CN103986194B - A kind of self microgrid Optimal Configuration Method and device - Google Patents

Abstract

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 typical day in each month in the Construction of probability model of intensity of illumination and load 1 year, then utilize multistate system theoretical, by the wind speed of typical case's day, intensity of illumination and load carry out multimode division, thus by continuous print nondeterministic statement according to and probability distribution rule be transformed into multiple discrete Qualitative state really and process, thus can exert oneself by direct modeling blowing machine, photovoltaic cell is exerted oneself and load randomness feature, what reduce Optimal Allocation Model solves difficulty, the self micro-grid system obtained is configured more reasonable, scene is exerted oneself and the robustness of load fluctuation stronger.

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 to a kind of self microgrid Optimal Configuration Method and device in particular.

Background technology

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

Wherein, microgrid is the system unit be made up of various distributed power source, load, energy-storage system and control device etc., its be one can teaching display stand control, the autonomous system of protect and manage, usually the microgrid be not connected with bulk power grid is called self microgrid.

In actual applications, in order to make the operating state of this self microgrid best, usually all configuration can be optimized to it, find through research, 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 history year (namely 8760 hours), adopts intelligent algorithm such as genetic algorithm, particle cluster algorithm etc. to solve its Optimal Allocation Model.Because regenerative resource is as the uncertainty of wind energy and solar energy etc., there is some difference compared with the data in obtained history year to cause the wind speed in each moment during self microgrid actual motion, intensity of illumination and load data, and the system configuration that this difference may cause the optimization of existing self microgrid Optimal Configuration Method to draw is under actual motion condition and non-optimal.

Summary of the invention

In view of this, the invention provides a kind of self microgrid Optimal Configuration Method and device, solve the technical problem effectively cannot considering wind speed, intensity of illumination and negative rules when existing self microgrid Optimal Configuration Method is optimized configuration.

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

A kind of self microgrid Optimal Configuration Method, described method comprises:

In the self microgrid history year that utilization obtains, the related data of wind speed, intensity of illumination and load, determines the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively;

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

According to pre-conditioned, state demarcation is carried out to the wind speed of the typical day in each month, intensity of illumination and load, and the size of wind speed, intensity of illumination and load calculated under each state and the probability of happening of correspondence;

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 reserve information;

Stochastic generation presets the parent population of population scale, and in described parent population, each individuality comprises the random value of all described optimized variables;

According to the size of wind speed, intensity of illumination and load under preset algorithm and all states of described typical case's day and the probability of happening of correspondence, each individuality in described parent population is iterated simulation calculation, determines 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, the described facility information according to obtained described self microgrid, determine that the operation constraint information of described self microgrid comprises:

According to the peak load short of electricity probability obtaining the permission of predetermined system, 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 state-of-charge constraint information and the charge-discharge electric power constraint information of described batteries to store energy equipment.

Preferably, the related data of wind speed, intensity of illumination and load in the self microgrid history year that described utilization obtains, determine the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively, comprising:

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

By described each every day in month each moment wind speed mean value and standard deviation substitute into the computing formula presetting wind speed probability density function parameter, determine the probability density function of described wind speed;

By described each every day in month each moment intensity of illumination mean value and standard deviation substitute into the computing formula presetting intensity of illumination probability density function parameter, determine the probability density function of described intensity of illumination;

By described each every day in month each moment load mean value and standard deviation substitute into the computing formula presetting load probability density function parameter, determine the probability density function of described load.

Preferably, in the history year obtained, 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, then according to pre-conditioned, state demarcation is carried out to the wind speed of the typical day in each month, determines size and the probability of happening of the wind speed under each state, comprising:

When determining that the wind speed of described typical case's 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 predetermined number, determine the size of the wind speed under each state;

The probability density function of wind speed in described typical case's day in each moment is substituted into the first state probability of happening formula, calculates the probability of happening F of wind speed under each state _w(i), described 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 described first predetermined number; I=1,2,3 ..., N _w; F (v) represents the probability density function of the wind speed in described typical case's day in each moment;

When determining that the wind speed of described typical case's 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 the probability density function of the wind speed in described typical case's day in each moment is substituted into the second state probability of happening formula, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1), wherein, described 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 determining that the wind speed of described typical case's 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 the probability density function of the wind speed in described typical case's day in each moment is substituted into third state probability of happening formula, calculate N _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2), wherein, described second state probability of happening formula is:

