CN102780219B - 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

Various dimensions wind-powered electricity generation based on the wind-powered electricity generation operation simulation ability method of discrimination of dissolving

Technical field

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

Background technology

Since the eighties in last century, oil crisis, climate change, energy problem become international focus, and the clean energy resource that the wind energy of take is representative is fast-developing, 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.Generally, wind-powered electricity generation is exerted oneself and is shown the characteristic that is different from normal power supplies: randomness, fluctuation, uncertainty.These characteristics are that the safe operation of electric power system has brought severe challenge with stable control, the method that science is therefore provided is to realize the dissolve differentiation of ability of electric power system wind-powered electricity generation, and wind-powered electricity generation is dissolved ability using the important indicator of each function links such as the operation as 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, 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 history typical case wind-powered electricity generation power curve for electric power system, with deterministic computational methods, considers that wind-powered electricity generation is because usually differentiating the wind-powered electricity generation ability of dissolving, and its key step is:

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

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

3) according to the size judgement of adjustable space, can receive the wind-powered electricity generation power curve selecting;

4) multiple proportions is adjusted wind-powered electricity generation power curve, the wind-powered electricity generation that the wind-powered electricity generation power curve in the time of just can being received by electric power system the is considered as electric power system ability of dissolving.

The method Shortcomings:

1) 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, 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 wind-electricity integration for the impact of the each side factors such as electric power system peak regulation, frequency modulation, standby, Steam Generator in Load Follow and network;

4) for the situation of dissolving after following wind-electricity integration, comprehensively do not hold, 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, current taked measure only relates to the control of closing down of wind-powered electricity generation unit itself, does not coordinate the start-stop with conventional rack, limiting electric power system dissolve controlled original paper and the controlled range of wind-powered electricity generation.Under the definite open state of conventional unit, when electric power system, cannot dissolve too much wind-powered electricity generation while exerting oneself, closed portion wind turbine consists of unique possible strategy, thereby has wasted part wind power resources.

In sum, need a set of 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, the factor such as marginal capacity, load-following capacity and network delivery ability fast, consider wind-powered electricity generation power producing characteristics: the instrument that provides Quick 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, the method specifically comprises the following steps:

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

(1-1) Two-parameter Weibull distribution function F _{w (c, k)}(x) expression formula is as follows:

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

(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) mean wind speed

expression formula as follows:

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

(1-4) by wind speed deviation σ, try to achieve form parameter k, expression formula is as follows:

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

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

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

2) temporal correlation of wind speed setting: (be the wave characteristic of wind speed according to the temporal correlation characteristic quantity θ that surveys wind data matching and obtain wind speed, the mode that characterizes fluctuations in wind speed is auto-correlation function, auto-correlation function refers to the linearly dependent coefficient of the sequence of time series and self different time displacement, auto-correlation function is the tolerance of time series temporal correlation, the size of reflecting time sequence fluctuation, the value of auto-correlation function increased and decays with the time difference, time series fluctuation Shaoxing opera is strong, auto-correlation function decay is faster), the auto-correlation function of wind speed is numerically represented by negative exponential function, expression formula is as follows:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

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

3) spatial coherence of wind speed setting: adjacent wind energy turbine set is due to 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, between wind energy turbine set, wind speed correlation is main relevant with geographic distance: at a distance of nearer wind-powered electricity generation section, owing to being subject to the impact of same weather conditions, its wind speed will show stronger correlation; 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 is negative exponent relation in coefficient correlation and the geographic distance between wind energy turbine set between windy field gas velocity, expression formula is as follows:

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

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

4) take annual wind speed mean value obtains annual each monthly average wind series (being the Seasonal Characteristics of wind speed: due to climate reasons, Various Seasonal wind energy turbine set location velocity wind levels is different, and has certain rule) as base value, and each monthly average wind series is designated as k _m, k _min element expression as follows:

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

In formula (7), k _mifor k _min i element; v _mimean wind speed for the i month in year; for average of the whole year wind speed;

5) take whole day wind speed mean value as 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, because the difference of wind energy turbine set location surface temperature causes that in a few days mean wind speed is different in the same time), in a few days each constantly mean wind speed sequence be designated as k _h, k _hin element expression as follows:

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

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

for whole day mean wind speed; N ^dayfor 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) in its domain of definition (l, 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 formula (9), θ>=0, W _tfor standard Brownian movement, v (X _t) be the nonnegative function being defined on (l, u), expression formula is as follows:

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

:

Random process X is that each state 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 the method simulation wind speed, establish wind speed and meet the Weibull distribution that is respectively c and k suc as formula the scale parameter shown in (1) and formula (2) and form parameter, mean wind speed

shown in (3):

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

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

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

According to formula (9)-(12), single wind energy turbine set sequential wind speed

can be generated by 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:

First generate the Brownian movement W that multidimensional is relevant _t, W _teach dimension is standard Brownian movement, and between each dimension, correlation matrix equals wind farm wind velocity correlation matrix; Afterwards, utilize W _teach is tieed up component and generates each wind farm wind velocity sequence by method in step (6-1).

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

Wind farm wind velocity sequence is not completely random process, to due to climate reasons, Various Seasonal wind energy turbine set location velocity wind levels is different, and there is certain rule (as little in winter, summer is large), in a few days, because the difference of wind energy turbine set location surface temperature causes in a few days not mean wind speed different (as large in evening, daytime is little) in the same time, according to 4) and 5) wind series to random generation

revise:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ 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 power producing characteristics curve, expression formula is as follows:

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

In 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 and revise rear wind series

wind energy turbine set sequential power curve is generated by following formula:

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

In formula (16), η _ifor wind energy turbine set wake effect coefficient, expression wind energy turbine set, because of exerting oneself that wake effect loses, gets 5% ~ 10% conventionally; n _itfor wind energy turbine set, can use unit number of units, be a stochastic variable, represents unit reliability level in wind energy turbine set (if unit fault is obeyed independently exponential distribution in false wind electric field, for arbitrary time t, wind energy turbine set can be obeyed Bei Nuli by unit number of units and be distributed).

