CN102780219A - Method for discriminating wind power digestion capability from multiple dimensions based on wind power operation simulation - Google Patents

Abstract

The invention discloses a method for discriminating wind power digestion capability from multiple dimensions based on wind power operation simulation, belonging to the field of electric power system operation and control. The method includes the steps of: simulating the timing sequence outputs of a wind power station by making use of the technology of simulating the operations of multiple wind power stations according to the measured wind data; and according to the simulated timing sequence outputs of the wind power station and the discrimination cluster of annual wind power digestion capacity, discriminating the wind power digestion capacity from multiple dimensions by taking the peak shaving ability, the frequency regulating ability, the load tracking ability, the rapid reserve ability and the network transmission ability as the constraint conditions. The method can help the dispatching, operating and control workers of an electric power system precisely predict the wind power volume which can be accepted by the electric power system in the future from multiple dimensions, and then help the workers set out a wind power digestion plan so to realize high-efficiency utilization of the wind power. The method exhibits great practical significance and has a good application prospect.

Description

Based on the various dimensions wind-powered electricity generation of the wind-powered electricity generation operation simulation ability method of discrimination of dissolving

Technical field

The invention belongs to power system operation and control field, particularly based on the various dimensions wind-powered electricity generation of the wind-powered electricity generation operation simulation ability method of discrimination of dissolving.

Background technology

Since the eighties in last century, oil crisis, climate change, energy problem become international focus, are that the clean energy resource of representative is fast-developing with the wind energy, become important alternative energy source at a specified future date.Greatly developing regenerative resource is the important component part of China's energy development strategy.Wind power technology is ripe, is one of regenerative resource of tool business development potentiality.Usually, wind-powered electricity generation is exerted oneself and is shown the characteristic that is different from conventional power supply: randomness, fluctuation, uncertainty.These characteristics are that the safe operation of electric power system has brought severe challenge with stable control; Therefore provide scientific methods realizing the dissolve differentiation of ability of electric power system wind-powered electricity generation, the wind-powered electricity generation ability of dissolving will be as the important indicator of each function links such as the operation of electric power system, scheduling, control.

The prerequisite that the wind-powered electricity generation of electric power system is dissolved is the dissolve differentiation of ability of electric power system wind-powered electricity generation, promptly determines year, the admissible wind-powered electricity generation scale of electric power system monthly, day degree; At present a kind of wind-powered electricity generation based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving is the historical typical wind-powered electricity generation power curve to electric power system, considers that with deterministic computational methods the wind-powered electricity generation factor differentiates the wind-powered electricity generation ability of dissolving, and its key step is:

1) chooses some the typical wind-powered electricity generation power curves of history;

2) calculate the adjustable space of each confinement dimension of electric power system according to the unit regulating power;

3) can admit the wind-powered electricity generation power curve that selects according to the size judgement of adjustable space;

4) wind-powered electricity generation that multiple proportions adjustment wind-powered electricity generation power curve, the wind-powered electricity generation power curve in the time of can being admitted by electric power system just the are regarded as electric power system ability of dissolving.

This method exists not enough:

1) the wind-powered electricity generation power curve participates in the process that the wind-powered electricity generation ability of dissolving differentiates with deterministic method, does not consider randomness, fluctuation and uncertainty that wind-powered electricity generation is exerted oneself;

2) the typical wind-powered electricity generation power curve of selecting for use can not characterize whole scenes (randomness, fluctuation and probabilistic different expression form) that wind-powered electricity generation is exerted oneself;

3) fail to take into full account the influence of wind-electricity integration for each side factors such as electric power system peak regulation, frequency modulation, subsequent use, Steam Generator in Load Follow and networks;

4) comprehensively do not hold for the situation of dissolving behind the following wind-electricity integration, can not guarantee that wind-powered electricity generation moves within certain ratio of dissolving;

And from the angle of the control strategy of wind-electricity integration, the current measure of taking only relates to the control of closing down of wind-powered electricity generation unit itself, does not cooperate the start-stop with conventional rack, is limiting electric power system the dissolve controlled original paper and the controlled range of wind-powered electricity generation.Under the open state that conventional unit is confirmed, can't dissolve too much wind-powered electricity generation when exerting oneself when electric power system, closed portion wind-powered electricity generation machine consists of unique possible strategy, thereby has wasted part wind-powered electricity generation resource.

In sum; Need one to overlap more science and the comprehensive wind-powered electricity generation ability method of discrimination of dissolving; And take into account electric power system peak modulation capacity, fm capacity, factors such as marginal capacity, load-following capacity and network delivery ability fast, consider the wind-powered electricity generation power producing characteristics: the instrument that provides quick differentiation wind-powered electricity generation to dissolve ability for scheduling, operation, the control personnel of electric power system.

Disclose at present a kind of method of utilizing windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself, this method specifically may further comprise the steps:

1) obtain scale parameter c and the form parameter k that Weibull distributes according to surveying the wind data match:

(1-1) two-parameter Weibull distribution function F _{W (c, k)}(x) expression formula is following:

$F_{W(c,k)}\left(x\right)=1-\exp [-{\left(\frac{x}{c}\right)}^k],x\∈[0,+\∞)---\left(1\right)$

(1-2) the probability density function f of two-parameter Weibull distribution _{W (c, k)}(x) as follows:

$f_{W(c,k)}\left(x\right)=\frac{k}{c}{\left(\frac{x}{c}\right)}^{k-1}\exp [-{\left(\frac{x}{c}\right)}^k]---\left(2\right)$

(1-3) expression formula of mean wind speed is following:

$\stackrel{\‾}{x}=\mathrm{c\Γ}(1+1/k)---\left(3\right)$

(1-4) try to achieve form parameter k by the wind speed standard deviation sigma, expression formula is following:

$\frac{\mathrm{\σ}}{\stackrel{\‾}{x}}=\sqrt{[\mathrm{\Γ}(1+\frac{2}{k})/{\mathrm{\Γ}}^2(1+\frac{1}{k})]-1}---\left(4\right)$

Wherein, mean wind speed x is directly proportional with Weibull distribution mesoscale parameter c; Γ is a gamma function:

$\mathrm{\Γ}\left(a\right)={\∫}_0^{+\∞}{y}^{a-1}{e}^{-y}\mathrm{dy});$

2) temporal correlation of wind speed setting: according to surveying the temporal correlation characteristic quantity θ that the wind data match obtains wind speed (is the wave characteristic of wind speed; The mode that characterizes fluctuations in wind speed property is an auto-correlation function, and auto-correlation function is meant the linearly dependent coefficient of the sequence of time series and self different time displacement, and auto-correlation function is the tolerance of time series temporal correlation; The size of reflection time series fluctuation; The value of auto-correlation function increases with the time difference and decays, and time series fluctuation Shaoxing opera is strong, and the auto-correlation function decay is fast more); The auto-correlation function of wind speed is represented by negative exponential function that numerically expression formula is following:

ρ(k)=e ^-θk,θ＞0,k=1,2,3...?(5)

In the formula (5), the speed of the size of θ decision auto-correlation function decay, and then the severe of sign fluctuations in wind speed;

3) spatial coherence of wind speed setting: adjacent wind energy turbine set is because the vicinity in geographical position; The residing meteorological condition of wind energy turbine set is similar; Therefore the wind speed of the wind energy turbine set of close together spatially often has positive correlation; The wind speed correlation is main relevant with geographic distance between the wind energy turbine set: owing to receive the influence of same weather conditions, its wind speed will show stronger correlation at a distance of nearer wind-powered electricity generation section; The wind-powered electricity generation section of apart from each other, its probability that runs into same weather conditions is less, so its wind speed correlation a little less than; There are the negative exponent relation in coefficient correlation between the windy field gas velocity and the geographic distance between the wind energy turbine set, and expression formula is following:

$c=e^{-\frac{d}{M}}---\left(6\right)$

In the formula (6), c is the wind speed coefficient correlation; D is a geographic distance between the two wind-powered electricity generation sections; M is that the wind speed coefficient correlation is with the range attenuation factor;

4) be that base value obtains annual each monthly average wind series (be the Seasonal Characteristics of wind speed: because climate reasons, Various Seasonal wind energy turbine set location velocity wind levels is different, and has certain rule) with annual wind speed mean value, each monthly average wind series is designated as k _m, k _mIn element expression following:

$k_{\mathrm{mi}}=\frac{v_{\mathrm{mi}}}{{\stackrel{\‾}{v}}_y},$ i＝1,2,3,...,12 (7)

In the formula (7), k _MiBe k _mIn i element; v _MiMean wind speed for the i month in year;

Be the average of the whole year wind speed;

5) with whole day wind speed mean value be base value obtain in a few days each constantly mean wind speed sequence (be the day internal characteristic of wind speed: in a few days; Owing to the difference of wind energy turbine set location surface temperature causes that in a few days different mean wind speeds constantly are different), in a few days each constantly mean wind speed sequence be designated as k _h, k _hIn element expression following:

$k_{\mathrm{hj}}=\frac{v_{\mathrm{hj}}}{{\stackrel{\‾}{v}}_d},$ j=1,2,3,...,N ^day (8)

In the formula (8), k _HjBe k _hIn j element; v _HjBe the mean wind speed of j period in a few days;

Be the whole day mean wind speed; N ^DayBe period sum in a few days;

6) utilize windy electric field operation analogue technique to carry out wind speed simulation:

(6-1) single wind farm wind velocity simulation:

If probability density function f (x) its domain of definition (l, in u) non-negative, continuously and variance limited, its mathematic expectaion E (x)=μ, stochastic differential equation

${\mathrm{dX}}_t=-\mathrm{\θ}(X_t-\mathrm{\μ})\mathrm{dt}+\sqrt{v\left(X_t\right)}d{W}_t,$ t≥0 (9)

In the formula (9), θ>=0, W _tBe the standard Brownian movement, v (X _t) be defined in (expression formula is following for l, the nonnegative function on u):

$v\left(x\right)=\frac{2\mathrm{\θ}}{f\left(x\right)}{\∫}_l^x(\mathrm{\μ}-y)f\left(y\right)\mathrm{dy},$ x∈(l,u)(10)

Then:

Random process X is that each attitude experience (ergodic) and probability density function are f (x).

Random process X is that average returns (mean-reverting) and its auto-correlation function meets:

corr(X _s+t，X _s)=e ^-θt,s,t≥0 (11)

Utilize the time series of this method simulation wind speed; If meeting the Weibull that is respectively c and k suc as formula the scale parameter shown in (1) and the formula (2) and form parameter, wind speed distributes; Mean wind speed

is suc as formula shown in (3), then:

$v\left(x\right)=\frac{2\mathrm{\θ}}{f\left(x\right)}{\∫}_l^x(\stackrel{\‾}{x}-y)f\left(y\right)\mathrm{dy}$

$=\frac{2\mathrm{\θ}}{f\left(x\right)}(\stackrel{\‾}{x}F\left(x\right)-{\∫}_l^x\mathrm{yf}\left(y\right)\mathrm{dy})---\left(12\right)$

$=\frac{2\mathrm{\θ}}{f\left(x\right)}(\mathrm{c\Γ}(\frac{1}{k}+1)(1-\exp [-{\left(\frac{x}{c}\right)}^k\left]\right)-\mathrm{c\Γ}({\left(\frac{x}{c}\right)}^k,\frac{1}{k}+1))$

According to formula (9)-(12), single wind energy turbine set sequential wind speed can be generated by the following formula iterative computation:

${\hat{v}}_{\mathrm{it}}^{*}={\hat{v}}_{\mathrm{it}-1}^{*}+d{X}_t---\left(13\right)$

(6-2) windy field gas velocity simulation:

At first generate the relevant Brownian movement W of multidimensional _t, W _tEach dimension is the standard Brownian movement, and correlation matrix equals the wind farm wind velocity correlation matrix between each dimension; Afterwards, utilize W _tEach ties up component each wind farm wind velocity sequence of (6-1) middle method generation set by step.