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

Preferably, the related data of the intensity of illumination obtained comprises: the minimum intensity of illumination G of the photovoltaic cell work of described self microgrid _minwith specified intensity of illumination G _s, then according to pre-conditioned, state demarcation is carried out to the intensity of illumination of the typical day in each month, determines size and the probability of happening of the intensity of illumination under each state, comprising:

When determining that the intensity of illumination of shown typical case's day is between described 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 predetermined number, determine the size of the intensity of illumination under each state;

The probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 4th state probability of happening formula, calculates the probability of happening F of intensity of illumination under each state _g(j), described 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 described second predetermined number, j=1,2,3 ..., N _g, represent the sequence number of the second predetermined number state; F (G) represents the probability density function of the intensity of illumination in described typical case's day in each moment;

When determining that the intensity of illumination of described typical case's 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 the probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 5th state probability of happening formula, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 1), described 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's 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 the probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 6th state probability of happening formula, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 2), described 6th state probability of happening formula is:

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

Preferably, described according to pre-conditioned, state demarcation is carried out to the load of the typical day in each month, and calculates the size of the load under each state and corresponding probability of happening, comprising:

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

The probability density function of the load in described typical case's day in each moment is substituted into the 7th state probability of happening formula, calculates the probability of happening F of load under each state _l(z), described 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 described 3rd predetermined number; Z=1,2,3 ..., N _l; F (P _l) represent the probability density function of wind speed in described typical case's day in each moment, μ _lrepresent the mean value of described typical case's each moment load of day, σ _lrepresent the standard deviation of described typical case's each moment load of day.

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

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

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

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

The population obtained is merged to described parent population and described progeny population and carries out quick non-dominated ranking, and therefrom select the individuality of the optimum presetting population scale to form new parent population;

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

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

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

A kind of self microgrid distributes device rationally, and described device comprises:

First determination module, for utilizing the related data of wind speed, intensity of illumination and load in obtained self microgrid history year, determine the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively;

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

First computing module, for according to pre-conditioned, carries out state demarcation to the wind speed of the typical day in each month, intensity of illumination and load, and the size of wind speed, intensity of illumination and load calculated under each state and the probability of happening of correspondence;

Second determination module, for the facility information according to 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 reserve information;

First generation module, presets the parent population of population scale for stochastic generation, in described parent population, each individuality comprises the random value of all described optimized variables;

First simulation algorithm model, for the size of wind speed, intensity of illumination and load under foundation preset algorithm and all states of described typical case's day and the probability of happening of correspondence, the parameter of each optimized variable in described parent population is iterated simulation calculation, determines 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 second determination module comprises:

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

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

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

Preferably, shown first determination module comprises:

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

4th determines subelement, for by described each every day in month each moment wind speed mean value and standard deviation substitute into the computing formula presetting wind speed probability density function parameter, determine the probability density function of described wind speed;

5th determines subelement, for by described each every day in month each moment intensity of illumination mean value and standard deviation substitute into the computing formula presetting intensity of illumination probability density function parameter, determine the probability density function of described intensity of illumination;

6th determines subelement, for by described each every day in month each moment load mean value and standard deviation substitute into the computing formula presetting load probability density function parameter, determine the probability density function of described load.

Known via above-mentioned technical scheme, compared with prior art, present disclosure 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 typical day in each month in the Construction of probability model of intensity of illumination and load microgrid location 1 year, and by utilizing multistate system theoretical, by the wind speed of typical case's day, intensity of illumination and load carry out multimode division, thus by continuous print nondeterministic statement according to and probability distribution rule be transformed into multiple discrete Qualitative state really and process, thus can exert oneself by direct modeling blowing machine, photovoltaic cell is exerted oneself and load randomness feature, what reduce Optimal Allocation Model solves difficulty, ensure that the self micro-grid system configuration drawn is more reasonable, scene is exerted oneself and the robustness of load fluctuation stronger.

Accompanying drawing explanation

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

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 distributes device rationally.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

In actual applications, self microgrid needs are according to data such as the actual working characteristics of various assembly 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 each part of this self micro-grid system, and system capacity management strategy parameter, namely configuration is optimized to this self microgrid, makes it be operated in as far as possible ideally.

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, set up and distribute required Mathematical Modeling 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 the wind speed in a certain year in this self micro-grid system location past, intensity of illumination and load data, bring default intelligent algorithm into (as genetic algorithm, particle cluster algorithm etc.) solve this Optimal Allocation Model, thus obtain the allocation optimum of this self micro-grid system.