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

Summary of the invention

The object of the invention is to overcome the dissolve deficiency of ability method of discrimination of existing electric 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 fast prediction gained 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 based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving, it 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, as constraints, are carried out various dimensions differentiation to the wind-powered electricity generation ability of dissolving fast;

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

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

(1-11) Two-parameter Weibull distribution function F _{w (c, k)}(x) expression formula is as follows:

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

In formula (1), x is 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) mean wind speed

expression formula as follows:

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

(1-14) by wind speed deviation σ, try to achieve form parameter k, expression formula is as follows:

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

Wherein, mean wind speed

be directly proportional to Weibull distribution mesoscale parameter c; Γ is 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 the temporal correlation characteristic quantity θ that surveys wind data matching and obtain wind speed, the auto-correlation function of wind speed numerically represents by negative exponential function, and expression formula is as follows:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

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

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

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

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

1-4) take annual wind speed mean value obtains each monthly average wind series k as base value _m, k _min element expression as follows:

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

In formula (7), k _mifor k _min i element; v _mimean wind speed for the i month in year;

for average of the whole year wind speed;

1-5) take whole day wind speed mean value obtains in a few days each mean wind speed sequence k constantly as base value _h, k _hin element expression as follows:

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

In formula (8), k _hjfor k _hin j element; v _hjfor the mean wind speed of j period in a few days; for whole day mean wind speed; N ^dayfor 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 formula (2) and form parameter, wind speed distributes, mean wind speed

shown in (3):

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

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

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

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

can be generated by 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:

First generate the Brownian movement W that multidimensional is relevant _t, W _teach dimension is standard Brownian movement, and between each dimension, correlation matrix equals wind farm wind velocity correlation matrix; Afterwards, utilize W _teach is tieed up component and generates each wind farm wind velocity sequence by method in step (1-61);

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

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

revise:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ 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 power producing characteristics curve, expression formula is as follows:

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

In 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 and revise rear wind series

wind energy turbine set sequential power curve is generated by following formula:

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

In 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 number of units;

2) ability that the wind energy turbine set sequential obtaining 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 to various dimensions differentiation, specifically comprises:

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

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

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

(2-13) the in a few days wind-powered electricity generation power curve in " the in a few days wind-powered electricity generation power curve storehouse " of the daily load curve in each unit and corresponding unit is combined, within 1 year, have

the combination of exerting oneself of individual load-wind-powered electricity generation, the combination of exerting oneself of these load-wind-powered electricity generations forms the annual wind-powered electricity generation ability of dissolving and differentiates collection Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated in collection Ω, establish n load-wind-powered electricity generation exert oneself combine k bar in the j bar load curve of i unit and " the in a few days wind-powered electricity generation power curve storehouse " of i unit in a few days 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

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 formula (15),

represent

in t element;

represent

in t element; N ^hrepresent in a few days hourage;

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

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 formula (17), represent

in t element; represent

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

2-2) determine in a few days Unit Combination state:

If unit adds up to N ^unit, during n the load-wind-powered electricity generation that annual wind-powered electricity generation is dissolved in ability differentiation collection Ω exerted oneself and combined (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}represent this unit whole day start at=1 o'clock; In a few days whether each unit starts shooting definite as follows: by machine set type, start shooting successively, power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine, same type units is by the descending start of unit capacity, until meet electric power system equivalent load demand, finally obtain in a few days Unit Combination state of electric power system;

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

(2-31) it is the maximum output of i unit;

it is the minimum load of i unit; I unit minimum load coefficient is designated as λ _i(i=1,2 ..., N ^unit), expression formula is as follows:

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

In formula (18), C _ithe capacity that represents i unit;

(2-32) determine the adjustable minimum output of electric power system that described n load-wind-powered electricity generation exerted oneself and combined

expression formula is as follows:

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

(2-33) determine the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be blower fan and be forced to subtract and exert oneself or shut down, abandon wind-powered electricity generation amount for be forced to subtract the loss value of the wind-powered electricity generation the sent out electric weight of exerting oneself or shutting down caused due to blower fan) of combination of described 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 formula (20),

for described n the load-wind-powered electricity generation in a few days wind-powered electricity generation of the combination t wind-powered electricity generation value of exerting oneself constantly in sequence of exerting oneself of exerting oneself; for described n the load-wind-powered electricity generation t equivalent load value constantly in the equivalent load sequence of combination of exerting oneself; G (x) is function of state, and expression formula is as follows:

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

, when equal at 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through peak modulation capacity constraint; When

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by peak modulation capacity, do not retrain;

If (2-34) differentiate whole load-wind-powered electricity generations in collection Ω in ability that annual wind-powered electricity generation is dissolved, exert oneself after combination calculated, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under peak modulation capacity the retrains 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 as follows:

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

Otherwise rotate back into (2-2);

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

(2-41) a minute level for i platform unit the 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 formula (23),

refer to maximum adjustable the exerting oneself of minute level of i platform unit;

(2-42) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 minute of exerting oneself, be designated as

expression formula is as follows:

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

(2-43) by a few days constantly comparing successively

with

When

being greater than at 0 o'clock, in a few days there are indivedual fm capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by fm capacity, do not retrained; When

equal at 0 o'clock, represent that in a few days all moment all meet fm capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through fm capacity constraint;