(6-3) correction of wind energy turbine set simulation wind speed

The wind farm wind velocity sequence is not the completely random process; To because climate reasons; Various Seasonal wind energy turbine set location velocity wind levels is different; And has certain rule (little, summer is big), in a few days, owing to the difference of wind energy turbine set location surface temperature causes that in a few days different mean wind speeds constantly are different (big like evening like winter; Daytime is little), according to 4) and 5) wind series

that generates is at random revised:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ i＝1,2,...,12,j=1,2,...,m (14)

(6-4) wind energy turbine set is simulated the sequence of exerting oneself

If C _i(x) be wind-powered electricity generation unit output characteristic curve, expression formula is following:

$C_i\left(v\right)=\left\{\begin{array}{cc}0,& 0\&le;v<v_{\mathrm{in}},v>v_{\mathrm{out}}\\ \frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3}R,& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ R,& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(15\right)$

In the formula (15), v _In, v _RatedWith v _OutBe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit.Utilize correction back wind series

wind energy turbine set sequential power curve to generate by following formula:

$P_{\mathrm{it}}=n_{\mathrm{it}}(1-{\mathrm{\&eta;}}_i)C_i\left(v_{\mathrm{it}}^{*}\right)---\left(16\right)$

In the formula (16), η _iBe wind energy turbine set wake effect coefficient, the expression wind energy turbine set is exerted oneself because of what wake effect lost, gets 5% ~ 10% usually; n _ItCan use unit platform number for wind energy turbine set, be a stochastic variable, represents unit reliability level in the wind energy turbine set (if the unit fault is obeyed independently exponential distribution in the false wind electric field, then for arbitrary time t, wind energy turbine set can use unit platform number to obey the Bei Nuli distribution).

Windy electric field operation analogue technique can realize reduction, reproduction and simulation to output of wind electric field in the electric power system, for dissolve ability and consider that the power system dispatching of output of wind electric field and operation all have important meaning of the wind-powered electricity generation of analyzing wind farm grid-connected influence to electric power system, electric power system.

Summary of the invention

The objective of the invention is to overcome the dissolve deficiency of ability method of discrimination of existing power system wind-powered electricity generation; A kind of wind-powered electricity generation based on operation simulation ability method of discrimination of dissolving is provided; Whether the present invention can help power system dispatching, operation and control personnel annual, monthly and a few days ago with regard to the clear and definite electric power system expection wind-powered electricity generation ratio of dissolving, judge forecasting institute fast and get wind-powered electricity generation and can fully be dissolved by electric power system.

The invention discloses a kind of various dimensions wind-powered electricity generation ability method of discrimination of dissolving, it is characterized in that, comprising:, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself 1) according to surveying wind data based on wind-powered electricity generation operation simulation; 2) according to simulation wind energy turbine set sequential exert oneself, the annual wind-powered electricity generation ability of dissolving is differentiated collection and peak modulation capacity, fm capacity, load-following capacity, marginal capacity and network delivery ability are carried out various dimensions to the wind-powered electricity generation ability of dissolving and differentiated as constraints fast;

1) according to surveying wind data, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself, specifically may further comprise the steps:

1-1) obtain scale parameter c and the form parameter k that Weibull distributes according to surveying the wind data match:

(1-11) two-parameter Weibull distribution function F _{W (c, k)}(x) expression formula is following:

$F_{W(c,k)}\left(x\right)=1-\exp [-{\left(\frac{x}{c}\right)}^k],x\&Element;[0,+\&infin;)---\left(1\right)$

In the formula (1), x is a wind speed;

(1-12) the probability density function f of two-parameter Weibull distribution _{W (c, k)}(x) as follows:

$f_{W(c,k)}\left(x\right)=\frac{k}{c}{\left(\frac{x}{c}\right)}^{k-1}\exp [-{\left(\frac{x}{c}\right)}^k]---\left(2\right)$

(1-13) expression formula of mean wind speed

is following:

$\stackrel{\&OverBar;}{x}=\mathrm{c\&Gamma;}(1+1/k)---\left(3\right)$

(1-14) try to achieve form parameter k by the wind speed standard deviation sigma, expression formula is following:

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{x}}=\sqrt{[\mathrm{\&Gamma;}(1+\frac{2}{k})/{\mathrm{\&Gamma;}}^2(1+\frac{1}{k})]-1}---\left(4\right)$

Wherein, mean wind speed is directly proportional with Weibull distribution mesoscale parameter c; Γ is a gamma function:

$\mathrm{\&Gamma;}\left(a\right)={\&Integral;}_0^{+\&infin;}{y}^{a-1}{e}^{-y}\mathrm{dy});$

1-2) the temporal correlation of wind speed setting: according to surveying the temporal correlation characteristic quantity θ that the wind data match obtains wind speed, the auto-correlation function of wind speed is represented by negative exponential function that numerically expression formula is following:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

In the formula (5), the speed of the size of θ decision auto-correlation function decay, the severe of sign fluctuations in wind speed;

1-3) the spatial coherence of wind speed setting: there are the negative exponent relation in coefficient correlation between the windy field gas velocity and the geographic distance between the wind energy turbine set, and expression formula is following:

$c=e^{-\frac{d}{M}}---\left(6\right)$

In the formula (6), c is the wind speed coefficient correlation; D is a geographic distance between the two wind-powered electricity generation sections; M is that the wind speed coefficient correlation is with the range attenuation factor;

Be that base value obtains each monthly average wind series k 1-4) with annual wind speed mean value _m, k _mIn element expression following:

$k_{\mathrm{mi}}=\frac{v_{\mathrm{mi}}}{{\stackrel{\&OverBar;}{v}}_y},$ i＝1,2,3,...,12 (7)

In the formula (7), k _MiBe k _mIn i element; v _MiMean wind speed for the i month in year;

Be the average of the whole year wind speed;

Be that base value obtains each moment mean wind speed sequence k in a few days 1-5) with whole day wind speed mean value _h, k _hIn element expression following:

$k_{\mathrm{hj}}=\frac{v_{\mathrm{hj}}}{{\stackrel{\&OverBar;}{v}}_d},$ j=1,2,3,...,N ^day (8)

In the formula (8), k _HjBe k _hIn j element; v _HjBe the mean wind speed of j period in a few days;

Be the whole day mean wind speed; N ^DayBe period sum in a few days;

1-6) utilize windy electric field operation analogue technique to carry out wind speed simulation:

(1-61) single wind farm wind velocity simulation:

If meeting the Weibull that is respectively c and k suc as formula the scale parameter shown in (1) and the formula (2) and form parameter, wind speed distributes; Mean wind speed

is suc as formula shown in (3), then:

$v\left(x\right)=\frac{2\mathrm{\&theta;}}{f\left(x\right)}{\&Integral;}_l^x(\stackrel{\&OverBar;}{x}-y)f\left(y\right)\mathrm{dy}$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\stackrel{\&OverBar;}{x}F\left(x\right)-{\&Integral;}_l^x\mathrm{yf}\left(y\right)\mathrm{dy})---\left(9\right)$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\mathrm{c\&Gamma;}(\frac{1}{k}+1)(1-\exp [-{\left(\frac{x}{c}\right)}^k\left]\right)-\mathrm{c\&Gamma;}({\left(\frac{x}{c}\right)}^k,\frac{1}{k}+1))$

According to formula (9), single wind energy turbine set sequential wind speed

can be generated by the following formula iterative computation:

${\hat{v}}_{\mathrm{it}}^{*}={\hat{v}}_{\mathrm{it}-1}^{*}+d{X}_t---\left(10\right)$

(1-62) windy field gas velocity simulation:

At first generate the relevant Brownian movement W of multidimensional _t, W _tEach dimension is the standard Brownian movement, and correlation matrix equals the wind farm wind velocity correlation matrix between each dimension; Afterwards, utilize W _tEach ties up component each wind farm wind velocity sequence of (1-61) middle method generation set by step;

(1-63) correction of wind energy turbine set simulation wind speed

According to 1-4) and 1-5), the wind series

that generates is at random revised:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ i＝1,2,...,12,j=1,2,...,m (11)

(1-64) obtain wind energy turbine set and simulate the sequence of exerting oneself

If C _i(x) be wind-powered electricity generation unit output characteristic curve, expression formula is following:

$C_i\left(v\right)=\left\{\begin{array}{cc}0,& 0\&le;v<v_{\mathrm{in}},v>v_{\mathrm{out}}\\ \frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3}R,& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ R,& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(12\right)$

In the formula (12), v _In, v _RatedWith v _OutBe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit;

Utilize correction back wind series

wind energy turbine set sequential power curve to generate by following formula:

$P_{\mathrm{it}}=n_{\mathrm{it}}(1-{\mathrm{\&eta;}}_i)C_i\left(v_{\mathrm{it}}^{*}\right)---\left(13\right)$

In the formula (13), P _ItBe exerting oneself of i the wind energy turbine set t moment; η _iBe i wind energy turbine set wake effect coefficient; n _ItBe that i wind energy turbine set can be used unit platform number;

2) ability that the wind energy turbine set sequential that obtains according to simulation is exerted oneself, annual wind-powered electricity generation is dissolved differentiation collection and peak modulation capacity, fm capacity, load-following capacity, quick marginal capacity and network delivery ability are as constraints; The wind-powered electricity generation ability of dissolving is carried out various dimensions differentiates, specifically comprise:

2-1) generate the annual wind-powered electricity generation ability of dissolving and differentiate collection Ω:

(2-11) with in monthly, 1 being divided into Unit 12, corresponding one month of unit, there is N i unit _iThe bar daily load curve, i=1,2 ..., 12;

The wind energy turbine set sequential that (2-12) obtains according to simulation is exerted oneself, and presses the unit and sets up " wind-powered electricity generation power curve storehouse in a few days ", establishes " wind-powered electricity generation power curve storehouse in a few days " total N of i unit _IjBar is the wind-powered electricity generation power curve in a few days, i=1, and 2 ..., 12;

(2-13) power curve of wind-powered electricity generation in a few days in " the wind-powered electricity generation power curve storehouse in a few days " of daily load curve in each unit and corresponding unit is done combination; Then 1 year total

individual load-wind-powered electricity generation combination of exerting oneself, these the load-wind-powered electricity generations combination of exerting oneself is formed the annual wind-powered electricity generation ability of dissolving and is differentiated and collect Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated among the collection Ω, establish n load-wind-powered electricity generation exert oneself make up by the k bar in " the wind-powered electricity generation power curve storehouse in a few days " of the j bar load curve of i unit and i unit in a few days the wind-powered electricity generation power curve form i=1 wherein, 2; .., 12, j=1; 2 ..., N _i, k=1,2 ..., N _Ij:

The electric power system hour level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system hour stage load sequence that prediction obtains is designated as column vector

Then an electric power system hour level equivalent load sequence is column vector

$D_n^h=L_n^h-W_n^h---\left(14\right)$

Equivalent load hour level change sequence is designated as

$V_n^h\left(t\right)=D_n^h(t+1)-D_n^h\left(t\right)$ t=1,2，...,N ^h-1 (15)

In the formula (15),

Expression

In t element;

Expression

In t element; N ^hRepresent hourage in a few days;

The electric power system minute level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system minute stage load sequence that prediction obtains is designated as column vector

Then an electric power system minute level equivalent load sequence is

$D_n^m=L_n^m-W_n^m---\left(16\right)$

Equivalent load minute level change sequence is designated as

$V_n^m\left(t\right)=D_n^m(t+1)-D_n^m\left(t\right)$ t=1,2,...,N ^m-1 (17)

In the formula (17),

Expression In t element; Expression

In t element; N ^mRepresent the number of minutes in a few days;

2-2) confirm unit assembled state in a few days:

If unit adds up to N ^Unit, the annual wind-powered electricity generation ability of dissolving differentiate n load-wind-powered electricity generation among the collection Ω exert oneself make up in (n=1,2 ..., the open state variable of i platform unit N) is designated as u _{N, i}(i=1,2 ..., N ^Unit), suppose that unit does not in a few days allow start and stop, works as u _{N, i}Represent this unit whole day shutdown at=0 o'clock, work as u _{N, i}Represented this unit whole day start at=1 o'clock; In a few days whether each unit is started shooting and is confirmed as follows: start shooting successively by the machine set type; Power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine; Unit of the same type is by the descending start of unit capacity; Up to satisfying electric power system equivalent load demand, finally obtain electric power system unit assembled state in a few days;