But, find after deliberation, because regenerative resource is as the uncertainty of wind energy, solar energy and power load etc., there is some difference compared with the data in obtained history year to cause the wind speed in each moment during self microgrid actual motion, intensity of illumination and load data, and the system configuration that this difference may cause the optimization of existing self microgrid Optimal Configuration Method to draw is under actual motion condition and non-optimal.

In order to solve the above-mentioned technical problem existed 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 typical day in each month in the Construction of probability model of intensity of illumination and load self microgrid location 1 year, and by utilizing multistate system theoretical, by the wind speed of typical case's day, intensity of illumination and load carry out multimode division, thus by continuous print nondeterministic statement according to and probability distribution rule be transformed into multiple discrete Qualitative state really and process, thus can exert oneself by direct modeling blowing machine, photovoltaic cell is exerted oneself and load randomness feature, what reduce Optimal Allocation Model solves difficulty, ensure that the self micro-grid system configuration drawn is more reasonable, scene is exerted oneself and the robustness of load fluctuation stronger.

Embodiment one:

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: the related data of wind speed, intensity of illumination and load in the self microgrid history year that utilization obtains, determines the probability density function of the correspondence that the wind speed in each every day in month in each moment in this history year, intensity of illumination and load meet respectively.

The embodiment of the present invention, based on the uncertainty of the wind speed of self microgrid, intensity of illumination and power load, sets up corresponding probabilistic model respectively, 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 history year; σ _wrepresent the standard deviation of the wind speed in each every day in month in each moment in history year; Γ () is gamma (Gamma) function.

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

Wherein, for solving of above-mentioned form parameter and scale parameter, cumulative distribution function matching Weibull curve (i.e. least square method) can also be passed through, or estimate with mean wind speed and maximum wind velocity, concrete solution procedure can with reference to the method for solving of existing Weibull distribution parameters (i.e. 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 section of interior self microgrid of time (as one hour or several hours) meets beta (Beta) distribution, then 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 the actual intensity of illumination in certain period and maximum intensity of illumination (i.e. specified intensity of illumination) respectively; α and β is the form parameter of Beta distribution; μ _gand σ _grepresent the illumination mean value in this period and standard deviation respectively; Γ () is gamma function.

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

Three, the probabilistic model of load

Find after deliberation, the fluctuation of the load of self microgrid is similar to Normal Distribution, and thus, 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 _lrepresent mean value and the standard deviation of the load in each every day in month in each moment in this history year respectively; 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 history year, first will according to this obtained self microgrid certain in history year internal loading related data, obtain mean value and the standard deviation of the load in each every day in month in each moment in this history year accordingly, substituted into and preset load new probability formula and formula (7), determined the probability density function of load.

In sum, the solution procedure of the probabilistic model of wind speed, intensity of illumination and load, all for time step with 1h (unit: hour), first obtain the related data of the wind speed of certain known history year of this self microgrid and 8760h, intensity of illumination and load, calculate mean value and the standard deviation of wind speed per hour each every day in month interior in this history year, intensity of illumination and load more accordingly, substitute into the computing formula of corresponding probability density function parameter, thus determine the probability density function of wind speed, intensity of illumination and load.

Step S12: the probability density function of the correspondence met according to described wind speed, intensity of illumination and load, builds the typical 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, thus, at the wind speed of existing self microgrid Optimal Configuration Method based on certain history year, the related data of intensity of illumination and load, when configuration is optimized to this self microgrid, will inevitably be affected it and distribute effect rationally, in order to solve this problem, the embodiment of the present invention is by said method determination wind speed, the probabilistic model of intensity of illumination and load, and then construct the typical day representing each month in this history year accordingly, afterwards, only need based on the wind speed in this typical case's day in each moment, the related data of intensity of illumination and load, this self microgrid is optimized and configures, for the distributing rationally of self microgrid of current actual motion has established reliable basis.

Step S13: according to pre-conditioned, carries out state demarcation to the wind speed of the typical day in each month, intensity of illumination and load, determines the size of the wind speed under each state, intensity of illumination and load and the probability of happening of correspondence.

