speed of day, intensity of illumination and load carry out multimode division, thereby by continuous nondeterministic statement according to and probability distribution rule be transformed into a plurality of discrete Qualitative states really and process, thereby can exert oneself by direct modeling blowing machine, the photovoltaic cell randomness feature of exerting oneself and load, reduced the difficulty that solves of Optimal Allocation Model, the system configuration that assurance obtains is more reasonable, scene is exerted oneself stronger with the robustness of load fluctuation.

It should be noted that; " first " in the above embodiment of the present invention, " second " ... do not represent order, it is only for distinguishing disparate modules or unit, and; for above-mentioned module and unit; all belong to functional name, it can also be by Optimal Configuration Method being carried out to the division of other modes, and again divided step is named; as long as the technical scheme providing is identical with technical scheme essence provided by the present invention, all belong to protection range of the present invention.

In this specification, each embodiment adopts the mode of going forward one by one to describe, and each embodiment stresses is the difference with other embodiment, between each embodiment identical similar part mutually referring to.For the disclosed device of embodiment, because it corresponds to the method disclosed in Example, so description is fairly simple, relevant part partly illustrates referring to method.

Above-mentioned explanation to the disclosed embodiments, makes professional and technical personnel in the field can realize or use the present invention.To the multiple modification of these embodiment, will be apparent for those skilled in the art, General Principle as defined herein can, in the situation that not departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention will can not be restricted to these embodiment shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (10)

1. a self microgrid Optimal Configuration Method, is characterized in that, described method comprises:

Utilize the related data of the self microgrid obtaining historical year interior wind speed, intensity of illumination and load, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively;

The corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in self microgrid location 1 year;

According to pre-conditioned, wind speed, intensity of illumination and the load of typical case's day in each month carried out to state division, and calculate size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state;

According to the facility information of obtained described self microgrid, determine the multiple-objection optimization configuration information of described self microgrid, wherein, described multiple-objection optimization configuration information comprises: optimized variable, optimization aim information, and the operation constraint information of described self microgrid and operation policy information;

The random parent population that generates default population scale, the random value that in described parent population, each individuality comprises all described optimized variables;

Size and corresponding probability of happening according to wind speed, intensity of illumination and load under preset algorithm and all states of described typical case day, to the simulation calculation that iterates of each individuality in described parent population, determine the allocation optimum of described self microgrid.

2. method according to claim 1, it is characterized in that, when the equipment of described self microgrid comprises: when blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, described according to the facility information of obtained described self microgrid, determine that the operation constraint information of described self microgrid comprises:

According to the peak load short of electricity probability of the predetermined system permission of obtaining, determine the power-balance constraint information of self microgrid;

According to the unit type of described diesel generator, technical characteristic and economic performance, determine the units limits information of described diesel generator;

According to the unit type of described batteries to store energy equipment, technical characteristic and economic performance, determine the state-of-charge constraint information of described batteries to store energy equipment and discharge and recharge power constraint information.

3. method according to claim 2, it is characterized in that, the related data of self microgrid historical year interior wind speed, intensity of illumination and load that described utilization is obtained, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively, comprising:

Utilize the related data of the self microgrid obtaining historical year interior wind speed, intensity of illumination and load, calculate mean value and the standard deviation of each every day in month of each moment wind speed, intensity of illumination and load;

By described each every day in month each the constantly mean value of wind speed and computing formula of the default wind speed probability density function parameter of standard deviation substitution, determine the probability density function of described wind speed;

By described each every day in month each the constantly mean value of intensity of illumination and computing formula of the default intensity of illumination probability density function parameter of standard deviation substitution, determine the probability density function of described intensity of illumination;

By the computing formula of the mean value that described each every day in month, each was loaded constantly and the default load of standard deviation substitution probability density function parameter, determine the probability density function of described load.