(2-44) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation of year ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving _freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, λ _freqexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-51) the positive percentage reserve of definition power system load, maintenance and emergency duty

with negative percentage reserve

expression formula

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 formula (27),

represent a day peak load;

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

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

(2-52) the definition electric power system wind-powered electricity generation positive percentage reserve of exerting oneself with negative percentage reserve

expression formula 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 formula (29),

the wind-powered electricity generation that represents the peak load period is exerted oneself;

represent the 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 stand-by requirement capacity;

(2-53) determine that the annual wind-powered electricity generation ability of dissolving differentiates n the positive stand-by requirement capacity of electric power system that load-wind-powered electricity generation is exerted oneself and combined in collection Ω

with negative stand-by requirement capacity

expression formula 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) the quick standby positive adjustment factor of i platform unit

with negative regulator coefficient

expression formula 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 formula (33),

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

(2-55) electric power system that described n load-wind-powered electricity generation exerted oneself under combination is just standby for capacity

with the negative standby capacity that supplies

expression formula 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) relatively

with

with

When

be greater than

or

be greater than

time, representing that electric power system marginal capacity is not enough, described n load-wind-powered electricity generation exerted oneself to combine and by marginal capacity, do not retrained; When

be not more than or

be not more than

time, representing that electric power system marginal capacity is abundant, described n load-wind-powered electricity generation exerted oneself to combine and passed through marginal capacity constraint;

(2-57) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving _rese, carry out the wind-powered electricity generation ability of dissolving of standby dimension and differentiate, λ _reseexpression formula is as follows:

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

Otherwise rotate back into step (2-2);

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

(2-61) a hour level for i platform unit the 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 formula (37),

refer to maximum adjustable the exerting oneself of hour level of i platform unit;

(2-62) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 hour of exerting oneself, be designated as

expression formula is as follows:

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

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

with

When

being greater than at 0 o'clock, in a few days there are indivedual load-following capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by load-following capacity, do not retrained; When

equal at 0 o'clock, represent that in a few days all moment all meet load-following capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through load-following capacity constraint;

(2-64) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving _foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, λ _follexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-71) when electric power system node exists outside district outside power transmission plan Shi，Ji district the power transmission hour level sequence of exerting oneself, be

(2-72) the circuit transmission capacity limits of establishing k bar interconnection is

electric power system is sent capacity limitation outside and is

expression formula is as follows:

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

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

(2-73) for the annual wind-powered electricity generation ability of dissolving, differentiate n load-wind-powered electricity generation in the collection Ω power transmission of exerting oneself outside combination ，Dang district and exert oneself when being greater than electric power system and sending capacity limitation outside, electric power system will be abandoned wind because network capacity retrains generation

expression formula is as follows:

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

In formula (41),

for

in t element;

When n the wind-powered electricity generation amount of abandoning that load-wind-powered electricity generation is exerted oneself and combined described in this

be 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through network capacity constraint; When described n load-wind-powered electricity generation exert oneself combination the wind-powered electricity generation amount of abandoning

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by network capacity, do not retrain;

(2-74) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving _grid, carry out the wind-powered electricity generation ability of dissolving of network delivery competence dimension and differentiate, λ _gridexpression formula is as follows:

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

Otherwise rotate back into (2-2);

2-8) according to the annual wind-powered electricity generation ability of dissolving, differentiate collection Ω, carry out usining 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:

(2-81) establish electric power system control parameter line vector

have 5 elements, characterize the constraint that electric power system is considered in differentiation wind-powered electricity generation is dissolved ability process; When the value of element is 1, represent differentiates wind-powered electricity generation and dissolve and consider the constraint of corresponding factor in ability process; When the value of element is 0, represent differentiates wind-powered electricity generation and dissolve and do not consider the constraint of corresponding factor in ability process;

the corresponding relation of middle element is: first element

corresponding peak modulation capacity, second element corresponding fm capacity, the 3rd element

corresponding marginal capacity, the 4th element fast

corresponding load-following capacity, the 5th element

map network conveying capacity; When considering the constraint of whole factors,

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

expression formula is as follows:

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

In 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 as follows:

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

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

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

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

carry out the dissolve differentiation of ability of monthly wind-powered electricity generation, expression formula is as follows:

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

In formula (45), N (Ω _i) expression set omega _iload-wind-powered electricity generation number of combinations of exerting oneself; Ω (n) represents that the annual wind-powered electricity generation ability of dissolving differentiates the combination of exerting oneself of n load-wind-powered electricity generation in collection Ω;

(2-93) by the day degree wind-powered electricity generation ability of dissolving, differentiate collection Ω _{i, j}, characterize the annual wind-powered electricity generation ability of dissolving and differentiate load-wind-powered electricity generation that in collection Ω, daily load curve is j days i month composite set of exerting oneself, i=1,2,3 ..., 12, j=1,2,3 ... N _i

(2-94) by the wind-powered electricity generation of the i j day month ratio of dissolving

carry out the dissolve differentiation of ability of day degree wind-powered electricity generation,

expression formula is as follows:

${\mathrm{\&lambda;}}_{i,j}^{S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_{i,j}}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_{i,j}\right)}\&times;100\%,$ 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, standby dimension, load-following capacity dimension and the network delivery competence dimension ability of dissolving is differentiated, and also can carry out monthly and wind-powered electricity generation day degree the ability of dissolving and differentiate; Utilizing wind-powered electricity generation of the present invention to dissolve ability method of discrimination can be for 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 control program optimized, the efficient utilization of realization to wind-powered electricity generation, significant to the planning of electric power system, operation, scheduling and control.

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 set of various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving, the complete electric power system peak modulation capacity of taking into account, fm capacity, quick marginal capacity, load-following capacity and network delivery ability, utilize the windy electric field operation analogue technique of considering temporal correlation, science is differentiated year, monthly and the day degree wind-powered electricity generation ability of dissolving, for power system dispatching, the instrument that operation and control personnel provide a set of Quick wind-powered electricity generation to dissolve ability.