In actual applications, distribution trend due to the wind speed in each month, intensity of illumination and load is not all not etc., the actual conditions for different typical case's day are needed to carry out different disposal, and because wind speed directly determines the exerting oneself of the blower fan of this self microgrid, intensity of illumination directly determines exerting oneself of the photovoltaic cell of this self microgrid, so, the embodiment of the present invention carries out multimode modeling by the wind speed of the typical day to each month, intensity of illumination, carry out the multimode modeling of blower fan being 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 determining that the wind speed of typical case's day is between this incision wind speed v _ciwith this rated wind speed v _crbetween time, wind speed v will be cut _ciwith rated wind speed v _crbetween air speed value be divided into the state of the first predetermined number, determine the size of the wind speed under each state.Now, if the first predetermined number is designated as N _w, the discretization step-length of the wind speed divided is then (v _cr-v _ci)/N _w, so, 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, wind speed probability density function f (v) (i.e. formula (1)) in this typical case's day in each moment is substituted into the first state probability of happening formula, 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 determining that the wind speed of typical case's day is lower than incision wind speed v _cior higher than cut-out wind speed v _cotime, because the output of now blower fan is 0, then the wind speed under this state can be thought and equals zero, if this state is designated as N _w+ 1 state, now, can substitute into the second state probability of happening formula by probability density function f (v) of the wind speed in 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 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 determining that the wind speed of typical case's day is between rated wind speed v _crwith cut-out wind speed v _cobetween time, blower fan will export with rated power, and the wind speed so under this state can be thought and equals rated wind speed v _crif this state is designated as N _w+ 2 states, so substitute into third state probability of happening formula by probability density function f (v) of the wind speed in typical case's day in each moment, can calculate N _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, scope belonging to concrete according to the wind speed of typical case's day, carries out different state demarcation, then adopts corresponding state probability of happening formula, calculates the probability of happening of wind speed under each state, calls in order to subsequent calculations.

Two, the multimode modeling of intensity of illumination

In embodiments of the present invention, the related data of the intensity of illumination in self microgrid history year that 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, the state demarcation so for typical case's in a few days intensity of illumination in each moment can be as follows:

1), when the intensity of illumination of typical case's day is 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 predetermined number, determine the size of the intensity of illumination under each state.

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

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

The probability density function F (G) (i.e. formula (4)) of the intensity of illumination in this typical case's day in each moment is substituted into the 4th state probability of happening formula, calculates 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 determining that the intensity of illumination of 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, and the intensity of illumination so under this state also can be thought and equals zero, and if this state is designated as N _g+ 1 state, substitutes into the 5th state probability of happening formula by the probability density function f (G) 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's day is not less than specified intensity of illumination G _stime, now, because photovoltaic cell exports with rated power, then the intensity of illumination under this state equals specified intensity of illumination G _sif this state is designated as N _g+ 2 states, so substitute into the 6th state probability of happening formula by the probability density function f (G) 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 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

Because the fluctuation of load is similar to Normal Distribution, the scope of numeric distribution within distance average 3 standard deviations of about 99.3%, therefore, the embodiment of the present invention can suppose this typical case's day in each moment load P _lt () is distributed in apart from this moment load average value mu _l(t) and 3 standard deviation sigma _lscope within (t), i.e. P _l(t) ∈ [μ _l(t)-3 σ _l(t), μ _l(t)+3 σ _l(t)] (16)

By the state that the load sharing in moment each within the scope of this is the 3rd predetermined number, the mean value of this moment load, standard deviation and the 3rd predetermined number are substituted into P _l(z)=μ _l-3 σ _l+ 6 σ _l(z-1/2)/N _l, calculate the size P of load under each state _li (), afterwards, by the probability density function f (P of this typical case in a few days load in each moment _l) substitute into the 7th state probability of happening formula, thus the probability of happening of load under obtaining 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, embodiment of the present invention employing multistate system theory is exerted oneself to typical case's each moment blower fan of day, photovoltaic cell is exerted oneself and load carries out multimode modeling, thus change continuous print nondeterministic statement into multiple discrete Qualitative state really according to the probability distribution rule of its correspondence and carry out subsequent treatment, avoid and set up stochastic model, reduce the difficulty of distributing rationally.

Step S14: according to the facility information of obtained self microgrid, determines 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 reserve 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 characteristic of the equipment of different model and economy specific be also different, so, in the distributing rationally of self microgrid, the variable that needs are optimized is the quantity of the equipment of certain model, as the diesel engine generator of 2 100kW (unit: kilowatt), instead of the rated capacity of equipment, otherwise, as the diesel engine generator economic parameters with 100kW is optimized, obtaining result is need diesel engine generator rated capacity 164kW, obviously, there is not the diesel engine generator of 164kW, so, when after the model determining 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 constructed by the embodiment of the present invention 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, then this optimized variable X can be expressed as: X=[N _wTn _pVn _bATn _dE].

It should be noted that, for the requirement (i.e. 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 being optimized configuration to self microgrid, according to different optimization demands, determines the optimization aim of this self microgrid, thus the target information that is optimized.In actual applications, optimization aim can comprise economy objectives and feature of environmental protection target etc. usually.