4. method according to claim 1, is characterized in that, the related data of the historical year interior wind speed obtaining comprises: the incision wind speed v of the blower fan of described self microgrid _ci, rated wind speed v _crwith cut-out wind speed v _co, according to pre-conditioned, the wind speed of typical case's day in each month is carried out to state division, determine size and the probability of happening of the wind speed under each state, comprising:

When the wind speed of determining described typical case day is between described incision wind speed v _ciwith described rated wind speed v _crbetween time, by described incision wind speed v _ciwith described rated wind speed v _crbetween air speed value be divided into the state of the first default number, determine the size of the wind speed under each state;

By the probability density function substitution first state probability of happening formula of described typical case day each wind speed constantly, calculate the probability of happening F of wind speed under each state _w(i), described the first state probability of happening formula is:

$F_W\left(i\right)={\∫}_{[(i-1)/N_W](v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}^{(i/N_W)(v_{\mathrm{cr}}-v_{\mathrm{ci}})+v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv};$

Wherein, N _wrepresent the described first default number; I=1,2,3 ..., N _w; F (v) represents the probability density function of described typical case day each wind speed constantly;

When the wind speed of determining described typical case day is lower than described incision wind speed v _cior higher than described cut-out wind speed v _cotime, determine that the wind speed under this state is zero;

Using this state as N _w+ 1 state, and by the probability density function substitution second state probability of happening formula of described typical case day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1), wherein, described the second state probability of happening formula is:

$F_W(N_W+1)={\∫}_0^{v_{\mathrm{ci}}}f\left(v\right)\mathrm{dv}+{\∫}_{v_{\mathrm{co}}}^{+\∞}f\left(v\right)\mathrm{dv};$

When the wind speed of determining described typical case day is between described rated wind speed v _crwith described cut-out wind speed v _cobetween time, determine that the wind speed under this state is described rated wind speed;

Using this state as N _w+ 2 states, and by the probability density function substitution third state probability of happening formula of described typical case day each wind speed constantly, calculate N _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2), wherein, described the second state probability of happening formula is:

$F_W(N_W+2)={\∫}_{v_{\mathrm{cr}}}^{v_{\mathrm{co}}}f\left(v\right)\mathrm{dv}.$

5. method according to claim 1, is characterized in that, the related data of the intensity of illumination of obtaining comprises: the minimum intensity of illumination G of the photovoltaic cell work of described self microgrid _minwith specified intensity of illumination G _s, according to pre-conditioned, the intensity of illumination of typical case's day in each month is carried out to state division, determine size and the probability of happening of the intensity of illumination under each state, comprising:

Typical case's day intensity of illumination is between described minimum intensity of illumination G shown in determine _minwith specified intensity of illumination G _sbetween time, by described minimum intensity of illumination G _minwith specified intensity of illumination G _sbetween intensity of illumination be divided into the state of the second default number, determine the size of the intensity of illumination under each state;

By probability density function substitution the 4th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under each state _g(j), described the 4th state probability of happening formula is:

$F_G\left(j\right)={\∫}_{[(j-1)/N_G](G_s-G_{\min })+G_{\min }}^{(j/N_G)(G_s-G_{\min })+G_{\min }}f\left(G\right)\mathrm{dG};$

Wherein, N _grepresent the described second default number, j=1,2,3 ..., N _g, represent that second presets the sequence number of a number state; F (G) represents the probability density function of described typical case day each intensity of illumination constantly;

When the intensity of illumination of determining described typical case day is lower than described minimum intensity of illumination G _mintime, determine that described intensity of illumination is zero;

Using this state as N _g+ 1 state, and by probability density function substitution the 5th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 1), described the 5th state probability of happening formula is:

$F_G(N_G+1)={\∫}_0^{G_{\min }}f\left(G\right)\mathrm{dG};$

When determining that the intensity of illumination of described typical case day is not less than specified intensity of illumination G _stime, determine that described intensity of illumination is described specified intensity of illumination G _s;

Using this state as N _g+ 2 states, and by probability density function substitution the 6th state probability of happening formula of described typical case day each intensity of illumination constantly, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 2), described the 6th state probability of happening formula is:

$F_G(N_G+2)={\∫}_1^{+\∞}f\left(G\right)\mathrm{dG}.$

6. method according to claim 1, is characterized in that, described foundation is pre-conditioned, the load of typical case's day in each month is carried out to state division, and calculate the size of the load under each state and corresponding probability of happening, comprising:

Load within 3 standard deviations of distance load mean value is divided into the state of the 3rd default number, determines the size of loading under each state;

Probability density function substitution the 7th state probability of happening formula by described typical case day each load constantly, calculates the probability of happening F loading under each state _l(z), described the 7th state probability of happening formula is:

$F_L\left(z\right)={\∫}_{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·(z-1)/N_L}^{{\mathrm{\μ}}_L-{3\mathrm{\σ}}_L+{6\mathrm{\σ}}_L\·z/N_L}f\left(P_L\right){\mathrm{dP}}_L;$

Wherein, N _lrepresent the described the 3rd default number; Z=1,2,3 ..., N _l; f(P _l) represent the probability density function of described typical case day each wind speed constantly, μ _lrepresent described typical case's day each mean value of constantly loading, σ _lrepresent described typical case's day each standard deviation of constantly loading.