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 fast following wind-powered electricity generation and whether can fully be dissolved by electric power system, each function links such as the operation of electric power system, scheduling, control are had important practical significance and good application prospect.

Accompanying drawing explanation

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

Embodiment

Below in conjunction with drawings and Examples, the ability method of discrimination of dissolving of the various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation is elaborated.The invention discloses a kind of various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving, it 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, as constraints, are carried out various dimensions differentiation to the wind-powered electricity generation ability of dissolving fast;

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

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

(1-11) Two-parameter Weibull distribution function F _{w (c, k)}(x) expression formula is as follows:

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

In formula (1), x is 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) mean wind speed

expression formula as follows:

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

(1-14) by wind speed deviation σ, try to achieve form parameter k, expression formula is as follows:

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

Wherein, mean wind speed be directly proportional to Weibull distribution mesoscale parameter c; Γ is 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 the temporal correlation characteristic quantity θ that surveys wind data matching and obtain wind speed, the auto-correlation function of wind speed numerically represents by negative exponential function, and expression formula is as follows:

ρ(k)=e ^-θk,θ＞0,k=1,2,3... (5)

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

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

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

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

1-4) take annual wind speed mean value obtains each monthly average wind series k as base value _m, k _min element expression as follows:

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

In formula (7), k _mifor k _min i element; v _mimean wind speed for the i month in year;

for average of the whole year wind speed;

1-5) take whole day wind speed mean value obtains in a few days each mean wind speed sequence k constantly as base value _h, k _hin element expression as follows:

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

In formula (8), k _hjfor k _hin j element; v _hjfor the mean wind speed of j period in a few days; for whole day mean wind speed; N ^dayfor 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 formula (2) and form parameter, wind speed distributes, mean wind speed

shown in (3):

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

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

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

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

First generate the Brownian movement W that multidimensional is relevant _t, W _teach dimension is standard Brownian movement, and between each dimension, correlation matrix equals wind farm wind velocity correlation matrix; Afterwards, utilize W _teach is tieed up component and generates each wind farm wind velocity sequence by method in step (1-61);

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

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

revise:

$v_{\mathrm{it}}^{*}=k_{\mathrm{mi}}{k}_{\mathrm{hj}}{\hat{v}}_{\mathrm{it}}^{*},$ 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 power producing characteristics curve, expression formula is as follows:

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

In 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 and revise rear wind series

wind energy turbine set sequential power curve is generated by following formula:

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

In 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 number of units;

2) ability that the wind energy turbine set sequential obtaining 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 to various dimensions differentiation, specifically comprises:

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

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

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

(2-13) the in a few days wind-powered electricity generation power curve in " the in a few days wind-powered electricity generation power curve storehouse " of the daily load curve in each unit and corresponding unit is combined, within 1 year, have

the combination of exerting oneself of individual load-wind-powered electricity generation, the combination of exerting oneself of these load-wind-powered electricity generations forms the annual wind-powered electricity generation ability of dissolving and differentiates collection Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated in collection Ω, establish n load-wind-powered electricity generation exert oneself combine k bar in the j bar load curve of i unit and " the in a few days wind-powered electricity generation power curve storehouse " of i unit in a few days 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

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 formula (15),

represent

in t element; represent

in t element; N ^hrepresent in a few days hourage;

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

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 formula (17),

represent

in t element;

represent

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

2-2) determine in a few days Unit Combination state:

If unit adds up to N ^unit, during n the load-wind-powered electricity generation that annual wind-powered electricity generation is dissolved in ability differentiation collection Ω exerted oneself and combined (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}represent this unit whole day start at=1 o'clock; In a few days whether each unit starts shooting definite as follows: by machine set type, start shooting successively, power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine, same type units is by the descending start of unit capacity, until meet electric power system equivalent load demand, finally obtain in a few days Unit Combination state of electric power system;

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

(2-31)

it is the maximum output of i unit; it is the minimum load of i unit; I unit minimum load coefficient is designated as λ _i(i=1,2 ..., N ^unit), expression formula is as follows:

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

In formula (18), C _ithe capacity that represents i unit;

(2-32) determine the adjustable minimum output of electric power system that described n load-wind-powered electricity generation exerted oneself and combined

expression formula is as follows:

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

(2-33) determine the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be blower fan and be forced to subtract and exert oneself or shut down, abandon wind-powered electricity generation amount for be forced to subtract the loss value of the wind-powered electricity generation the sent out electric weight of exerting oneself or shutting down caused due to blower fan) of combination of described 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 formula (20),

for described n the load-wind-powered electricity generation in a few days wind-powered electricity generation of the combination t wind-powered electricity generation value of exerting oneself constantly in sequence of exerting oneself of exerting oneself; for described n the load-wind-powered electricity generation t equivalent load value constantly in the equivalent load sequence of combination of exerting oneself; G (x) is function of state, and expression formula is as follows:

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

, when

equal at 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through peak modulation capacity constraint; When

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by peak modulation capacity, do not retrain;

If (2-34) differentiate whole load-wind-powered electricity generations in collection Ω in ability that annual wind-powered electricity generation is dissolved, exert oneself after combination calculated, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under peak modulation capacity the retrains 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 as follows:

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

Otherwise rotate back into (2-2);

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

(2-41) a minute level for i platform unit the 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 formula (23),

refer to maximum adjustable the exerting oneself of minute level of i platform unit;

(2-42) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 minute of exerting oneself, be designated as

expression formula is as follows:

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

(2-43) by a few days constantly comparing successively

with

When

being greater than at 0 o'clock, in a few days there are indivedual fm capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by fm capacity, do not retrained; When

equal at 0 o'clock, represent that in a few days all moment all meet fm capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through fm capacity constraint;

(2-44) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation of year ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving _freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, λ _freqexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-51) the positive percentage reserve of definition power system load, maintenance and emergency duty with negative percentage reserve

expression formula 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 formula (27),

represent a day peak load;

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

(2-52) the definition electric power system wind-powered electricity generation positive percentage reserve of exerting oneself

with negative percentage reserve expression formula 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 formula (29),

the wind-powered electricity generation that represents the peak load period is exerted oneself;

represent the 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 stand-by requirement capacity;

(2-53) determine that the annual wind-powered electricity generation ability of dissolving differentiates n the positive stand-by requirement capacity of electric power system that load-wind-powered electricity generation is exerted oneself and combined in collection Ω

with negative stand-by requirement capacity

expression formula 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) the quick standby positive adjustment factor of i platform unit

with negative regulator coefficient

expression formula 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 formula (33),

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

(2-55) electric power system that described n load-wind-powered electricity generation exerted oneself under combination is just standby for capacity

with the negative standby capacity that supplies

expression formula 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) relatively with

with

When

be greater than

or

be greater than time, representing that electric power system marginal capacity is not enough, described n load-wind-powered electricity generation exerted oneself to combine and by marginal capacity, do not retrained; When

be not more than

or

be not more than

time, representing that electric power system marginal capacity is abundant, described n load-wind-powered electricity generation exerted oneself to combine and passed through marginal capacity constraint;

(2-57) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving _rese, carry out the wind-powered electricity generation ability of dissolving of standby dimension and differentiate, λ _reseexpression formula is as follows:

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

Otherwise rotate back into step (2-2);

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

(2-61) a hour level for i platform unit the 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 formula (37),

refer to maximum adjustable the exerting oneself of hour level of i platform unit;

(2-62) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 hour of exerting oneself, be designated as expression formula is as follows:

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

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

with

When

being greater than at 0 o'clock, in a few days there are indivedual load-following capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by load-following capacity, do not retrained; When

equal at 0 o'clock, represent that in a few days all moment all meet load-following capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through load-following capacity constraint;

(2-64) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving _foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, λ _follexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-71) when electric power system node exists outside district outside power transmission plan Shi，Ji district the power transmission hour level sequence of exerting oneself, be

(2-72) the circuit transmission capacity limits of establishing k bar interconnection is

electric power system is sent capacity limitation outside and is expression formula is as follows:

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

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

(2-73) for the annual wind-powered electricity generation ability of dissolving, differentiate n load-wind-powered electricity generation in the collection Ω power transmission of exerting oneself outside combination ，Dang district and exert oneself when being greater than electric power system and sending capacity limitation outside, electric power system will be abandoned wind because network capacity retrains generation

expression formula is as follows:

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

In formula (41),

for

in t element;

When n the wind-powered electricity generation amount of abandoning that load-wind-powered electricity generation is exerted oneself and combined described in this

be 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through network capacity constraint; When described n load-wind-powered electricity generation exert oneself combination the wind-powered electricity generation amount of abandoning

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by network capacity, do not retrain;

(2-74) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving _grid, carry out the wind-powered electricity generation ability of dissolving of network delivery competence dimension and differentiate, λ _gridexpression formula is as follows:

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

Otherwise rotate back into (2-2);

2-8) according to the annual wind-powered electricity generation ability of dissolving, differentiate collection Ω, carry out usining 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:

(2-81) establish electric power system control parameter line vector have 5 elements, characterize the constraint that electric power system is considered in differentiation wind-powered electricity generation is dissolved ability process; When the value of element is 1, represent differentiates wind-powered electricity generation and dissolve and consider the constraint of corresponding factor in ability process; When the value of element is 0, represent differentiates wind-powered electricity generation and dissolve and do not consider the constraint of corresponding factor in ability process;

the corresponding relation of middle element is: first element

corresponding peak modulation capacity, second element corresponding fm capacity, the 3rd element

corresponding marginal capacity, the 4th element fast corresponding load-following capacity, the 5th element map network conveying capacity; When considering the constraint of whole factors,

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

expression formula is as follows:

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

In 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 as follows:

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

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

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

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

carry out the dissolve differentiation of ability of monthly wind-powered electricity generation,

expression formula is as follows:

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

In formula (45), N (Ω _i) expression set omega _iload-wind-powered electricity generation number of combinations of exerting oneself; Ω (n) represents that the annual wind-powered electricity generation ability of dissolving differentiates the combination of exerting oneself of n load-wind-powered electricity generation in collection Ω;

(2-93) by the day degree wind-powered electricity generation ability of dissolving, differentiate collection Ω _{i, j}, characterize the annual wind-powered electricity generation ability of dissolving and differentiate load-wind-powered electricity generation that in collection Ω, daily load curve is j days i month composite set of exerting oneself, i=1,2,3 ..., 12, j=1,2,3 ... N _i

(2-94) by the wind-powered electricity generation of the i j day month ratio of dissolving

carry out the dissolve differentiation of ability of day degree wind-powered electricity generation,

expression formula is as follows:

${\mathrm{\&lambda;}}_{i,j}^{S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)}=\frac{\underset{\mathrm{\&Omega;}\left(n\right)\&Element;{\mathrm{\&Omega;}}_{i,j}}{\mathrm{\&Sigma;}}\left(\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}h(S_{\mathrm{sys}}^{\mathrm{con}}\left(i\right)\&CenterDot;{\mathrm{\&lambda;}}_i)\right)}{N\left({\mathrm{\&Omega;}}_{i,j}\right)}\&times;100\%,$ 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, standby dimension, load-following capacity dimension and the network delivery competence dimension ability of dissolving is differentiated, and also can carry out monthly and wind-powered electricity generation day degree the ability of dissolving and differentiate; Utilizing wind-powered electricity generation of the present invention to dissolve ability method of discrimination can be for 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 control program optimized, the efficient utilization of realization to wind-powered electricity generation, significant to the planning of electric power system, operation, scheduling and control.