2-3) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of peak regulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-31)

It is the EIAJ of i unit;

The minimum that is i unit is exerted oneself; I the minimum power factor of unit is designated as λ _i(i=1,2 ..., N ^Unit), expression formula is following:

${\mathrm{\&lambda;}}_i=\frac{P_i^{\max }-P_i^{\min }}{C_i},$ i＝1,2，...,N ^unit (18)

In the formula (18), C _iThe capacity of representing i unit;

(2-32) confirm said n load-wind-powered electricity generation exert oneself the combination the adjustable minimum output of electric power system expression formula following:

$P_{n,\mathrm{sys}}^{\min }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}(P_i^{\max }-{\mathrm{\&lambda;}}_i{C}_i)---\left(19\right)$

(2-33) confirm the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be compelled the subtracting of blower fan and exert oneself or shut down, abandon the wind-powered electricity generation amount for because the compelled loss value that subtracting the wind-powered electricity generation the sent out electric weight that caused of exerting oneself or shut down of blower fan)

of combination of said n load-wind-powered electricity generation

$P_{n,\mathrm{cut}}^{\mathrm{peak}}=\underset{t=1}{\overset{N^h}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g(P_{n,\mathrm{sys}}^{\min }-D_n^h\left(t\right))\}---\left(20\right)$

In the formula (20),

is said n load-wind-powered electricity generation the exert oneself wind-powered electricity generation value of exerting oneself in t moment in the sequence of the wind-powered electricity generation in a few days that makes up of exerting oneself;

is the exert oneself equivalent load value in t moment in the equivalent load sequence that makes up of said n load-wind-powered electricity generation; G (x) is a function of state, and expression formula is following:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<=0\\ 1,& x>0\end{array}\right.---\left(21\right)$

Then; As

when equaling 0, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passed through the peak modulation capacity constraint; Greater than 0 the time, represent that said n load-wind-powered electricity generation exert oneself combination through the peak modulation capacity constraint as

;

(2-34) if differentiating whole load-wind-powered electricity generations among the collection Ω in ability that annual wind-powered electricity generation is dissolved exerts oneself after combination calculation accomplishes, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the peak modulation capacity constraint ratio lambda of dissolving _Peak, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _PeakExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{peak}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left({-P}_{n,\mathrm{cut}}^{\mathrm{peak}}\right)}{N}\&times;100\%---\left(22\right);$

Otherwise rotate back into (2-2);

2-4) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-41) minute level of the i platform unit adjustment factor

of exerting oneself

${\mathrm{\&upsi;}}_i^m=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{m,\max }}{C_i},$ i＝1,2,...,N ^unit (23)

In the formula (23),

is meant that minute level maximum adjustable of i platform unit exerts oneself;

(2-42) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 minute of exerting oneself, it is following to be designated as expression formula:

$V_{n,\mathrm{sys}}^{m,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^m{C}_i---\left(24\right)$

(2-43) followed by days of time comparing

and

As

greater than 0 the time; In a few days there are indivedual fm capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the fm capacity constraint; As

when equaling 0; In a few days all satisfy the fm capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the fm capacity constraint;

(2-44) judge whether the annual wind-powered electricity generation in the 1 year ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving if accomplish _Freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _FreqExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{freq}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^m-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right)}{N}\&times;100\%---\left(25\right);$

Otherwise rotate back into (2-2);

2-5) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-51) define the power system load, the regular maintenance and emergency reserve reserve ratio

and the negative reserve ratio

expressions

As follows:

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}=\frac{R_{n,\mathrm{sys}}^{+}}{L_n^{h,\max }}\&times;100\%---\left(26\right)$

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}=\frac{R_{n,\mathrm{sys}}^{-}}{L_n^{h,\max }}\&times;100\%---\left(27\right)$

In formula (26) and the formula (27),

expression day peak load;

representes power system load, maintenance and the positive stand-by requirement capacity of accident;

expression power system load, maintenance and the negative stand-by requirement capacity of accident;

(2-52) define the power system wind power output is alternate rate

and the negative reserve ratio

expression is as follows:

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}=\frac{R_{n,\mathrm{wind}}^{+}}{W_n^{h,1\max }}\&times;100\%---\left(28\right)$

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}=\frac{R_{n,\mathrm{wind}}^{-}}{W_n^{h,1\max }}\&times;100\%---\left(29\right)$

In formula (28) and the formula (29), the wind-powered electricity generation of

expression peak load period is exerted oneself;

expression electric power system wind-powered electricity generation positive stand-by requirement capacity of exerting oneself; expression electric power system wind-powered electricity generation is exerted oneself and is born the stand-by requirement capacity;

(2-53) determine the annual capacity of wind power consumptive determine the set Ω in the n-th load - a combination of wind power output of the power system is spare capacity requirements

and the negative demand spare capacity

expression is as follows:

$R_{n,\mathrm{sys}}^{n+}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}\&CenterDot;W_n^{h,1\max }---\left(30\right)$

$R_{n,\mathrm{sys}}^{n-}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}\&CenterDot;W_n^{h,1\max }---\left(31\right)$

(2-54) i-units are fast alternate adjustment coefficient

and the negative adjustment coefficient

expression is as follows:

${\mathrm{\&alpha;}}_i^{+}=\frac{\mathrm{\&Delta;}{R}_i^{+}}{C_i}---\left(32\right)$

${\mathrm{\&alpha;}}_i^{-}=\frac{\mathrm{\&Delta;}{R}_i^{-}}{C_i}---\left(33\right)$

In formula (32) and the formula (33),

is respectively i platform unit available positive reserve capacity and negative reserve capacity under open state;

(2-55) of the n-th load - combinations of wind power output of the power system is available spare capacity

and the negative spare capacity available expression is as follows:

$R_{n,\mathrm{sys}}^{s+}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{+}{C}_i---\left(34\right)$

$R_{n,\mathrm{sys}}^{s-}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{-}{C}_i---\left(35\right)$

(2-56) compare

and

and

As

greater than

or

during greater than

; Expression electric power system marginal capacity is not enough, and said n load-wind-powered electricity generation exerted oneself combination not through the marginal capacity constraint; When being not more than

or

as

and being not more than ; Expression electric power system marginal capacity is abundant, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the marginal capacity constraint;

(2-57) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving if accomplish _Rese, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate λ _ReseExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{rese}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\&CenterDot;g(R_{n,\mathrm{sys}}^{s-}-R_{n,\mathrm{sys}}^{n-}))}{N}\&times;100\%---\left(36\right);$

Otherwise rotate back into step (2-2);

2-6) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-61) hour level of the i platform unit adjustment factor

of exerting oneself

${\mathrm{\&upsi;}}_i^h=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{h,\max }}{C_i},$ i＝1,2，...,N ^unit (37)

In the formula (37), is meant that hour level maximum adjustable of i platform unit exerts oneself;

(2-62) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 hour of exerting oneself, it is following to be designated as

expression formula:

$V_{m,\mathrm{sys}}^{h,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^h{C}_i---\left(38\right)$

(2-63) confirm that can the exert oneself load-following capacity constraint of combination of said n load-wind-powered electricity generation pass through, by in a few days constantly comparing

successively and

As

greater than 0 the time; In a few days there are indivedual load-following capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the load-following capacity constraint; As

when equaling 0; In a few days all satisfy the load-following capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the load-following capacity constraint;

(2-64) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving if accomplish _Foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate λ _FollExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{foll}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right)}{N}\&times;100\%---\left(39\right);$

Otherwise rotate back into (2-2);

2-7) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-71) when the electric power system node existed the district to send the electricity plan outside, the note district sent an electricity hour level outside and exerts oneself sequence for

(2-72) set of k lines of contact line transmission capacity limit is

the outgoing power system capacity limit of

expression is as follows:

$P_{\mathrm{sys}}^{\lim }=\underset{k=1}{\overset{N^{\mathrm{line}}}{\mathrm{\&Sigma;}}}{P}_k^{\lim }---\left(40\right)$

In the formula (40), N ^LineExpression electric power system interconnection sum;

(2-73) differentiate the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω to the annual wind-powered electricity generation ability of dissolving; Exert oneself when sending capacity limitation outside greater than electric power system when the district sends electricity outside, electric power system will to abandon wind

expression formula following because of the network capacity constraint produces:

$P_{n,\mathrm{cut}}^{\mathrm{grid}}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\&CenterDot;\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\}---\left(41\right)$

In the formula (41),

is t element in

;

Then when the exert oneself wind-powered electricity generation amount of abandoning

when being 0 of combination of this said n load-wind-powered electricity generation, representing that said n load-wind-powered electricity generation exerts oneself to make up has passed through the network capacity constraint; Exert oneself the wind-powered electricity generation amount of abandoning

of combination greater than 0 the time when said n load-wind-powered electricity generation, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passes through network capacity and retrain;

(2-74) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving if accomplish _Grid, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate λ _GridExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{grid}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}[1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)]}{N}\&times;100\%---\left(42\right);$

Otherwise rotate back into (2-2);

2-8) differentiate collection Ω, carry out with peak modulation capacity, fm capacity, marginal capacity, load-following capacity and network delivery ability be as comprehensive constraint fast, carry out the wind-powered electricity generation integration capability of dissolving and differentiate according to the annual wind-powered electricity generation ability of dissolving:

(2-81) set up power system control parameter row vector

There are five elements that characterize the power system in identifying the wind power consumptive ability to process considering the constraints; when the element has a value of 1 indicates that discrimination wind power consumptive capacity of the process to consider the corresponding factor constraints; when the element has a value of 0 indicates that discrimination ability of wind power consumptive process does not consider the constraints of the corresponding factors;

The correspondence between the elements: the first element corresponding peaking capacity, the second element

corresponds FM capability The third element

fast spare capacity corresponds to the fourth element

corresponding load following capability, the fifth element

corresponds to the network transmission capacity; when considering all the factors of constraint,

(2-82) note considers that the wind-powered electricity generation of a plurality of dimension constraints ratio of dissolving is that

expression formula is following:

${\mathrm{\&lambda;}}^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left[\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right]}{N}\&times;100\%---\left(43\right)$

In the formula (43),

$h\left(x\right)=\left\{\begin{array}{cc}1& x=0\\ x& x\&NotEqual;0\end{array}\right.$

${\mathrm{\&lambda;}}_1=g(-P_{n,\mathrm{cut}}^{\mathrm{peak}});$

${\mathrm{\&lambda;}}_2=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right);$

${\mathrm{\&lambda;}}_3=g\left(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\right);$

${\mathrm{\&lambda;}}_4=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right);$

${\mathrm{\&lambda;}}_5=1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)$

(2-83) with considering five wind-powered electricity generations under dimensions constraint ratio lambda of dissolving ^TotalCarry out the wind-powered electricity generation integration capability of dissolving and differentiate λ ^TotalExpression formula is following:

${\mathrm{\&lambda;}}^{\mathrm{total}}={\mathrm{\&lambda;}}^{\left[\mathrm{1,1,1,1,1}\right]}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}{\mathrm{\&lambda;}}_i\right)}{N}\&times;100\%---\left(44\right);$

2-9) differentiate collection Ω to the dissolve differentiation of ability of monthly and day degree wind-powered electricity generation according to the annual wind-powered electricity generation ability of dissolving

(2-91) differentiate collection Ω with the monthly wind-powered electricity generation ability of dissolving _i, characterize the annual wind-powered electricity generation ability of dissolving and differentiate the load-wind-powered electricity generation that is in the i month among the collection Ω composite set of exerting oneself, i=1,2,3 ..., 12;

(2-92) carry out the dissolve differentiation of ability of monthly wind-powered electricity generation with the wind-powered electricity generation of the i month ratio

of dissolving,

expression formula is following:

${\mathrm{\&lambda;}}_i^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_i}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_i\right)}\&times;100\%,$ i＝1,2,...,12(45)

In the formula (45), N (Ω _i) the expression set omega _iLoad-wind-powered electricity generation number of combinations of exerting oneself; The annual wind-powered electricity generation of Ω (n) the expression ability of dissolving is differentiated the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω;

(2-93) differentiate collection Ω with the day degree wind-powered electricity generation ability of dissolving _{I, j}, characterizing the annual wind-powered electricity generation ability of dissolving, to differentiate daily load curve among the collection Ω be load-wind-powered electricity generation of j days i month composite set of exerting oneself, i=1, and 2,3 ..., 12, j=1,2,3 ... N _i

(2-94) carry out the dissolve differentiation of ability of day degree wind-powered electricity generation with the wind-powered electricity generation of the i j day month ratio

of dissolving, expression formula is following:

${\mathrm{\&lambda;}}_{i,j}^{S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_{i,j}}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_{i,j}\right)}\&times;100\%,$ i=1,2，...,12(46)。

Technical characterstic of the present invention and beneficial effect:

The various dimensions wind-powered electricity generation that the present invention can carry out peak regulation dimension, frequency modulation dimension, subsequent use dimension, load-following capacity dimension and the network delivery ability dimension ability of dissolving is differentiated, and also can carry out monthlyly differentiating with wind-powered electricity generation day degree the ability of dissolving; Utilizing wind-powered electricity generation of the present invention to dissolve the ability method of discrimination can be to a certain wind-powered electricity generation installation scale; Can obtain the ratio of dissolving in the year under this scale, wind-powered electricity generation monthly and day degree; Annual wind-powered electricity generation installation planning be can instruct, arrange monthly wind-powered electricity generation operation strategy, day degree power system dispatching and controlling schemes optimized; Realization is to the efficient utilization of wind-powered electricity generation, and is significant to planning, operation, scheduling and the control of electric power system.