In embodiments of the present invention, select total ready-made (totalnetpresentcost only in the life cycle management of self microgrid, being called for short NPC) desired value is as economy objectives, comprise self microgrid life cycle (namely k) in the desired value of all costs and benefits, discount rate now can be utilized (namely r) future cash flow to be converted to present worth.Wherein, the desired value C (k) of this part is become 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 _m(k) and fuel cost desired value C _f(k); The desired value I (k) of income section 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)

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

And for above-mentioned feature of environmental protection target, along with China is to the raising of the attention degree of environmental issue, the embodiment of the present invention can by carbon dioxide (i.e. CO ₂) discharge capacity desired value as another target optimized.Various in practical application, in above-mentioned each equipment of self microgrid, can CO be produced ₂power supply only have diesel engine generator, therefore, the present invention can 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

State on the invention in embodiment, with above-mentioned optimization aim for instructing, the operation constraint information for determined self microgrid can comprise power-balance constraint information, the units limits information of diesel generator and the state-of-charge constraint information of batteries to store energy equipment and charge-discharge electric power constraint information facility constraints information.

Wherein, for the determination of the units limits information of this self microgrid diesel engine generator, due to when the operate power of diesel engine generator is lower than certain value, its unit power fuel consumption can be larger, and can reduce the useful life of this diesel engine generator, therefore, in the practical application of the embodiment of the present invention, before being come into operation by diesel engine generator, and after determining the unit type of diesel engine generator used, technical characteristic and economic performance, first the minimum operate power P of this diesel engine generator to be set _{g_min}, thus 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, distribute rationally in process at self microgrid, because the useful life of batteries to store energy equipment (BESS) can by the impact of the factors such as its discharge and recharge degree of depth, discharge and recharge number of times and charge-discharge electric power size, and, unordered use also can shorten the useful life of storage battery, thus affects the security of operation of whole self microgrid, and increases production cost.Therefore, in order to ensure the useful life of this storage battery, the use of the embodiment of the present invention to storage battery is provided with certain constraint, concrete:

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

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

Thus, the charged constraint information of device determined batteries to store energy equipment comprises: maximum state-of-charge SOC _maxwith minimum state-of-charge SOC _min.

2), the charge-discharge electric power constraint of BESS

In embodiments of the present invention, in order to avoid BESS charges under small-power, and reduce the discharge and recharge number of times of BESS, extend its useful life, need the minimum charge power P that batteries to store energy equipment allows at it _{bATch_min}on charge, in addition, go back the following condition of demand fulfillment:

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 the maximum charge power of BESS permission and minimum charge power, belong to the charge-discharge electric power constraint information of this batteries to store energy equipment.

In addition, in self microgrid is distributed rationally, also need to consider power-balance constraint, the typical in a few days power-supply device (i.e. above-mentioned blower fan, photovoltaic battery panel, storage battery and diesel engine generator) in each moment is exerted oneself and can meet load needs, ensure self microgrid reliable power supply, the embodiment of the present invention can use year load short of electricity probability (lossofpowersupplyprobability, 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 _losst () represents the desired value of t load vacancy, P _lt () represents the desired value of the load of t demand fulfillment; Hop count when N is emulation total, in the embodiment of the present invention, owing to being take 1h as time step, and carries out simulation calculation to the typical day that certain 12 month of history year is corresponding, thus, and 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, be specifically as follows:

f _LPSP≤f _Lmax(25)

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

But, self microgrid distribute the time stimulatiom often needing to carry out multiple period rationally, meeting the demand of day part load for reasonably coordinating exerting oneself of each power supply in each emulation period micro-grid system, often needing a set of rational operation reserve of design to exert oneself to coordinate each power supply.Thus, following operation reserve can be followed when the embodiment of the present invention emulates system: as blower fan generated output P _wT(t) and photovoltaic cell capable of generating power power P _pVt () sum is not less than load power demand P _ltime (t), need blower fan and photovoltaic battery panel to exert oneself, and diesel engine generator and batteries to store energy equipment do not work; And when excess power is greater than P _{bATch_min}time, then this blower fan and photovoltaic cell can the chargings of accumulators energy storage device, and 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 with P _{bATch_max}charge, remainder is consumed by controllable load or directly abandons; 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 (i.e. excess power) is by controllable load consumption or directly abandon.