7. according to the method described in claim 1-6 any one, it is characterized in that, described size and corresponding probability of happening according to wind speed, intensity of illumination and load under preset algorithm and all states of described typical case day, to the parameter of each optimized variable in the described parent population simulation calculation that iterates, determine the allocation optimum of described self microgrid, comprising:

According to preset algorithm, each individuality in described parent population is carried out to simulation calculating, determine each individual corresponding target function value in described parent population;

According to described target function, described parent population is carried out to quick non-dominated Sorting;

By the crossover and mutation computing to described parent population, determine the progeny population corresponding to described parent population;

Described parent population and described progeny population are merged to the population obtaining and carry out quick non-dominated Sorting, and therefrom select the optimum individuality of default population scale to form new parent population;

Judge whether current iteration number of times reaches default maximum iteration time;

If so, determine that Output rusults is the allocation optimum of described self microgrid;

If not, return describedly according to preset algorithm, each individuality in described parent population is carried out to simulation calculating, determine that in described parent population, each individual corresponding target function value step continues to carry out.

8. self microgrid is distributed a device rationally, it is characterized in that, described device comprises:

The first determination module, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, the corresponding probability density function that in definite described historical year, wind speed, intensity of illumination and the load in each every day in month in each moment meet respectively;

First builds module, for the corresponding probability density function meeting according to described wind speed, intensity of illumination and load, builds the typical case day in each month in microgrid engineering location 1 year;

The first computing module, for according to pre-conditioned, carries out state division to wind speed, intensity of illumination and the load of typical case's day in each month, and calculates size and the corresponding probability of happening of wind speed, intensity of illumination and load under each state;

The second determination module, be used for according to the facility information of obtained described self microgrid, determine the multiple-objection optimization configuration information of described self microgrid, wherein, described multiple-objection optimization configuration information comprises: optimized variable, optimization aim information, and the operation constraint information of described self microgrid and operation policy information;

The first generation module, for the random parent population that generates default population scale, the random value that in described parent population, each individuality comprises all described optimized variables;

The first simulation algorithm model, size and corresponding probability of happening for wind speed, intensity of illumination and load according under preset algorithm and all states of described typical case day, to the parameter of each optimized variable in the described parent population simulation calculation that iterates, determine the optimal solution set that described self microgrid is distributed rationally.

9. device according to claim 8, is characterized in that, when the equipment of described self microgrid comprises: when blower fan, photovoltaic battery panel, batteries to store energy equipment and diesel engine generator, described the second determination module comprises:

First determines subelement, for the peak load short of electricity probability allowing according to predetermined system, determines the power-balance constraint information of self microgrid;

Second determines subelement, for according to the unit type of described diesel generator, technical characteristic and economic performance, determines the units limits information of described diesel generator;

The 3rd determines subelement, for according to the unit type of described batteries to store energy equipment, technical characteristic and economic performance, determines the state-of-charge constraint information of described batteries to store energy equipment and discharges and recharges power constraint information.

10. device according to claim 8, is characterized in that, shown in the first determination module comprise:

The first computation subunit, for utilizing the related data of obtained self microgrid historical year interior wind speed, intensity of illumination and load, calculates mean value and the standard deviation of each every day in month of each moment wind speed, intensity of illumination and load;

The 4th determines subelement, for by described each every day in month each mean value of wind speed and computing formula of the default wind speed probability density function parameter of standard deviation substitution constantly, determines the probability density function of described wind speed;

The 5th determines subelement, for by described each every day in month each mean value of intensity of illumination and computing formula of the default intensity of illumination probability density function parameter of standard deviation substitution constantly, determines the probability density function of described intensity of illumination;

The 6th determines subelement, for each is loaded constantly by described each every day in month mean value and the computing formula of the default load of standard deviation substitution probability density function parameter, determines the probability density function of described load.