The present invention has jumped out the constraint of ability method of discrimination in flow scheme design and theoretical method aspect of dissolving of existing electric power system wind-powered electricity generation; Set up a cover based on dissolve ability method of discrimination of the various dimensions wind-powered electricity generation of wind-powered electricity generation operation simulation; Complete electric power system peak modulation capacity, fm capacity, quick marginal capacity, load-following capacity and the network delivery ability taken into account; Utilize the windy electric field operation analogue technique of considering temporal correlation; Science differentiates annual, monthly and the day degree wind-powered electricity generation ability of dissolving, for power system dispatching, operation and control personnel provide one to overlap the dissolve instrument of ability of quick differentiation wind-powered electricity generation.

The present invention can help power system dispatching, operation and control personnel precisely to estimate the admissible wind-powered electricity generation scale of Future Power System from a plurality of dimensions; And then the wind-powered electricity generation of clear and definite Future Power System under the different time yardstick scale of dissolving; Judge following wind-powered electricity generation fast and whether can fully be dissolved, each function links such as the operation of electric power system, scheduling, control are had important practical significance and good prospects for application by electric power system.

Description of drawings

Fig. 1 simulates force curve for embodiment each wind energy turbine set operation one day;

Embodiment

Below in conjunction with accompanying drawing and embodiment, the ability method of discrimination of dissolving based on the various dimensions wind-powered electricity generation of wind-powered electricity generation operation simulation is elaborated.The invention discloses a kind of various dimensions wind-powered electricity generation ability method of discrimination of dissolving, it is characterized in that, comprising:, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself 1) according to surveying wind data based on wind-powered electricity generation operation simulation; 2) according to simulation wind energy turbine set sequential exert oneself, the annual wind-powered electricity generation ability of dissolving is differentiated collection and peak modulation capacity, fm capacity, load-following capacity, marginal capacity and network delivery ability are carried out various dimensions to the wind-powered electricity generation ability of dissolving and differentiated as constraints fast;

1) according to surveying wind data, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself, specifically may further comprise the steps:

1-1) obtain scale parameter c and the form parameter k that Weibull distributes according to surveying the wind data match:

(1-11) two-parameter Weibull distribution function F _{W (c, k)}(x) expression formula is following:

$F_{W(c,k)}\left(x\right)=1-\exp [-{\left(\frac{x}{c}\right)}^k],x\&Element;[0,+\&infin;)---\left(1\right)$

In the formula (1), x is a wind speed;

(1-12) the probability density function f of two-parameter Weibull distribution _{W (c, k)}(x) as follows:

$f_{W(c,k)}\left(x\right)=\frac{k}{c}{\left(\frac{x}{c}\right)}^{k-1}\exp [-{\left(\frac{x}{c}\right)}^k]---\left(2\right)$

(1-13) expression formula of mean wind speed

is following:

$\stackrel{\&OverBar;}{x}=\mathrm{c\&Gamma;}(1+1/k)---\left(3\right)$

(1-14) try to achieve form parameter k by the wind speed standard deviation sigma, expression formula is following:

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{x}}=\sqrt{[\mathrm{\&Gamma;}(1+\frac{2}{k})/{\mathrm{\&Gamma;}}^2(1+\frac{1}{k})]-1}---\left(4\right)$

Wherein, mean wind speed

is directly proportional with Weibull distribution mesoscale parameter c; Γ is a gamma function:

$\mathrm{\&Gamma;}\left(a\right)={\&Integral;}_0^{+\&infin;}{y}^{a-1}{e}^{-y}\mathrm{dy});$

1-2) the temporal correlation of wind speed setting: according to surveying the temporal correlation characteristic quantity θ that the wind data match obtains wind speed, the auto-correlation function of wind speed is represented by negative exponential function that numerically expression formula is following:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

In the formula (5), the speed of the size of θ decision auto-correlation function decay, the severe of sign fluctuations in wind speed;

1-3) the spatial coherence of wind speed setting: there are the negative exponent relation in coefficient correlation between the windy field gas velocity and the geographic distance between the wind energy turbine set, and expression formula is following:

$c=e^{-\frac{d}{M}}---\left(6\right)$

In the formula (6), c is the wind speed coefficient correlation; D is a geographic distance between the two wind-powered electricity generation sections; M is that the wind speed coefficient correlation is with the range attenuation factor;

Be that base value obtains each monthly average wind series k 1-4) with annual wind speed mean value _m, k _mIn element expression following:

$k_{\mathrm{mi}}=\frac{v_{\mathrm{mi}}}{{\stackrel{\&OverBar;}{v}}_y},$ i＝1,2,3,...,12 (7)

In the formula (7), k _MiBe k _mIn i element; v _MiMean wind speed for the i month in year; Be the average of the whole year wind speed;

Be that base value obtains each moment mean wind speed sequence k in a few days 1-5) with whole day wind speed mean value _h, k _hIn element expression following:

$k_{\mathrm{hj}}=\frac{v_{\mathrm{hj}}}{{\stackrel{\&OverBar;}{v}}_d},$ j=1,2,3,...,N ^day (8)

In the formula (8), k _HjBe k _hIn j element; v _HjBe the mean wind speed of j period in a few days;

Be the whole day mean wind speed; N ^DayBe period sum in a few days;

1-6) utilize windy electric field operation analogue technique to carry out wind speed simulation:

(1-61) single wind farm wind velocity simulation:

If meeting the Weoibull that is respectively c and k suc as formula the scale parameter shown in (1) and the formula (2) and form parameter, wind speed distributes; Mean wind speed

is suc as formula shown in (3), then:

$v\left(x\right)=\frac{2\mathrm{\&theta;}}{f\left(x\right)}{\&Integral;}_l^x(\stackrel{\&OverBar;}{x}-y)f\left(y\right)\mathrm{dy}$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\stackrel{\&OverBar;}{x}F\left(x\right)-{\&Integral;}_l^x\mathrm{yf}\left(y\right)\mathrm{dy})---\left(9\right)$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\mathrm{c\&Gamma;}(\frac{1}{k}+1)(1-\exp [-{\left(\frac{x}{c}\right)}^k\left]\right)-\mathrm{c\&Gamma;}({\left(\frac{x}{c}\right)}^k,\frac{1}{k}+1))$

According to formula (9), single wind energy turbine set sequential wind speed

can be generated by the following formula iterative computation:

${\hat{v}}_{\mathrm{it}}^{*}={\hat{v}}_{\mathrm{it}-1}^{*}+d{X}_t---\left(10\right)$

(1-62) windy field gas velocity simulation:

At first generate the relevant Brownian movement W of multidimensional _t, W _tEach dimension is the standard Brownian movement, and correlation matrix equals the wind farm wind velocity correlation matrix between each dimension; Afterwards, utilize W _tEach ties up component each wind farm wind velocity sequence of (1-61) middle method generation set by step;

(1-63) correction of wind energy turbine set simulation wind speed

According to 1-4) and 1-5), the wind series that generates is at random revised:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ i＝1,2,...,12,j=1,2,..,m (11)

(1-64) obtain wind energy turbine set and simulate the sequence of exerting oneself

If C _i(x) be wind-powered electricity generation unit output characteristic curve, expression formula is following:

$C_i\left(v\right)=\left\{\begin{array}{cc}0,& 0\&le;v<v_{\mathrm{in}},v>v_{\mathrm{out}}\\ \frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3}R,& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ R,& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(12\right)$

In the formula (12), v _In, v _RatedWith v _OutBe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit;

Utilize correction back wind series

wind energy turbine set sequential power curve to generate by following formula:

$P_{\mathrm{it}}=n_{\mathrm{it}}(1-{\mathrm{\&eta;}}_i)C_i\left(v_{\mathrm{it}}^{*}\right)---\left(13\right)$

In the formula (13), P _ItBe exerting oneself of i the wind energy turbine set t moment; η _iBe i wind energy turbine set wake effect coefficient; n _ItBe that i wind energy turbine set can be used unit platform number;

2) ability that the wind energy turbine set sequential that obtains according to simulation is exerted oneself, annual wind-powered electricity generation is dissolved differentiation collection and peak modulation capacity, fm capacity, load-following capacity, quick marginal capacity and network delivery ability are as constraints; The wind-powered electricity generation ability of dissolving is carried out various dimensions differentiates, specifically comprise:

2-1) generate the annual wind-powered electricity generation ability of dissolving and differentiate collection Ω:

(2-11) with in monthly, 1 being divided into Unit 12, corresponding one month of unit, there is N i unit _iThe bar daily load curve, i=1,2 ..., 12;

The wind energy turbine set sequential that (2-12) obtains according to simulation is exerted oneself, and presses the unit and sets up " wind-powered electricity generation power curve storehouse in a few days ", establishes " wind-powered electricity generation power curve storehouse in a few days " total N of i unit _IjBar is the wind-powered electricity generation power curve in a few days, i=1, and 2 ..., 12;

(2-13) power curve of wind-powered electricity generation in a few days in " the wind-powered electricity generation power curve storehouse in a few days " of daily load curve in each unit and corresponding unit is done combination; Then 1 year total

individual load-wind-powered electricity generation combination of exerting oneself, these the load-wind-powered electricity generations combination of exerting oneself is formed the annual wind-powered electricity generation ability of dissolving and is differentiated and collect Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated among the collection Ω, establish n load-wind-powered electricity generation exert oneself make up by the k bar in " the wind-powered electricity generation power curve storehouse in a few days " of the j bar load curve of i unit and i unit in a few days the wind-powered electricity generation power curve form i=1 wherein, 2; ..., 12, j=1; 2 ..., N _i, k=1,2 ..., N _Ij:

The electric power system hour level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system hour stage load sequence that prediction obtains is designated as column vector

Then an electric power system hour level equivalent load sequence is column vector

$D_n^h=L_n^h-W_n^h---\left(14\right)$

Equivalent load hour level change sequence is designated as

$V_n^h\left(t\right)=D_n^h(t+1)-D_n^h\left(t\right)$ t=1,2，...,N ^h-1 (15)

In the formula (15),

Expression In t element;

Expression In t element; N ^hRepresent hourage in a few days;

The electric power system minute level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system minute stage load sequence that prediction obtains is designated as column vector

Then an electric power system minute level equivalent load sequence is

$D_n^m=L_n^m-W_n^m---\left(16\right)$

Equivalent load minute level change sequence is designated as

$V_n^m\left(t\right)=D_n^m(t+1)-D_n^m\left(t\right)$ t=1,2,...,N ^m-1 (17)

In the formula (17),

Expression

In t element;

Expression In t element; N ^mRepresent the number of minutes in a few days;

