CN104079000B - A kind of grid power transmission nargin control method being applicable to large-scale wind power access - Google Patents

Abstract

The invention provides a kind of grid power transmission nargin control method being applicable to large-scale wind power access, described method comprises step 1: adopt mixed integer programming approach to carry out the optimization of security constraint Unit Combination and calculate, obtain the start-stop state and the plan of exerting oneself that meet the conventional power generation usage unit of target function and constraints; Step 2: adopt Probabilistic Load Flow method to quantize the impact of following multi-period wind-powered electricity generation randomness on transmission of electricity nargin, obtain the trend probability distribution scope of grid branch or transmission cross-section; Step 3: judge whether trend probability distribution scope meets the requirement of confidential interval, if do not meet, returns step 1, re-starts Unit Combination optimization and calculates, plan of exerting oneself described in adjustment.Compared with prior art, a kind of grid power transmission nargin control method being applicable to large-scale wind power access provided by the invention, taken into full account the randomness of wind-powered electricity generation, perfect short term security constrained dispatch plans the result of use in wind power integration area, enhances the enforceability of short-term plan.

Description

A kind of grid power transmission nargin control method being applicable to large-scale wind power access

Technical field

The present invention relates to a kind of transmission of electricity nargin control method, be specifically related to a kind of grid power transmission nargin control method being applicable to large-scale wind power access.

Background technology

The restriction of transmission cross-section generally can be subject to during wind power integration electrical network, when processing network constraint, traditional generation schedule is a few days ago based on deterministic plan trend, namely next day system loading prediction and bus load predict be deterministic, and the plan trend of next day is only by start-stop and the decision of exerting oneself of generating set, plan a few days ago can by optimizing unit start-stop and exerting oneself plan power flowcontrol at zone of reasonableness.

Along with the access scale of wind-powered electricity generation constantly expands, due to the uncertainty of wind power output, plan a few days ago trend generally can by the start-stop of conventional power generation usage unit, to exert oneself and the disturbance common factor of wind-powered electricity generation determined, therefore planning trend can with randomness to a certain degree, thus affect the Network Security Constraints effect of a few days ago planning, produce trend beyong contemplation and cross the border or transmission cross-section conveying nargin.Therefore need for wind-powered electricity generation reserves enough adjustment nargin in Short Term Generation Schedules, the such as single period needs to reserve enough spinning reserves and network power transmission channel, and multi-period coupling then needs normal power supplies to reserve climbing adjustment nargin.How to arrange short-term electricity generation operation plan, need to provide a kind of technical scheme, on the basis meeting electrical power system network safety constraint, optimize the start-stop of the conventional power generation usage units such as fired power generating unit and exert oneself, to dissolve wind power as far as possible, improve the grid branch in wind power integration area or the power control capabilities of transmission cross-section.

Summary of the invention

In order to meet the needs of prior art, the invention provides a kind of grid power transmission nargin control method being applicable to large-scale wind power access, comprising adjusting the start-stop state of conventional power generation usage unit and exerting oneself plan to control grid power transmission nargin, described method comprises:

Step 1: carry out the optimization of security constraint Unit Combination with mixed integer programming approach and calculate, obtains the start-stop state and the plan of exerting oneself that meet the conventional power generation usage unit of target function and constraints;

Step 2: by the Probabilistic Load Flow method based on cumulant and Gram-Charlier series expansion, the trend probability distribution scope of following multi-period grid branch or transmission cross-section during calculating wind power integration; And

Step 3: judge whether described trend probability distribution scope meets the requirement of confidential interval, if do not meet, returns step 1, re-starts described security constraint Unit Combination optimization and calculates, plan of exerting oneself described in adjustment.