As blower fan generated output P _wT(t) and photovoltaic cell capable of generating power power P _pVt () sum is less than load power demand P _lt time (), if batteries to store energy equipment can meet workload demand, then power shortage is preferentially discharged by this batteries to store energy equipment provides, and diesel engine generator does not work; If wind-light storage cannot meet workload demand, then this batteries to store energy equipment will not discharge, and open diesel engine generator to meet workload demand; If make this diesel engine generator work still cannot meet workload demand under nominal power, then this batteries to store energy equipment is discharged; If this diesel engine generator is operated in rated power, makes batteries to store energy equipment discharge simultaneously and all cannot meet workload demand, now occur electricity shortage, cut-off parts insignificant load can be allowed.

Therefore, the embodiment of the present invention according to related datas such as the wind speed of obtained self microgrid historical years, intensity of illumination and loads, with above-mentioned information of distributing rationally for instructing, can determine the quantity of optimum various power-supply device.

Step S15: stochastic generation presets the parent population of population scale, in this parent population, each individuality comprises the random value of all optimized variables.

Before execution step S15, the size of each moment wind speed, intensity of illumination and load under each state and the probability of happening of correspondence thereof of the typical case's day above-mentioned each month calculated need be read, and hop count, population scale N and maximum iteration time t during the emulation of operator's setting total _maxetc. parameter.

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

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

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

In embodiments of the present invention, preset algorithm can be NAGA-II algorithm.After determining parent population P, can according to the size of wind speed, intensity of illumination and load under this algorithm and each state of the typical day calculated and the probability of happening of correspondence, simulation calculation is carried out to each individuality in parent population, determine each individual corresponding optimization target values, i.e. above-mentioned economy objectives value and feature of environmental protection desired value, then quick non-dominated ranking is carried out, parent population P is intersected, mutation operation, obtain corresponding progeny population Q, afterwards, parent population P is merged with corresponding progeny population Q, obtains a new population R _t, now, by the population R new to this _tafter carrying out quick non-dominated ranking, select the individuality of wherein optimum default population scale number, form new parent population P, now complete an iterative process, perform, until reach maximum iteration time t according to the circulation of this alternative manner _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 typical day in each month in certain history year of the Construction of probability model of intensity of illumination and load, make the present invention only according to the wind speed of the typical day in each month, the related data of intensity of illumination and load carries out follow-up distributing rationally, and utilize multistate system theoretical, by the wind speed of typical case's day, intensity of illumination and load carry out multimode division, thus by continuous print nondeterministic statement according to and probability distribution rule be transformed into multiple discrete Qualitative state really and process, thus can exert oneself by direct modeling blowing machine, photovoltaic cell is exerted oneself and load randomness feature, what reduce Optimal Allocation Model solves difficulty, ensure that the system configuration obtained is more reasonable, scene is exerted oneself and the robustness of load fluctuation stronger.

Embodiment two:

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

First determination module S21, for utilizing the related data of wind speed, intensity of illumination and load in obtained self microgrid history year, determine the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively.

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

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

4th determines subelement, for by described each every day in month each moment wind speed mean value and standard deviation substitute into the computing formula presetting wind speed probability density function parameter, determine the probability density function of described wind speed;

5th determines subelement, for by described each every day in month each moment intensity of illumination mean value and standard deviation substitute into the computing formula presetting intensity of illumination probability density function parameter, determine the probability density function of described intensity of illumination;

6th determines subelement, for by described each every day in month each moment load mean value and standard deviation substitute into the computing formula presetting load probability density function parameter, determine the probability density function of described load.

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

First computing module S23, for according to pre-conditioned, carries out state demarcation to the wind speed of the typical day in each month, intensity of illumination and load, and the size of wind speed, intensity of illumination and load calculated under each state and the probability of happening of correspondence.

Within obtained history 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, then the first computing module S23 can comprise:

First state demarcation subelement, for determining that the wind speed of described typical case's day is 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 predetermined number, determine the size of the wind speed under each state.

Second computation subunit, the probability density function for the wind speed by this typical case's day in each moment substitutes into the first state probability of happening formula, 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 predetermined number; I=1,2,3 ..., N _w; F (v) represents the probability density function of the wind speed in described typical case's day in each moment.

8th determines subelement, determines that the wind speed of described typical case's day is lower than described incision wind speed v for working as _cior higher than described cut-out wind speed v _cotime, determine that the wind speed under this state is zero.

3rd computation subunit, for using this state as N _w+ 1 state, and the probability density function of the wind speed in this typical case's day in each moment is substituted into the second state probability of happening formula, 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:

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

9th determines subelement, determines that the wind speed of described typical case's day is between described rated wind speed v for working as _crwith described cut-out wind speed v _cobetween time, determine that the wind speed under this state is described rated wind speed.