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 set of various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation ability method of discrimination of dissolving, the complete electric power system peak modulation capacity of taking into account, fm capacity, quick marginal capacity, load-following capacity and network delivery ability, utilize the windy electric field operation analogue technique of considering temporal correlation, science is differentiated year, monthly and the day degree wind-powered electricity generation ability of dissolving, for power system dispatching, the instrument that operation and control personnel provide a set of Quick wind-powered electricity generation to dissolve ability.

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 fast following wind-powered electricity generation and whether can fully be dissolved by electric power system, each function links such as the operation of electric power system, scheduling, control are had important practical significance and good application prospect.

Embodiment:

Take certain provincial area sets 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 as example.

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

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

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

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

The coefficient correlation of the windy electric field obtaining according to upper 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, as shown in Figure 1:

2) the wind energy turbine set sequential obtaining according to simulation is exerted oneself and is usingd peak modulation capacity, fm capacity, load-following capacity, quick marginal capacity and network delivery ability as various dimensions constraints, and the wind-powered electricity generation ability of dissolving is differentiated, and specifically comprises:

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

(2-11) will in monthly, 1 be divided into Unit 12 (unit corresponding month), there is N i unit _ibar daily load curve (i=1,2 ..., 12);

,

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

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

(2-13) the in a few days wind-powered electricity generation power curve in " the in a few days wind-powered electricity generation power curve storehouse " of the daily load curve in each unit and corresponding unit is combined, within 1 year, have

the combination of exerting oneself of individual load-wind-powered electricity generation, these combinations form the annual wind-powered electricity generation ability of dissolving and differentiate collection Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated in collection Ω, establish n load-wind-powered electricity generation exert oneself combine k bar in the j bar load curve of i unit and " the in a few days wind-powered electricity generation power curve storehouse " of i unit in a few days 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

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 formula (15),

represent

in t element;

represent in t element; N ^hrepresent in a few days hourage;

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

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 formula (17),

represent

in t element;

represent

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

2-2) determine in a few days Unit Combination state:

If unit adds up to N ^unit, during n the load-wind-powered electricity generation that annual wind-powered electricity generation is dissolved in ability differentiation collection Ω exerted oneself and combined (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,irepresent this unit whole day shutdown at=0 o'clock, work as u _{n, i}represent this unit whole day start at=1 o'clock; In a few days whether each unit starts shooting definite as follows: by machine set type, start shooting successively, power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine, same type units is by the descending start of unit capacity, until meet electric power system equivalent load demand, finally obtain in a few days Unit Combination state of electric power system;

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

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

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

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

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

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

(2-81) note considers that the wind-powered electricity generation ratio of dissolving of a plurality of dimension constraints is

when simultaneously using peak regulation and network delivery ability as constraint, when wind-powered electricity generation installation scale is 11561MW, the wind-powered electricity generation ratio of dissolving is 96.5%;

(2-83) note considers that five wind-powered electricity generation ratios of dissolving under dimension constraint are λ ^total, when simultaneously using 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 is 11561MW, the wind-powered electricity generation ratio of dissolving is 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 in collection Ω in load-wind-powered electricity generation of i month composite set of exerting oneself, i=1,2,3 ..., 12;

(2-92) wind-powered electricity generation of the i month ratio of dissolving take July as example, when simultaneously using 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 is 11561MW, the wind-powered electricity generation ratio of dissolving in July is 96.9%;

(2-93) the day degree wind-powered electricity generation of the i j day month ratio of dissolving

take July 1 is example, when simultaneously using 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 is 11561MW, the wind-powered electricity generation ratio of dissolving on July 1 is 97.1%;

For 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 control program optimized, the efficient utilization of realization to wind-powered electricity generation, significant to the planning of electric power system, operation, scheduling and control.

Table 1

Table 2

Table 3

Table 4

Table 5

Above-described specific embodiment is only for the explanation effect that realizes of the present invention, not in order to limit the present invention.Modification, conversion and the improvement of any unsubstantiality of doing within all basic ideas in method proposed by the invention and framework, within all should being included in protection scope of the present invention.

Claims (1)

1. the ability method of discrimination of dissolving of the various dimensions wind-powered electricity generation based on wind-powered electricity generation operation simulation, 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, as constraints, are carried out various dimensions differentiation to the wind-powered electricity generation ability of dissolving fast;

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

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

(1-11) Two-parameter Weibull distribution function F _{w (c, k)}(x) expression formula is as follows:

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

In formula (1), x is wind speed;

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

$F_{W(c,k)}\left(x\right)=\frac{k}{c}{\left(\frac{x}{c}\right)}^{k-1}\exp \&lsqb;-{\left(\frac{x}{c}\right)}^k\&rsqb;---\left(2\right)$

(1-13) mean wind speed

expression formula as follows:

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

(1-14) by wind speed deviation σ, try to achieve form parameter k, expression formula is as follows:

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

Wherein, mean wind speed

be directly proportional to Weibull distribution mesoscale parameter c; Γ is 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 the temporal correlation characteristic quantity θ that surveys wind data matching and obtain wind speed, the auto-correlation function of wind speed numerically represents by negative exponential function, and expression formula is as follows:

ρ(k)＝e ^-θk,θ>0,k＝1,2,3...(5)