2-2) confirm unit assembled state in a few days:

If unit adds up to N ^Unit, the annual wind-powered electricity generation ability of dissolving differentiate n load-wind-powered electricity generation among the collection Ω exert oneself make up in (n=1,2 ..., the open state variable of i platform unit N) is designated as u _{N, i}(i=1,2 ..., N ^Unit), suppose that unit does not in a few days allow start and stop, works as u _{N, i}Represent this unit whole day shutdown at=0 o'clock, work as u _{N, i}Represented this unit whole day start at=1 o'clock; In a few days whether each unit is started shooting and is confirmed as follows: start shooting successively by the machine set type; Power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine; Unit of the same type is by the descending start of unit capacity; Up to satisfying electric power system equivalent load demand, finally obtain electric power system unit assembled state in a few days;

2-3) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of peak regulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-31)

It is the EIAJ of i unit;

The minimum that is i unit is exerted oneself; I the minimum power factor of unit is designated as λ _i(i=1,2 ..., N ^Unit), expression formula is following:

${\mathrm{\&lambda;}}_i=\frac{P_i^{\max }-P_i^{\min }}{C_i},$ i＝1,2，...,N ^unit (18)

In the formula (18), C _iThe capacity of representing i unit;

(2-32) confirm said n load-wind-powered electricity generation exert oneself the combination the adjustable minimum output of electric power system

expression formula following:

$P_{n,\mathrm{sys}}^{\min }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}(P_i^{\max }-{\mathrm{\&lambda;}}_i{C}_i)---\left(19\right)$

(2-33) confirm the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be compelled the subtracting of blower fan and exert oneself or shut down, abandon the wind-powered electricity generation amount for because the compelled loss value that subtracting the wind-powered electricity generation the sent out electric weight that caused of exerting oneself or shut down of blower fan)

of combination of said n load-wind-powered electricity generation

$P_{n,\mathrm{cut}}^{\mathrm{peak}}=\underset{t=1}{\overset{N^h}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g(P_{n,\mathrm{sys}}^{\min }-D_n^h\left(t\right))\}---\left(20\right)$

In the formula (20),

is said n load-wind-powered electricity generation the exert oneself wind-powered electricity generation value of exerting oneself in t moment in the sequence of the wind-powered electricity generation in a few days that makes up of exerting oneself;

is the exert oneself equivalent load value in t moment in the equivalent load sequence that makes up of said n load-wind-powered electricity generation; G (x) is a function of state, and expression formula is following:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<=0\\ 1,& x>0\end{array}\right.---\left(21\right)$

Then; As

when equaling 0, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passed through the peak modulation capacity constraint; Greater than 0 the time, represent that said n load-wind-powered electricity generation exert oneself combination through the peak modulation capacity constraint as

;

(2-34) if differentiating whole load-wind-powered electricity generations among the collection Ω in ability that annual wind-powered electricity generation is dissolved exerts oneself after combination calculation accomplishes, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the peak modulation capacity constraint ratio lambda of dissolving _Peak, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _PeakExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{peak}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left({-P}_{n,\mathrm{cut}}^{\mathrm{peak}}\right)}{N}\&times;100\%---\left(22\right);$

Otherwise rotate back into (2-2);

2-4) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-41) minute level of the i platform unit adjustment factor

of exerting oneself

${\mathrm{\&upsi;}}_i^m=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{m,\max }}{C_i},$ i＝1,2,...,N ^unit (23)

In the formula (23),

is meant that minute level maximum adjustable of i platform unit exerts oneself;

(2-42) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 minute of exerting oneself, it is following to be designated as

expression formula:

$V_{n,\mathrm{sys}}^{m,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^m{C}_i---\left(24\right)$

(2-43) followed by days of time comparing

and

As

greater than 0 the time; In a few days there are indivedual fm capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the fm capacity constraint; As

when equaling 0; In a few days all satisfy the fm capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the fm capacity constraint;

(2-44) judge whether the annual wind-powered electricity generation in the 1 year ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving if accomplish _Freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _FreqExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{freq}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^m-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right)}{N}\&times;100\%---\left(25\right);$

Otherwise rotate back into (2-2);

2-5) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-51) define the power system load, the regular maintenance and emergency reserve reserve ratio

and the negative reserve ratio

expression is as follows:

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}=\frac{R_{n,\mathrm{sys}}^{+}}{L_n^{h,\max }}\&times;100\%---\left(26\right)$

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}=\frac{R_{n,\mathrm{sys}}^{-}}{L_n^{h,\max }}\&times;100\%---\left(27\right)$

In formula (26) and the formula (27),

expression day peak load; representes power system load, maintenance and the positive stand-by requirement capacity of accident;

expression power system load, maintenance and the negative stand-by requirement capacity of accident;

(2-52) define the power system wind power output is alternate rate

and the negative reserve ratio expression is as follows:

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}=\frac{R_{n,\mathrm{wind}}^{+}}{W_n^{h,1\max }}\&times;100\%---\left(28\right)$

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}=\frac{R_{n,\mathrm{wind}}^{-}}{W_n^{h,1\max }}\&times;100\%---\left(29\right)$

In formula (28) and the formula (29), the wind-powered electricity generation of

expression peak load period is exerted oneself;

expression electric power system wind-powered electricity generation positive stand-by requirement capacity of exerting oneself;

expression electric power system wind-powered electricity generation is exerted oneself and is born the stand-by requirement capacity;

(2-53) determine the annual capacity of wind power consumptive determine the set Ω in the n-th load - a combination of wind power output of the power system is spare capacity requirements

and the negative demand spare capacity

expression is as follows:

$R_{n,\mathrm{sys}}^{n+}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}\&CenterDot;W_n^{h,1\max }---\left(30\right)$

$R_{n,\mathrm{sys}}^{n-}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}\&CenterDot;W_n^{h,1\max }---\left(31\right)$

(2-54) i-units are fast alternate adjustment coefficient

and the negative adjustment coefficient

expression is as follows:

${\mathrm{\&alpha;}}_i^{+}=\frac{\mathrm{\&Delta;}{R}_i^{+}}{C_i}---\left(32\right)$

${\mathrm{\&alpha;}}_i^{-}=\frac{\mathrm{\&Delta;}{R}_i^{-}}{C_i}---\left(33\right)$

In formula (32) and the formula (33), is respectively i platform unit available positive reserve capacity and negative reserve capacity under open state;

(2-55) of the n-th load - combinations of wind power output of the power system is available spare capacity and the negative spare capacity available

expression is as follows:

$R_{n,\mathrm{sys}}^{s+}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{+}{C}_i---\left(34\right)$

$R_{n,\mathrm{sys}}^{s-}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{-}{C}_i---\left(35\right)$

(2-56) compare and

and

As

greater than

or

during greater than

; Expression electric power system marginal capacity is not enough, and said n load-wind-powered electricity generation exerted oneself combination not through the marginal capacity constraint; When being not more than

or

as

and being not more than

; Expression electric power system marginal capacity is abundant, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the marginal capacity constraint;

(2-57) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving if accomplish _Rese, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate λ _ReseExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{rese}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\&CenterDot;g(R_{n,\mathrm{sys}}^{s-}-R_{n,\mathrm{sys}}^{n-}))}{N}\&times;100\%---\left(36\right);$

Otherwise rotate back into step (2-2);

2-6) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-61) hour level of the i platform unit adjustment factor

of exerting oneself

${\mathrm{\&upsi;}}_i^h=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{h,\max }}{C_i},$ i＝1,2，...,N ^unit (37)

In the formula (37),

is meant that hour level maximum adjustable of i platform unit exerts oneself;

(2-62) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 hour of exerting oneself, it is following to be designated as

expression formula:

$V_{m,\mathrm{sys}}^{h,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^h{C}_i---\left(38\right)$

(2-63) confirm that can the exert oneself load-following capacity constraint of combination of said n load-wind-powered electricity generation pass through, by in a few days constantly comparing

successively and

As

greater than 0 the time; In a few days there are indivedual load-following capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the load-following capacity constraint; As when equaling 0; In a few days all satisfy the load-following capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the load-following capacity constraint;

(2-64) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving if accomplish _Foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate λ _FollExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{foll}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right)}{N}\&times;100\%---\left(39\right);$

Otherwise rotate back into (2-2);

2-7) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-71) when the electric power system node existed the district to send the electricity plan outside, the note district sent an electricity hour level outside and exerts oneself sequence for

(2-72) set of k lines of contact line transmission capacity limit is

the outgoing power system capacity limit of

expression is as follows:

$P_{\mathrm{sys}}^{\lim }=\underset{k=1}{\overset{N^{\mathrm{line}}}{\mathrm{\&Sigma;}}}{P}_k^{\lim }---\left(40\right)$

In the formula (40), N ^LineExpression electric power system interconnection sum;

(2-73) differentiate the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω to the annual wind-powered electricity generation ability of dissolving; Exert oneself when sending capacity limitation outside greater than electric power system when the district sends electricity outside, electric power system will to abandon wind

expression formula following because of the network capacity constraint produces:

$P_{n,\mathrm{cut}}^{\mathrm{grid}}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\&CenterDot;\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\}---\left(41\right)$

In the formula (41),

is t element in

;

Then when the exert oneself wind-powered electricity generation amount of abandoning

when being 0 of combination of this said n load-wind-powered electricity generation, representing that said n load-wind-powered electricity generation exerts oneself to make up has passed through the network capacity constraint; Exert oneself the wind-powered electricity generation amount of abandoning of combination greater than 0 the time when said n load-wind-powered electricity generation, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passes through network capacity and retrain;

(2-74) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving if accomplish _Grid, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate λ _GridExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{grid}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}[1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)]}{N}\&times;100\%---\left(42\right);$

Otherwise rotate back into (2-2);

2-8) differentiate collection Ω, carry out with peak modulation capacity, fm capacity, marginal capacity, load-following capacity and network delivery ability be as comprehensive constraint fast, carry out the wind-powered electricity generation integration capability of dissolving and differentiate according to the annual wind-powered electricity generation ability of dissolving:

(2-81) set up power system control parameter row vector

There are five elements that characterize the power system in identifying the wind power consumptive ability to process considering the constraints; when the element has a value of 1 indicates that discrimination wind power consumptive capacity of the process to consider the corresponding factor constraints; when the element has a value of 0 indicates that discrimination ability of wind power consumptive process does not consider the constraints of the corresponding factors;

The correspondence between the elements: the first element corresponding peaking capacity, the second element

corresponds FM capability The third element

fast spare capacity corresponds to the fourth element corresponding load following capability, the fifth element

corresponds to the network transmission capacity; when considering all the factors of constraint,

(2-82) note considers that the wind-powered electricity generation of a plurality of dimension constraints ratio of dissolving is that

expression formula is following:

${\mathrm{\&lambda;}}^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left[\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right]}{N}\&times;100\%---\left(43\right)$

In the formula (43),

$h\left(x\right)=\left\{\begin{array}{cc}1& x=0\\ x& x\&NotEqual;0\end{array}\right.$

${\mathrm{\&lambda;}}_1=g(-P_{n,\mathrm{cut}}^{\mathrm{peak}});$

${\mathrm{\&lambda;}}_2=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right);$

${\mathrm{\&lambda;}}_3=g\left(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\right);$

${\mathrm{\&lambda;}}_4=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right);$

${\mathrm{\&lambda;}}_5=1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)$

(2-83) with considering five wind-powered electricity generations under dimensions constraint ratio lambda of dissolving ^TotalCarry out the wind-powered electricity generation integration capability of dissolving and differentiate λ ^TotalExpression formula is following:

${\mathrm{\&lambda;}}^{\mathrm{total}}={\mathrm{\&lambda;}}^{\left[\mathrm{1,1,1,1,1}\right]}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}{\mathrm{\&lambda;}}_i\right)}{N}\&times;100\%---\left(44\right);$

2-9) differentiate collection Ω to the dissolve differentiation of ability of monthly and day degree wind-powered electricity generation according to the annual wind-powered electricity generation ability of dissolving