4th computation subunit, for using this state as N _w+ 2 states, and the probability density function of the wind speed in described typical case's day in each moment is substituted into third state probability of happening formula, 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, when the related data of 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, determines that the intensity of illumination of shown typical case's day is between described minimum intensity of illumination G for working as _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 predetermined number, determine the size of the intensity of illumination under each state.

5th computation subunit, the probability density function for the intensity of illumination by typical case's day in each moment substitutes into the 4th state probability of happening formula, 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 predetermined number, j=1,2,3 ..., N _g, represent the sequence number of the second predetermined number state; F (G) represents the probability density function of the intensity of illumination in typical case's day in each moment;

Tenth determines subelement, determines that the intensity of illumination of this typical case day is lower than described minimum intensity of illumination G for working as _mintime, determine that this intensity of illumination is zero.

6th computation subunit, for using this state as N _g+ 1 state, and the probability density function of the intensity of illumination in this typical case's day in each moment is substituted into the 5th state probability of happening formula, 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:

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

11 determines subelement, determines that the intensity of illumination of this typical case day is not less than specified intensity of illumination G for working as _stime, determine that this intensity of illumination is specified intensity of illumination G _s.

7th computation subunit, for using this state as N _g+ 2 states, and the probability density function of the intensity of illumination in typical case's day in each moment is substituted into the 6th state probability of happening formula, 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:

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

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

8th computation subunit: the probability density function for the load by typical case's day in each moment substitutes into the 7th state probability of happening formula, calculates the probability of happening F of load 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 described 3rd predetermined number; Z=1,2,3 ..., N _l; F (P _l) represent the probability density function of wind speed in described typical case's day in each moment, μ _lrepresent the mean value of described typical case's each moment load of day, σ _lrepresent the standard deviation of described typical case's each moment load of day.

Second determination module S24, for the facility information according to 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 reserve 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 second determination module S24 comprises:

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

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

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

First generation module S25, for presetting the parent population of population scale by described optimized variable stochastic generation.

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.

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

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

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

First sequence subelement, for according to described target function, carries out quick non-dominated ranking to described parent population;

First operator unit, for by the crossover and mutation computing to described parent population, determines and the progeny population that described parent population corresponds to;

First chooser unit, carries out quick non-dominated ranking for merging to described parent population and described progeny population the population obtained, and therefrom selects the individuality of the optimum presetting population scale to form new parent population;

First judgment sub-unit, for judging whether current iteration number of times reaches default maximum iteration time, if so, then 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 typical day in each month in certain history year of the Construction of probability model of intensity of illumination and load, make the present invention only according to the wind speed of the typical day in each month, the related data of intensity of illumination and load carries out follow-up distributing rationally, and by utilizing multistate system theoretical, by the wind speed of typical case's day, intensity of illumination and load carry out multimode division, thus by continuous print nondeterministic statement according to and probability distribution rule be transformed into multiple discrete Qualitative state really and process, thus can exert oneself by direct modeling blowing machine, photovoltaic cell is exerted oneself and load randomness feature, what reduce Optimal Allocation Model solves difficulty, ensure that the system configuration obtained is more reasonable, scene is exerted oneself and the robustness of load fluctuation stronger.

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

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

To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments 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:

The related data of wind speed, intensity of illumination and load in the self microgrid history year that utilization obtains, determine the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively, wherein, in described history 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 related data of described 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 _s;

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

According to pre-conditioned, state demarcation is carried out to the wind speed of the typical day in each month, intensity of illumination and load, and the size of wind speed, intensity of illumination and load calculated under each state and the probability of happening of correspondence;

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 reserve information;

Stochastic generation presets the parent population of population scale, and in described parent population, each individuality comprises the random value of all described optimized variables;

According to the size of wind speed, intensity of illumination and load under preset algorithm and all states of described typical case's day and the probability of happening of correspondence, each individuality in described parent population is iterated simulation calculation, determines 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, the described facility information according to 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 the predetermined system obtained allows, 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 state-of-charge constraint information and the charge-discharge electric power constraint information of described batteries to store energy equipment.

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

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

By described each every day in month each moment wind speed mean value and standard deviation substitute into the computing formula presetting wind speed probability density function parameter, determine the probability density function of described wind speed;

By described each every day in month each moment intensity of illumination mean value and standard deviation substitute into the computing formula presetting intensity of illumination probability density function parameter, determine the probability density function of described intensity of illumination;

By described each every day in month each moment load mean value and standard deviation substitute into the computing formula presetting load probability density function parameter, determine the probability density function of described load.