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

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

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

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

1-4) take annual wind speed mean value obtains each monthly average wind series k as base value _m, k _min element expression as follows: $k_{\mathrm{mi}}=\frac{v_{\mathrm{mi}}}{{\stackrel{\&OverBar;}{v}}_y},i=\mathrm{1,2,3},...,12---\left(7\right)$

In formula (7), k _mifor k _min i element; v _mimean wind speed for the i month in year;

for average of the whole year wind speed;

1-5) take whole day wind speed mean value obtains in a few days each mean wind speed sequence k constantly as base value _h, k _hin element expression as follows:

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

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

for whole day mean wind speed; N ^dayfor 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 formula (2) and form parameter, wind speed distributes, mean wind speed

shown in (3):

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

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

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

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

First generate the Brownian movement W that multidimensional is relevant _t, W _teach dimension is standard Brownian movement, and between each dimension, correlation matrix equals wind farm wind velocity correlation matrix; Afterwards, utilize W _teach is tieed up component and generates each wind farm wind velocity sequence by method in step (1-61);

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

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

revise:

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

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

If C _i(x) be wind-powered electricity generation unit power producing characteristics curve, expression formula is as follows:

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

In 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 and revise rear wind series

wind energy turbine set sequential power curve is generated by following formula:

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

In 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 number of units;

2) ability that the wind energy turbine set sequential obtaining 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 to various dimensions differentiation, specifically comprises:

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

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

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

(2-13) the in a few days wind-powered electricity generation power curve in " the in a few days wind-powered electricity generation power curve storehouse " of the daily load curve in each unit and corresponding unit is combined, within 1 year, have

the combination of exerting oneself of individual load-wind-powered electricity generation, the combination of exerting oneself of these load-wind-powered electricity generations forms the annual wind-powered electricity generation ability of dissolving and differentiates collection Ω;

(2-14) the annual wind-powered electricity generation ability of dissolving is differentiated in collection Ω, if n load-wind-powered electricity generation exerted oneself the k bar of combination in the j bar load curve of i unit and " the in a few days wind-powered electricity generation power curve storehouse " of i unit in a few days 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

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

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

In formula (15), represent

in t element;

represent in t element; N ^hrepresent in a few days hourage;

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

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

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

In formula (17),

represent

in t element;

represent

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

2-2) determine in a few days Unit Combination state:

If unit adds up to N ^unit, during n the load-wind-powered electricity generation that annual wind-powered electricity generation is dissolved in ability differentiation collection Ω exerted oneself and combined (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,irepresent this unit whole day shutdown at=0 o'clock, work as u _n,irepresent this unit whole day start at=1 o'clock; In a few days whether each unit starts shooting definite as follows: by machine set type, start shooting successively, power-up sequence is district's external power, nuclear power, thermoelectricity, water power and pumped storage, thermoelectricity, combustion machine, same type units is by the descending start of unit capacity, until meet electric power system equivalent load demand, finally obtain in a few days Unit Combination state of electric power system;

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

（2-31） $P_i^{\max }(i=\mathrm{1,2},...,N^{\mathrm{unit}})$ It is the maximum output of i unit; $P_i^{\max }(i=\mathrm{1,2},...,N^{\mathrm{unit}})$ It is the minimum load of i unit; I unit minimum load coefficient is designated as λ _i(i=1,2 ..., N ^unit), expression formula is as follows:

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

In formula (18), C _ithe capacity that represents i unit;

(2-32) determine the adjustable minimum output of electric power system that described n load-wind-powered electricity generation exerted oneself and combined

expression formula is as follows:

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

(2-33) determine the exert oneself wind-powered electricity generation amount of in a few days abandoning (abandon wind and be blower fan and be forced to subtract and exert oneself or shut down, abandon wind-powered electricity generation amount for be forced to subtract the loss value of the wind-powered electricity generation the sent out electric weight of exerting oneself or shutting down caused due to blower fan) of combination of described 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 formula (20),

for described n the load-wind-powered electricity generation in a few days wind-powered electricity generation of the combination t wind-powered electricity generation value of exerting oneself constantly in sequence of exerting oneself of exerting oneself;

for described n the load-wind-powered electricity generation t equivalent load value constantly in the equivalent load sequence of combination of exerting oneself; G (x) is function of state, and expression formula is as follows:

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

, when

equal at 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through peak modulation capacity constraint; When

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by peak modulation capacity, do not retrain;

If (2-34) differentiate whole load-wind-powered electricity generations in collection Ω in ability that annual wind-powered electricity generation is dissolved, exert oneself after combination calculated, obtain the wind-powered electricity generation of this wind-powered electricity generation installation scale under peak modulation capacity the retrains 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 as follows:

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

Otherwise rotate back into (2-2);

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

(2-41) a minute level for i platform unit the adjustment factor of exerting oneself

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

In formula (23),

refer to maximum adjustable the exerting oneself of minute level of i platform unit;

(2-42) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 minute of exerting oneself, be designated as

expression formula is as follows:

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

(2-43) by a few days constantly comparing successively

with

When

being greater than at 0 o'clock, in a few days there are indivedual fm capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by fm capacity, do not retrained; When equal at 0 o'clock, represent that in a few days all moment all meet fm capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through fm capacity constraint;

(2-44) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation of year ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the fm capacity constraint ratio lambda of dissolving _freq, carry out the wind-powered electricity generation ability of dissolving of frequency modulation dimension and differentiate, λ _freqexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-51) the positive percentage reserve of definition power system load, maintenance and emergency duty

with negative percentage reserve

expression formula 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 formula (27),

represent a day peak load;

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

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

(2-52) the definition electric power system wind-powered electricity generation positive percentage reserve of exerting oneself

with negative percentage reserve

expression formula 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 formula (29),

the wind-powered electricity generation that represents the peak load period is exerted oneself;

represent the 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 stand-by requirement capacity;