(2-91) differentiate collection Ω with the monthly wind-powered electricity generation ability of dissolving _i, characterize the annual wind-powered electricity generation ability of dissolving and differentiate the load-wind-powered electricity generation that is in the i month among the collection Ω composite set of exerting oneself, i=1,2,3 ..., 12;

(2-92) carry out the dissolve differentiation of ability of monthly wind-powered electricity generation with the wind-powered electricity generation of the i month ratio of dissolving,

expression formula is following:

${\mathrm{\&lambda;}}_i^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_i}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_i\right)}\&times;100\%,$ i＝1,2,...,12 (45)

In the formula (45), N (Ω _i) the expression set omega _iLoad-wind-powered electricity generation number of combinations of exerting oneself; The annual wind-powered electricity generation of Ω (n) the expression ability of dissolving is differentiated the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω;

(2-93) differentiate collection Ω with the day degree wind-powered electricity generation ability of dissolving _{I, j}, characterizing the annual wind-powered electricity generation ability of dissolving, to differentiate daily load curve among the collection Ω be load-wind-powered electricity generation of j days i month composite set of exerting oneself, i=1, and 2,3 ..., 12, j=1,2,3 ... N _i

(2-94) carry out the dissolve differentiation of ability of day degree wind-powered electricity generation with the wind-powered electricity generation of the i j day month ratio

of dissolving,

expression formula is following:

${\mathrm{\&lambda;}}_{i,j}^{S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_{i,j}}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_{i,j}\right)}\&times;100\%,$ i＝1,2,..,12(46)。

Technical characterstic of the present invention and beneficial effect:

The various dimensions wind-powered electricity generation that the present invention can carry out peak regulation dimension, frequency modulation dimension, subsequent use dimension, load-following capacity dimension and the network delivery ability dimension ability of dissolving is differentiated, and also can carry out monthlyly differentiating with wind-powered electricity generation day degree the ability of dissolving; Utilizing wind-powered electricity generation of the present invention to dissolve the ability method of discrimination can be to a certain wind-powered electricity generation installation scale; Can obtain the ratio of dissolving in the year under this scale, wind-powered electricity generation monthly and day degree; Annual wind-powered electricity generation installation planning be can instruct, arrange monthly wind-powered electricity generation operation strategy, day degree power system dispatching and controlling schemes optimized; Realization is to the efficient utilization of wind-powered electricity generation, and is significant to planning, operation, scheduling and the control of electric power system.

The present invention has jumped out the constraint of ability method of discrimination in flow scheme design and theoretical method aspect of dissolving of existing electric power system wind-powered electricity generation; Set up a cover based on dissolve ability method of discrimination of the various dimensions wind-powered electricity generation of wind-powered electricity generation operation simulation; Complete electric power system peak modulation capacity, fm capacity, quick marginal capacity, load-following capacity and the network delivery ability taken into account; Utilize the windy electric field operation analogue technique of considering temporal correlation; Science differentiates annual, monthly and the day degree wind-powered electricity generation ability of dissolving, for power system dispatching, operation and control personnel provide one to overlap the dissolve instrument of ability of quick differentiation wind-powered electricity generation.

The present invention can help power system dispatching, operation and control personnel precisely to estimate the admissible wind-powered electricity generation scale of Future Power System from a plurality of dimensions; And then the wind-powered electricity generation of clear and definite Future Power System under the different time yardstick scale of dissolving; Judge following wind-powered electricity generation fast and whether can fully be dissolved, each function links such as the operation of electric power system, scheduling, control are had important practical significance and good prospects for application by electric power system.

Embodiment:

With certain provincial area is that example is set forth the various dimensions wind-powered electricity generation based on the wind-powered electricity generation operation simulation proposed by the invention ability method of discrimination of dissolving.

1), utilize the simulation of windy electric field operation analogue technique to obtain the wind energy turbine set sequential and exert oneself according to surveying wind data:

(1-1) set the wind farm wind velocity parameter value,, preestablish the wind speed basic parameter of wind-powered electricity generation operation simulation according to this area's historical wind speed statistics, as shown in table 1:

(1-2) setting of wind speed correlation between the windy electric field, getting relevant electric power system is 300 with the range attenuation factor M:

Each wind energy turbine set geographic distance between any two is as shown in table 2:

The coefficient correlation of the windy electric field that obtains according to last table is as shown in table 3:

(1-3) the day internal characteristic k of wind speed _h, as shown in table 4:

(1-4) the Seasonal Characteristics k of wind speed _m, as shown in table 5:

(1-5) utilize wind-powered electricity generation operation analogue technique, simulation obtains the annual wind-powered electricity generation sequential sequence of exerting oneself, and the wind energy turbine set operation of getting a certain day simulates force curve, and is as shown in Figure 1:

2) the wind energy turbine set sequential that obtains according to simulation is exerted oneself and with peak modulation capacity, fm capacity, load-following capacity, marginal capacity and network delivery ability are differentiated the wind-powered electricity generation ability of dissolving as various dimensions constraints fast, is specifically comprised:

2-1) generate the annual wind-powered electricity generation ability of dissolving and differentiate collection Ω:

(2-11) with in monthly, 1 being divided into Unit 12 (unit corresponding month), there is N i unit _iThe bar daily load curve (i=1,2 ..., 12);

Then,

N ₁=31,N ₂=28,N ₃=31,

N ₄=30,N ₅=31,N ₆=30,

N ₇=31,N ₈=31,N ₉=30,

N ₁₀=31,N ₁₁=30,N ₁₂=31

The wind energy turbine set sequential that (2-12) obtains according to simulation is exerted oneself, and presses the unit and sets up " wind-powered electricity generation power curve storehouse in a few days ", establishes " wind-powered electricity generation power curve storehouse in a few days " total N of i unit _Ij(i=1,2 ..., 12) bar wind-powered electricity generation power curve in a few days;

(2-13) power curve of wind-powered electricity generation in a few days in " the wind-powered electricity generation power curve storehouse in a few days " of daily load curve in each unit and corresponding unit is done combination; Then 1 year total

individual load-wind-powered electricity generation combination of exerting oneself, these combinations are formed the annual wind-powered electricity generation ability of dissolving and are differentiated and collect Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated among the collection Ω, establish n load-wind-powered electricity generation exert oneself make up by the k bar in " the wind-powered electricity generation power curve storehouse in a few days " of the j bar load curve of i unit and i unit in a few days the wind-powered electricity generation power curve form i=1 wherein, 2; ..., 12, j=1; 2 ..., N _i, k=1,2 ..., N _Ij:

The electric power system hour level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system hour stage load sequence that prediction obtains is designated as column vector

Then an electric power system hour level equivalent load sequence is column vector

$D_n^h=L_n^h-W_n^h---\left(1\right)$

Equivalent load hour level change sequence is designated as

$V_n^h\left(t\right)=D_n^h(t+1)-D_n^h\left(t\right)$ t=1,2,...,N ^h-1 (2)

In the formula (15),

Expression

In t element;

Expression

In t element; N ^hRepresent hourage in a few days;

The electric power system minute level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system minute stage load sequence that prediction obtains is designated as column vector

Then an electric power system minute level equivalent load sequence is

$D_n^m=L_n^m-W_n^m---\left(3\right)$

Equivalent load minute level change sequence is designated as

$V_n^m\left(t\right)=D_n^m(t+1)-D_n^m\left(t\right)$ t=1,2,...,N ^m-1 (4)

In the formula (17),

Expression

In t element;

Expression

In t element; N ^mRepresent the number of minutes in a few days;

2-2) confirm unit assembled state in a few days:

If unit adds up to N ^Unit, the annual wind-powered electricity generation ability of dissolving differentiate n load-wind-powered electricity generation among the collection Ω exert oneself make up in (n=1,2 ..., the open state variable of i platform unit N) is designated as u _Ni(i=1,2 ..., N ^Unit), suppose that unit does not in a few days allow start and stop, works as u _{N, i}Represent this unit whole day shutdown at=0 o'clock, work as u _{N, i}Represented this unit whole day start at=1 o'clock; In a few days whether each unit is started shooting and is confirmed as follows: start shooting successively by the machine set type; Power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine; Unit of the same type is by the descending start of unit capacity; Up to satisfying electric power system equivalent load demand, finally obtain electric power system unit assembled state in a few days;

2-3) differentiate collection Ω according to the annual wind-powered electricity generation ability of dissolving, carry out the wind-powered electricity generation ability of dissolving of peak regulation dimension and differentiate, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving of peak regulation dimension was 96.8%;

2-4) differentiate collection Ω according to the annual wind-powered electricity generation ability of dissolving, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving of frequency modulation dimension was 100%;

2-5) differentiate collection Ω according to the annual wind-powered electricity generation ability of dissolving, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving of subsequent use dimension was 100%;

2-6) differentiate collection Ω according to the annual wind-powered electricity generation ability of dissolving, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving of load-following capacity dimension was 100%;

2-7) differentiate collection Ω according to the annual wind-powered electricity generation ability of dissolving, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving of load-following capacity dimension was 99.6%;

2-8) differentiate collection Ω, carry out with peak modulation capacity, fm capacity, marginal capacity, load-following capacity and network delivery ability be as the wind-powered electricity generation of the comprehensive end ability method of discrimination of dissolving fast according to the annual wind-powered electricity generation ability of dissolving:

(2-81) note consider the wind-powered electricity generation of a plurality of dimensions constraints dissolve ratio for

when simultaneously with peak regulation and network delivery ability when retraining; When wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving was 96.5%;

(2-83) note considers that five wind-powered electricity generation ratios of dissolving under the dimension constraint are λ ^Total, when simultaneously with peak modulation capacity, fm capacity, when marginal capacity, load-following capacity and network delivery ability are as constraint fast, when wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving was 96.5%;

2-9) monthly and the day degree wind-powered electricity generation ability method of discrimination of dissolving

(2-91) the monthly wind-powered electricity generation ability of dissolving is differentiated collection Ω _i, characterize the annual wind-powered electricity generation ability of dissolving and differentiate the load-wind-powered electricity generation that is in the i month among the collection Ω composite set of exerting oneself, i=1,2,3 ..., 12;

(2-92) wind-powered electricity generation of the i month ratio

of dissolving is an example with July; When simultaneously with peak modulation capacity, fm capacity, when marginal capacity, load-following capacity and network delivery ability are as constraint fast; When wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving in July was 96.9%;

(2-93) the day degree wind-powered electricity generation of the i j day month ratio of dissolving was an example with July 1; When simultaneously with peak modulation capacity, fm capacity, when marginal capacity, load-following capacity and network delivery ability are as constraint fast; When wind-powered electricity generation installation scale was 11561MW, the wind-powered electricity generation ratio of dissolving on July 1 was 97.1%;

To a certain wind-powered electricity generation installation scale; Can obtain the ratio of dissolving in the year under this scale, wind-powered electricity generation monthly and day degree; Annual wind-powered electricity generation installation planning be can instruct, arrange monthly wind-powered electricity generation operation strategy, day degree power system dispatching and controlling schemes optimized; Realization is to the efficient utilization of wind-powered electricity generation, and is significant to planning, operation, scheduling and the control of electric power system.

Table 1

Table 2

Table 3

Table 4

Table 5

Above-described specific embodiment is merely explanation realization effect of the present invention, not in order to restriction the present invention.Modification, conversion and the improvement of any unsubstantiality of being done within all basic ideas and frameworks in method proposed by the invention all should be included within protection scope of the present invention.