4. method according to claim 1, is characterized in that, described according to pre-conditioned, carries out state demarcation to the wind speed of the typical day in each month, determines size and the probability of happening of the wind speed under each state, comprising:

When determining that the wind speed of described typical case's 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 predetermined number, determine the size of the wind speed under each state;

The probability density function of wind speed in described typical case's day in each moment is substituted into the first state probability of happening formula, calculates the probability of happening F of wind speed under each state _w(i), described first state probability of happening formula is:

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

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

When determining that the wind speed of described typical case's 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 the probability density function of the wind speed in described typical case's day in each moment is substituted into the second state probability of happening formula, calculate N _wthe probability of happening F of wind speed under+1 state _w(N _w+ 1), wherein, described second state probability of happening formula is:

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

When determining that the wind speed of described typical case's 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 the probability density function of the wind speed in described typical case's day in each moment is substituted into third state probability of happening formula, calculate N _wthe probability of happening F of wind speed under+2 states _w(N _w+ 2), wherein, described second state probability of happening formula is:

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

5. method according to claim 1, is characterized in that, described according to pre-conditioned, carries out state demarcation to the intensity of illumination of the typical day in each month, determines size and the probability of happening of the intensity of illumination under each state, comprising:

When determining that the intensity of illumination of shown typical case's day is between described 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 predetermined number, determine the size of the intensity of illumination under each state;

The probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 4th state probability of happening formula, calculates the probability of happening F of intensity of illumination under each state _g(j), described 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)dG;$

Wherein, N _grepresent described second predetermined number, j=1,2,3 ..., N _g, represent the sequence number of the second predetermined number state; F (G) represents the probability density function of the intensity of illumination in described typical case's day in each moment;

When determining that the intensity of illumination of described typical case's 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 the probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 5th state probability of happening formula, calculate the probability of happening F of intensity of illumination under this state _g(N _g+ 1), described 5th state probability of happening formula is:

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

When determining that the intensity of illumination of described typical case's 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 the probability density function of the intensity of illumination in described typical case's day in each moment is substituted into the 6th state probability of happening formula, calculate the generation of intensity of illumination under this state general

Rate F _g(N _g+ 2), described 6th state probability of happening formula is:

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

6. method according to claim 1, is characterized in that, described according to pre-conditioned, carries out state demarcation to the load of the typical day in each month, and calculates the size of the load under each state and corresponding probability of happening, comprising:

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

The probability density function of the load in described typical case's day in each moment is substituted into the 7th state probability of happening formula, calculates the probability of happening F of load under each state _l(z), described 7th state probability of happening formula is:

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

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

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

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

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

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

The population obtained is merged to described parent population and described progeny population and carries out quick non-dominated ranking, and therefrom select the individuality of the optimum presetting population scale to form new parent population;

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

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

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

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

First determination module, for utilizing the related data of wind speed, intensity of illumination and load in obtained self microgrid history year, determine the probability density function of the correspondence that the wind speed in each every day in month in each moment in described history year, intensity of illumination and load meet respectively, wherein, in described history 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 related data of described 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 _s;

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

First computing module, for according to pre-conditioned, carries out state demarcation to the wind speed of the typical day in each month, intensity of illumination and load, and the size of wind speed, intensity of illumination and load calculated under each state and the probability of happening of correspondence;

Second determination module, for the facility information according to 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 reserve information;

First generation module, presets the parent population of population scale for stochastic generation, in described parent population, each individuality comprises the random value of all described optimized variables;

First simulation algorithm model, for the size of wind speed, intensity of illumination and load under foundation preset algorithm and all states of described typical case's day and the probability of happening of correspondence, the parameter of each optimized variable in described parent population is iterated simulation calculation, determines 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 second determination module comprises:

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

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

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

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

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

4th determines subelement, for by described each every day in month each moment wind speed mean value and standard deviation substitute into the computing formula presetting wind speed probability density function parameter, determine the probability density function of described wind speed;

5th determines subelement, for by described each every day in month each moment intensity of illumination mean value and standard deviation substitute into the computing formula presetting intensity of illumination probability density function parameter, determine the probability density function of described intensity of illumination;

6th determines subelement, for by described each every day in month each moment load mean value and standard deviation substitute into the computing formula presetting load probability density function parameter, determine the probability density function of described load.