(2-53) determine that the annual wind-powered electricity generation ability of dissolving differentiates n the positive stand-by requirement capacity of electric power system that load-wind-powered electricity generation is exerted oneself and combined in collection Ω

with negative stand-by requirement capacity expression formula 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) the quick standby positive adjustment factor of i platform unit

with negative regulator coefficient

expression formula 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 formula (33),

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

(2-55) electric power system that described n load-wind-powered electricity generation exerted oneself under combination is just standby for capacity

with the negative standby capacity that supplies

expression formula 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) relatively

with

with

When

be greater than or

be greater than

time, representing that electric power system marginal capacity is not enough, described n load-wind-powered electricity generation exerted oneself to combine and by marginal capacity, do not retrained; When

be not more than

or

be not more than

time, representing that electric power system marginal capacity is abundant, described n load-wind-powered electricity generation exerted oneself to combine and passed through marginal capacity constraint;

(2-57) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the marginal capacity constraint ratio lambda of dissolving _rese, carry out the wind-powered electricity generation ability of dissolving of standby dimension and differentiate, λ _reseexpression formula is as follows:

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

Otherwise rotate back into step (2-2);

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

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

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

In formula (37),

refer to maximum adjustable the exerting oneself of hour level of i platform unit;

(2-62) determine that the annual wind-powered electricity generation ability of dissolving differentiates in collection Ω n the load-wind-powered electricity generation adjustable maximum output in the electric power system of combining 1 hour of exerting oneself, be designated as

expression formula is as follows:

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

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

with

When

being greater than at 0 o'clock, in a few days there are indivedual load-following capacities constraints of constantly running counter in expression, and described n load-wind-powered electricity generation exerted oneself to combine and by load-following capacity, do not retrained; When

equal at 0 o'clock, represent that in a few days all moment all meet load-following capacity constraint, described n load-wind-powered electricity generation exerted oneself to combine and passed through load-following capacity constraint;

(2-64) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the load-following capacity constraint ratio lambda of dissolving _foll, carry out the wind-powered electricity generation ability of dissolving of load-following capacity dimension and differentiate, λ f _ollexpression formula is as follows:

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

Otherwise rotate back into (2-2);

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

(2-71) when electric power system node exists outside district outside power transmission plan Shi，Ji district the power transmission hour level sequence of exerting oneself, be

(2-72) the circuit transmission capacity limits of establishing k bar interconnection is

electric power system is sent capacity limitation outside and is

expression formula is as follows:

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

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

(2-73) for the annual wind-powered electricity generation ability of dissolving, differentiate n load-wind-powered electricity generation in the collection Ω power transmission of exerting oneself outside combination ，Dang district and exert oneself when being greater than electric power system and sending capacity limitation outside, electric power system will be abandoned wind because network capacity retrains generation expression formula is as follows:

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

In formula (41),

for in t element;

When n the wind-powered electricity generation amount of abandoning that load-wind-powered electricity generation is exerted oneself and combined described in this

be 0 o'clock, represent that the combination of exerting oneself of described n load-wind-powered electricity generation passed through network capacity constraint; When described n load-wind-powered electricity generation exert oneself combination the wind-powered electricity generation amount of abandoning

be greater than at 0 o'clock, represent described n load-wind-powered electricity generation exert oneself combination by network capacity, do not retrain;

(2-74) whether judgement is differentiated the combination of exerting oneself of whole load-wind-powered electricity generations in collection Ω to the annual wind-powered electricity generation ability of dissolving and is calculated and completes, if complete, obtains the wind-powered electricity generation of this wind-powered electricity generation installation scale under the network capacity constraint ratio lambda of dissolving _grid, carry out the wind-powered electricity generation ability of dissolving of network delivery competence dimension and differentiate, λ _gridexpression formula is as follows:

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

Otherwise rotate back into (2-2);

2-8) according to the annual wind-powered electricity generation ability of dissolving, differentiate collection Ω, carry out usining 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:

(2-81) establish electric power system control parameter line vector

have 5 elements, characterize the constraint that electric power system is considered in differentiation wind-powered electricity generation is dissolved ability process; When the value of element is 1, represent differentiates wind-powered electricity generation and dissolve and consider the constraint of corresponding factor in ability process; When the value of element is 0, represent differentiates wind-powered electricity generation and dissolve and do not consider the constraint of corresponding factor in ability process;

the corresponding relation of middle element is: first element

corresponding peak modulation capacity, second element

corresponding fm capacity, the 3rd element

corresponding marginal capacity, the 4th element fast

corresponding load-following capacity, the 5th element

map network conveying capacity; When considering the constraint of whole factors,

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

expression formula is as follows:

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

In formula (43),

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

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

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

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

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

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

(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 as follows:

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

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

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

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

carry out the dissolve differentiation of ability of monthly wind-powered electricity generation,

expression formula is as follows:

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

In formula (45), N (Ω _i) expression set omega _iload-wind-powered electricity generation number of combinations of exerting oneself; Ω (n) represents that the annual wind-powered electricity generation ability of dissolving differentiates the combination of exerting oneself of n load-wind-powered electricity generation in collection Ω;

(2-93) by the day degree wind-powered electricity generation ability of dissolving, differentiate collection Ω _i,j, characterize the annual wind-powered electricity generation ability of dissolving and differentiate load-wind-powered electricity generation that in collection Ω, daily load curve is j days i month composite set of exerting oneself, i=1,2,3 ..., 12, j=1,2,3 ... N _i

(2-94) by the wind-powered electricity generation of the i j day month ratio of dissolving

carry out the dissolve differentiation of ability of day degree wind-powered electricity generation,

expression formula is as follows:

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

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