Claims (1)

1. the various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving is characterized in that, comprising: 1) according to surveying wind data, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself; 2) according to simulation wind energy turbine set sequential exert oneself, the annual wind-powered electricity generation ability of dissolving is differentiated collection and peak modulation capacity, fm capacity, load-following capacity, marginal capacity and network delivery ability are carried out various dimensions to the wind-powered electricity generation ability of dissolving and differentiated as constraints fast;

1) according to surveying wind data, utilize windy electric field operation analogue technique simulation wind energy turbine set sequential to exert oneself, specifically may further comprise the steps:

1-1) obtain scale parameter c and the form parameter k that Weibull distributes according to surveying the wind data match:

(1-11) two-parameter Weibull distribution function F _{W (c, k)}(x) expression formula is following:

$F_{W(c,k)}\left(x\right)=1-\exp [-{\left(\frac{x}{c}\right)}^k],$ x∈[0,+∞) (1)

In the formula (1), x is a wind speed;

(1-12) the probability density function f of two-parameter Weibull distribution _{W (c, k)}(x) as follows:

$f_{W(c,k)}\left(x\right)=\frac{k}{c}{\left(\frac{x}{c}\right)}^{k-1}\exp [-{\left(\frac{x}{c}\right)}^k]---\left(2\right)$

(1-13) expression formula of mean wind speed

is following:

$\stackrel{\&OverBar;}{x}=\mathrm{c\&Gamma;}(1+1/k)---\left(3\right)$

(1-14) try to achieve form parameter k by the wind speed standard deviation sigma, expression formula is following:

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{x}}=\sqrt{[\mathrm{\&Gamma;}(1+\frac{2}{k})/{\mathrm{\&Gamma;}}^2(1+\frac{1}{k})]-1}---\left(4\right)$

Wherein, mean wind speed

is directly proportional with Weibull distribution mesoscale parameter c; Γ is a gamma function:

$\mathrm{\&Gamma;}\left(a\right)={\&Integral;}_0^{+\&infin;}{y}^{a-1}{e}^{-y}\mathrm{dy});$

1-2) the temporal correlation of wind speed setting: according to surveying the temporal correlation characteristic quantity θ that the wind data match obtains wind speed, the auto-correlation function of wind speed is represented by negative exponential function that numerically expression formula is following:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

In the formula (5), the speed of the size of θ decision auto-correlation function decay, the severe of sign fluctuations in wind speed;

1-3) the spatial coherence of wind speed setting: there are the negative exponent relation in coefficient correlation between the windy field gas velocity and the geographic distance between the wind energy turbine set, and expression formula is following:

$c=e^{-\frac{d}{M}}---\left(6\right)$

In the formula (6), c is the wind speed coefficient correlation; D is a geographic distance between the two wind-powered electricity generation sections; M is that the wind speed coefficient correlation is with the range attenuation factor;

Be that base value obtains each monthly average wind series k 1-4) with annual wind speed mean value _m, k _mIn element expression following:

$k_{\mathrm{mi}}=\frac{v_{\mathrm{mi}}}{{\stackrel{\&OverBar;}{v}}_y},$ i＝1,2,3,...,12 (7)

In the formula (7), k _MiBe k _mIn i element; v _MiMean wind speed for the i month in year;

Be the average of the whole year wind speed;

Be that base value obtains each moment mean wind speed sequence k in a few days 1-5) with whole day wind speed mean value _h, k _hIn element expression following:

$k_{\mathrm{hj}}=\frac{v_{\mathrm{hj}}}{{\stackrel{\&OverBar;}{v}}_d},$ j=1,2,3,...,N ^day (8)

In the formula (8), k _HjBe k _hIn j element; v _HjBe the mean wind speed of j period in a few days;

Be the whole day mean wind speed; N ^DayBe period sum in a few days;

1-6) utilize windy electric field operation analogue technique to carry out wind speed simulation:

(1-61) single wind farm wind velocity simulation:

If meeting the Weibull that is respectively c and k suc as formula the scale parameter shown in (1) and the formula (2) and form parameter, wind speed distributes; Mean wind speed

is suc as formula shown in (3), then:

$v\left(x\right)=\frac{2\mathrm{\&theta;}}{f\left(x\right)}{\&Integral;}_l^x(\stackrel{\&OverBar;}{x}-y)f\left(y\right)\mathrm{dy}$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\stackrel{\&OverBar;}{x}F\left(x\right)-{\&Integral;}_l^x\mathrm{yf}\left(y\right)\mathrm{dy})---\left(9\right)$

$=\frac{2\mathrm{\&theta;}}{f\left(x\right)}(\mathrm{c\&Gamma;}(\frac{1}{k}+1)(1-\exp [-{\left(\frac{x}{c}\right)}^k\left]\right)-\mathrm{c\&Gamma;}({\left(\frac{x}{c}\right)}^k,\frac{1}{k}+1))$

According to formula (9), single wind energy turbine set sequential wind speed can be generated by the following formula iterative computation:

${\hat{v}}_{\mathrm{it}}^{*}={\hat{v}}_{\mathrm{it}-1}^{*}+d{X}_t---\left(10\right)$

(1-62) windy field gas velocity simulation:

At first generate the relevant Brownian movement W of multidimensional _t, W _tEach dimension is the standard Brownian movement, and correlation matrix equals the wind farm wind velocity correlation matrix between each dimension; Afterwards, utilize W _tEach ties up component each wind farm wind velocity sequence of (1-61) middle method generation set by step;

(1-63) correction of wind energy turbine set simulation wind speed

According to 1-4) and 1-5), the wind series

that generates is at random revised:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ i＝1,2,...,12,j=1,2,...,m (11)

(1-64) obtain wind energy turbine set and simulate the sequence of exerting oneself

If C _i(x) be wind-powered electricity generation unit output characteristic curve, expression formula is following:

$C_i\left(v\right)=\left\{\begin{array}{cc}0,& 0\&le;v<v_{\mathrm{in}},v>v_{\mathrm{out}}\\ \frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3}R,& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ R,& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(12\right)$

In the formula (12), v _In, v _RatedWith v _OutBe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit;

Utilize correction back wind series wind energy turbine set sequential power curve to generate by following formula:

$P_{\mathrm{it}}=n_{\mathrm{it}}(1-{\mathrm{\&eta;}}_i)C_i\left(v_{\mathrm{it}}^{*}\right)---\left(13\right)$

In the formula (13), P _ItBe exerting oneself of i the wind energy turbine set t moment; η _iBe i wind energy turbine set wake effect coefficient; n _ItBe that i wind energy turbine set can be used unit platform number;

2) ability that the wind energy turbine set sequential that obtains according to simulation is exerted oneself, annual wind-powered electricity generation is dissolved differentiation collection and peak modulation capacity, fm capacity, load-following capacity, quick marginal capacity and network delivery ability are as constraints; The wind-powered electricity generation ability of dissolving is carried out various dimensions differentiates, specifically comprise:

2-1) generate the annual wind-powered electricity generation ability of dissolving and differentiate collection Ω:

(2-11) with in monthly, 1 being divided into Unit 12, corresponding one month of unit, there is N i unit _iThe bar daily load curve, i=1,2 ..., 12;

The wind energy turbine set sequential that (2-12) obtains according to simulation is exerted oneself, and presses the unit and sets up " wind-powered electricity generation power curve storehouse in a few days ", establishes " wind-powered electricity generation power curve storehouse in a few days " total N of i unit _IjBar is the wind-powered electricity generation power curve in a few days, i=1, and 2 ..., 12;

(2-13) power curve of wind-powered electricity generation in a few days in " the wind-powered electricity generation power curve storehouse in a few days " of daily load curve in each unit and corresponding unit is done combination; Then 1 year total

individual load-wind-powered electricity generation combination of exerting oneself, these the load-wind-powered electricity generations combination of exerting oneself is formed the annual wind-powered electricity generation ability of dissolving and is differentiated and collect Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated among the collection Ω, establish n load-wind-powered electricity generation exert oneself make up by the k bar in " the wind-powered electricity generation power curve storehouse in a few days " of the j bar load curve of i unit and i unit in a few days the wind-powered electricity generation power curve form i=1 wherein, 2; ..., 12, j=1; 2 .., N _i, k=1,2 ..., N _Ij:

The electric power system hour level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system hour stage load sequence that prediction obtains is designated as column vector

Then an electric power system hour level equivalent load sequence is column vector

$D_n^h=L_n^h-W_n^h---\left(14\right)$

Equivalent load hour level change sequence is designated as

$V_n^h\left(t\right)=D_n^h(t+1)-D_n^h\left(t\right)$ t=1,2，...,N ^h-1 (15)

In the formula (15),

Expression

In t element; Expression In t element; N ^hRepresent hourage in a few days;

The electric power system minute level wind-powered electricity generation that obtains of the simulation sequence of exerting oneself is designated as column vector

The electric power system minute stage load sequence that prediction obtains is designated as column vector

Then an electric power system minute level equivalent load sequence is

$D_n^m=L_n^m-W_n^m---\left(16\right)$

Equivalent load minute level change sequence is designated as

$V_n^m\left(t\right)=D_n^m(t+1)-D_n^m\left(t\right)$ t=1,2,...,N ^m-1 (17)

In the formula (17), Expression

In t element;

Expression In t element; N ^mRepresent the number of minutes in a few days;

2-2) confirm unit assembled state in a few days:

If unit adds up to N ^Unit, the annual wind-powered electricity generation ability of dissolving differentiate n load-wind-powered electricity generation among the collection Ω exert oneself make up in (n=1,2 ..., the open state variable of i platform unit N) is designated as u _{N, i}(i=1,2 ..., N ^Unit), suppose that unit does not in a few days allow start and stop, works as u _{N, i}Represent this unit whole day shutdown at=0 o'clock, work as u _{N, i}Represented this unit whole day start at=1 o'clock; In a few days whether each unit is started shooting and is confirmed as follows: start shooting successively by the machine set type; Power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine; Unit of the same type is by the descending start of unit capacity; Up to satisfying electric power system equivalent load demand, finally obtain electric power system unit assembled state in a few days;

2-3) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of peak regulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-31) It is the EIAJ of i unit;

The minimum that is i unit is exerted oneself; I the minimum power factor of unit is designated as λ _i(i=1,2 ..., N ^Unit), expression formula is following:

${\mathrm{\&lambda;}}_i=\frac{P_i^{\max }-P_i^{\min }}{C_i},$ i＝1,2,...,N ^unit (18)

In the formula (18), C _iThe capacity of representing i unit;

(2-32) confirm said n load-wind-powered electricity generation exert oneself the combination the adjustable minimum output of electric power system

expression formula following:

$P_{n,\mathrm{sys}}^{\min }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}(P_i^{\max }-{\mathrm{\&lambda;}}_i{C}_i)---\left(19\right)$

(2-33) confirm the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be compelled the subtracting of blower fan and exert oneself or shut down, abandon the wind-powered electricity generation amount for because the compelled loss value that subtracting the wind-powered electricity generation the sent out electric weight that caused of exerting oneself or shut down of blower fan)

of combination of said n load-wind-powered electricity generation

$P_{n,\mathrm{cut}}^{\mathrm{peak}}=\underset{t=1}{\overset{N^h}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g(P_{n,\mathrm{sys}}^{\min }-D_n^h\left(t\right))\}---\left(20\right)$

In the formula (20),

is said n load-wind-powered electricity generation the exert oneself wind-powered electricity generation value of exerting oneself in t moment in the sequence of the wind-powered electricity generation in a few days that makes up of exerting oneself;

is the exert oneself equivalent load value in t moment in the equivalent load sequence that makes up of said n load-wind-powered electricity generation; G (x) is a function of state, and expression formula is following:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<=0\\ 1,& x>0\end{array}\right.---\left(21\right)$

Then; As when equaling 0, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passed through the peak modulation capacity constraint; Greater than 0 the time, represent that said n load-wind-powered electricity generation exert oneself combination through the peak modulation capacity constraint as

;

(2-34) if differentiating whole load-wind-powered electricity generations among the collection Ω in ability that annual wind-powered electricity generation is dissolved exerts oneself after combination calculation accomplishes, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the peak modulation capacity constraint ratio lambda of dissolving _Peak, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _PeakExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{peak}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left({-P}_{n,\mathrm{cut}}^{\mathrm{peak}}\right)}{N}\&times;100\%---\left(22\right);$

Otherwise rotate back into (2-2);

2-4) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-41) minute level of the i platform unit adjustment factor

of exerting oneself

${\mathrm{\&upsi;}}_i^m=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{m,\max }}{C_i},$ i＝1,2,...,N ^unit (23)

In the formula (23),

is meant that minute level maximum adjustable of i platform unit exerts oneself;

(2-42) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 minute of exerting oneself, it is following to be designated as

expression formula:

$V_{n,\mathrm{sys}}^{m,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^m{C}_i---\left(24\right)$

(2-43) followed by days of time comparing

and

As

greater than 0 the time; In a few days there are indivedual fm capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the fm capacity constraint; As

when equaling 0; In a few days all satisfy the fm capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the fm capacity constraint;

(2-44) judge whether the annual wind-powered electricity generation in the 1 year ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving if accomplish _Freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate λ _FreqExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{freq}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^m-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right)}{N}\&times;100\%---\left(25\right);$

Otherwise rotate back into (2-2);

2-5) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-51) define the power system load, the regular maintenance and emergency reserve reserve ratio

and the negative reserve ratio

expression is as follows:

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}=\frac{R_{n,\mathrm{sys}}^{+}}{L_n^{h,\max }}\&times;100\%---\left(26\right)$

${\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}=\frac{R_{n,\mathrm{sys}}^{-}}{L_n^{h,\max }}\&times;100\%---\left(27\right)$

In formula (26) and the formula (27),

expression day peak load;

representes power system load, maintenance and the positive stand-by requirement capacity of accident;

expression power system load, maintenance and the negative stand-by requirement capacity of accident;

(2-52) define the power system wind power output is alternate rate and the negative reserve ratio

expression is as follows:

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}=\frac{R_{n,\mathrm{wind}}^{+}}{W_n^{h,1\max }}\&times;100\%---\left(28\right)$

${\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}=\frac{R_{n,\mathrm{wind}}^{-}}{W_n^{h,1\max }}\&times;100\%---\left(29\right)$

In formula (28) and the formula (29), the wind-powered electricity generation of

expression peak load period is exerted oneself;

expression electric power system wind-powered electricity generation positive stand-by requirement capacity of exerting oneself;

expression electric power system wind-powered electricity generation is exerted oneself and is born the stand-by requirement capacity;

(2-53) determine the annual capacity of wind power consumptive determine the set Ω in the n-th load - a combination of wind power output of the power system is spare capacity requirements

and the negative demand spare capacity

expression is as follows:

$R_{n,\mathrm{sys}}^{n+}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{+}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{+}\&CenterDot;W_n^{h,1\max }---\left(30\right)$

$R_{n,\mathrm{sys}}^{n-}={\mathrm{\&gamma;}}_{\mathrm{sys}}^{-}\&CenterDot;L_n^{h,\max }+{\mathrm{\&gamma;}}_{\mathrm{wind}}^{-}\&CenterDot;W_n^{h,1\max }---\left(31\right)$

(2-54) i-units are fast alternate adjustment coefficient

and the negative adjustment coefficient

expression is as follows:

${\mathrm{\&alpha;}}_i^{+}=\frac{\mathrm{\&Delta;}{R}_i^{+}}{C_i}---\left(32\right)$

${\mathrm{\&alpha;}}_i^{-}=\frac{\mathrm{\&Delta;}{R}_i^{-}}{C_i}---\left(33\right)$

In formula (32) and the formula (33),

is respectively i platform unit available positive reserve capacity and negative reserve capacity under open state;

(2-55) of the n-th load - combinations of wind power output of the power system is available spare capacity

and the negative spare capacity available expression is as follows:

$R_{n,\mathrm{sys}}^{s+}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{+}{C}_i---\left(34\right)$

$R_{n,\mathrm{sys}}^{s-}=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}(1-u_{n,i}){\mathrm{\&alpha;}}_i^{-}{C}_i---\left(35\right)$

(2-56) compare

and

and

As greater than

or

during greater than

; Expression electric power system marginal capacity is not enough, and said n load-wind-powered electricity generation exerted oneself combination not through the marginal capacity constraint; When being not more than or

as

and being not more than

; Expression electric power system marginal capacity is abundant, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the marginal capacity constraint;

(2-57) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving if accomplish _Rese, carry out the wind-powered electricity generation ability of dissolving of subsequent use dimension and differentiate λ _ReseExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{rese}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\&CenterDot;g(R_{n,\mathrm{sys}}^{s-}-R_{n,\mathrm{sys}}^{n-}))}{N}\&times;100\%---\left(36\right);$

Otherwise rotate back into step (2-2);

2-6) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-61) hour level of the i platform unit adjustment factor of exerting oneself

${\mathrm{\&upsi;}}_i^h=\frac{\mathrm{\&Delta;}{\mathrm{\&upsi;}}_i^{h,\max }}{C_i},$ i＝1,2，...,N ^unit (37)

In the formula (37),

is meant that hour level maximum adjustable of i platform unit exerts oneself;

(2-62) confirm that the annual wind-powered electricity generation ability of dissolving differentiates among the collection Ω n the load-wind-powered electricity generation adjustable EIAJ in the electric power system of making up 1 hour of exerting oneself, it is following to be designated as expression formula:

$V_{m,\mathrm{sys}}^{h,\max }=\underset{i=1}{\overset{N^{\mathrm{unit}}}{\mathrm{\&Sigma;}}}{u}_{n,i}{\mathrm{\&upsi;}}_i^h{C}_i---\left(38\right)$

(2-63) confirm that can the exert oneself load-following capacity constraint of combination of said n load-wind-powered electricity generation pass through, by in a few days constantly comparing successively and

As

greater than 0 the time; In a few days there are indivedual load-following capacity constraints of constantly running counter in expression, and said n load-wind-powered electricity generation exerted oneself combination not through the load-following capacity constraint; As

when equaling 0; In a few days all satisfy the load-following capacity constraint constantly in expression, and said n load-wind-powered electricity generation exerted oneself to make up and passed through the load-following capacity constraint;

(2-64) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving if accomplish _Foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate λ _FollExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{foll}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right)}{N}\&times;100\%---\left(39\right);$

Otherwise rotate back into (2-2);

2-7) differentiate collection Ω, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate, specifically comprise according to the annual wind-powered electricity generation ability of dissolving:

(2-71) when the electric power system node existed the district to send the electricity plan outside, the note district sent an electricity hour level outside and exerts oneself sequence for

(2-72) set of k lines of contact line transmission capacity limit is

the outgoing power system capacity limit of

expression is as follows:

$P_{\mathrm{sys}}^{\lim }=\underset{k=1}{\overset{N^{\mathrm{line}}}{\mathrm{\&Sigma;}}}{P}_k^{\lim }---\left(40\right)$

In the formula (40), N ^LineExpression electric power system interconnection sum;

(2-73) differentiate the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω to the annual wind-powered electricity generation ability of dissolving; Exert oneself when sending capacity limitation outside greater than electric power system when the district sends electricity outside, electric power system will to abandon wind

expression formula following because of the network capacity constraint produces:

$P_{n,\mathrm{cut}}^{\mathrm{grid}}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\min \{W_n^h\left(t\right),g\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\&CenterDot;\left(\right|P_{n,\mathrm{out}}^h\left(t\right)|-P_{\mathrm{sys}}^{\lim })\}---\left(41\right)$

In the formula (41),

is t element in

;

Then when the exert oneself wind-powered electricity generation amount of abandoning

when being 0 of combination of this said n load-wind-powered electricity generation, representing that said n load-wind-powered electricity generation exerts oneself to make up has passed through the network capacity constraint; Exert oneself the wind-powered electricity generation amount of abandoning

of combination greater than 0 the time when said n load-wind-powered electricity generation, represent that the combination of exerting oneself of said n load-wind-powered electricity generation passes through network capacity and retrain;

(2-74) judge whether the annual wind-powered electricity generation ability of dissolving is differentiated whole load-wind-powered electricity generations among collection Ω combination calculation of exerting oneself accomplishes,, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving if accomplish _Grid, carry out the wind-powered electricity generation ability of dissolving of network delivery ability dimension and differentiate λ _GridExpression formula is following:

${\mathrm{\&lambda;}}_{\mathrm{grid}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}[1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)]}{N}\&times;100\%---\left(42\right);$

Otherwise rotate back into (2-2);

2-8) differentiate collection Ω, carry out with peak modulation capacity, fm capacity, marginal capacity, load-following capacity and network delivery ability be as comprehensive constraint fast, carry out the wind-powered electricity generation integration capability of dissolving and differentiate according to the annual wind-powered electricity generation ability of dissolving:

(2-81) set up power system control parameter row vector

There are five elements that characterize the power system in identifying the wind power consumptive ability to process considering the constraints; when the element has a value of 1 indicates that discrimination wind power consumptive Factors considered during the corresponding capacity constraints; when the element has a value of 0 indicates that determine the ability of wind power consumptive process without considering the constraints of the corresponding factors;

The correspondence between the elements: the first element corresponding peaking capacity, the second element

corresponding FM capabilities, the third element fast spare capacity corresponds to the fourth element

corresponding load following capability, the first five elements

corresponds to the network transmission capacity; when considering all the factors of constraint,

(2-82) note considers that the wind-powered electricity generation of a plurality of dimension constraints ratio of dissolving is that

expression formula is following:

${\mathrm{\&lambda;}}^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left[\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right]}{N}\&times;100\%---\left(43\right)$

In the formula (43),

$h\left(x\right)=\left\{\begin{array}{cc}1& x=0\\ x& x\&NotEqual;0\end{array}\right.$

${\mathrm{\&lambda;}}_1=g(-P_{n,\mathrm{cut}}^{\mathrm{peak}});$

${\mathrm{\&lambda;}}_2=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^m\left(t\right)-V_{n,\mathrm{sys}}^{m,\max })\right);$

${\mathrm{\&lambda;}}_3=g\left(g(R_{n,\mathrm{sys}}^{s+}-R_{n,\mathrm{sys}}^{n+})\right);$

${\mathrm{\&lambda;}}_4=g\left(\underset{t=1}{\overset{N^{\mathrm{day}}-1}{\mathrm{\&Sigma;}}}g(V_n^h\left(t\right)-V_{n,\mathrm{sys}}^{h,\max })\right);$

${\mathrm{\&lambda;}}_5=1-g\left(P_{n,\mathrm{cut}}^{\mathrm{grid}}\right)$

(2-83) with considering five wind-powered electricity generations under dimensions constraint ratio lambda of dissolving ^TotalCarry out the wind-powered electricity generation integration capability of dissolving and differentiate λ ^TotalExpression formula is following:

${\mathrm{\&lambda;}}^{\mathrm{total}}={\mathrm{\&lambda;}}^{\left[\mathrm{1,1,1,1,1}\right]}=\frac{\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}{\mathrm{\&lambda;}}_i\right)}{N}\&times;100\%---\left(44\right);$

2-9) differentiate collection Ω to the dissolve differentiation of ability of monthly and day degree wind-powered electricity generation according to the annual wind-powered electricity generation ability of dissolving

(2-91) differentiate collection Ω with the monthly wind-powered electricity generation ability of dissolving _i, characterize the annual wind-powered electricity generation ability of dissolving and differentiate the load-wind-powered electricity generation that is in the i month among the collection Ω composite set of exerting oneself, i=1,2,3 ..., 12;

(2-92) carry out the dissolve differentiation of ability of monthly wind-powered electricity generation with the wind-powered electricity generation of the i month ratio

of dissolving,

expression formula is following:

${\mathrm{\&lambda;}}_i^{S_{\mathrm{sys}}^{\mathrm{con}}}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_i}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_i\right)}\&times;100\%,$ i＝1,2,..,12(45)

In the formula (45), N (Ω _i) the expression set omega _iLoad-wind-powered electricity generation number of combinations of exerting oneself; The annual wind-powered electricity generation of Ω (n) the expression ability of dissolving is differentiated the combination of exerting oneself of n load-wind-powered electricity generation among the collection Ω;

(2-93) differentiate collection Ω with the day degree wind-powered electricity generation ability of dissolving _{I, j}, characterizing the annual wind-powered electricity generation ability of dissolving, to differentiate daily load curve among the collection Ω be load-wind-powered electricity generation of j days i month composite set of exerting oneself, i=1, and 2,3 ..., 12, j=1,2,3 ... N _i

(2-94) carry out the dissolve differentiation of ability of day degree wind-powered electricity generation with the wind-powered electricity generation of the i j day month ratio

of dissolving, expression formula is following:

${\mathrm{\&lambda;}}_{i,j}^{S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_{i,j}}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_{i,j}\right)}\&times;100\%,$ i＝1,2,...,12(46)。

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