Preferably, described in described step 1, target function is $F=\min [\underset{i=1}{\overset{N_T}{\mathrm{\Σ}}}\underset{t=1}{\overset{N_t}{\mathrm{\Σ}}}{B}_i(P_i\left(t\right),t)+\underset{i=1}{\overset{N_T}{\mathrm{\Σ}}}\underset{t=1}{\overset{N_t}{\mathrm{\Σ}}}{\mathrm{Cu}}_i\left(t\right)];$

Wherein, N _tfor generating set number, i is generating set sequence number, N _tfor time hop count, t is period sequence number, and every 15 minutes is a period, then hop count N time _t=96; P _it () to be t period generating set i meritorious goes out force value, Cu _it () is the machine that the opens expense of t period generating set i, B _i(P _it (), t) is the generating expense of t period generating set i;

Preferably, described t period generating set i meritorious go out force value P _it the computing formula of () is:

Wherein, u _i,jthe flag bit of t sectional charge tiny increment curve j section that () is t period generating set i, for t period generating set i sectional charge tiny increment curve j section initially go out force value, P _i,jt () is t period generating set i going out force value and initially going out force value in sectional charge tiny increment curve j section difference, N _lfor the hop count of sectional charge tiny increment curve, for the lower limit of exerting oneself of t period generating set i;

Described difference P _i,jt the restrictive condition of () is: described flag bit $\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(t\right)\≤1;$

If flag bit then generating set is open state, if flag bit for stopped status;

Preferably, the generating expense B of described t period generating set i _i(P _i(t), computing formula t) is:

Wherein, K _i,jt () is the slope of generating set i sectional charge tiny increment curve j section;

Preferably, the machine that the opens expense Cu of described t period generating set i _it the defining method of () is:

If $T{\left(t\right)}_i^{\mathrm{on}}\≥(T_{\min ,i}^{\mathrm{on}}+T_i^{\mathrm{cold}})+1,$ Then Cu _i(t)=Cu _i,c;

If $T{\left(t\right)}_i^{\mathrm{on}}\≥(T_{\min ,i}^{\mathrm{on}}+T_i^{\mathrm{warm}})+1$ And $T{\left(t\right)}_i^{\mathrm{on}}\≤(T_{\min ,i}^{\mathrm{on}}+T_i^{\mathrm{cold}}),$ Then Cu _i(t)=Cu _i,w;

If $T{\left(t\right)}_i^{\mathrm{on}}\≥\left(T_{\min ,i}^{\mathrm{on}}\right)+1$ And $T{\left(t\right)}_i^{\mathrm{on}}\≤(T_{\min ,i}^{\mathrm{on}}+T_i^{\mathrm{warm}}),$ Then Cu _i(t)=Cu _i,h;

Otherwise, Cu _i(t)=0;

Wherein, for the running time of generating set i, for the minimum running time of generating set i, for after minimum downtime to the time required for startup temperature, for after minimum downtime to the time required for cold start-up, Cu _i,c, Cu _i,wand Cu _i,hfor constant;

Preferably, the constraints in described step 1 comprises;

Power-balance constraints described P _loadt () is t period Load Prediction In Power Systems value, described N _gfor conventional power generation usage unit and Wind turbines and number;

Reserve Constraint condition is: described with be respectively positive and negative stand-by requirement, described in for t period generating set i exerts oneself higher limit; Described N _tfor conventional power generation usage unit number;

Generating set is exerted oneself P _it the constraints of () is:

Generating set load increase and decrease rate constraints is $\left\{\begin{array}{c}{P}_i\left(t\right)-P_i(t-1)\≤(2-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}(t-1))\stackrel{\‾}{P_i}\left(t\right)+P_i^{\mathrm{up}}\left(t\right),u_{i,j}\∈u_{\mathrm{thermal}}\\ P_i(t-1)-P_i\left(t\right)\≤(2-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}(t-1))\stackrel{\‾}{P_i}\left(t\right)+P_i^{\mathrm{down}}\left(t\right),u_{i,j}\∈u_{\mathrm{thermal}}\end{array}\right.,$ Described with in the t period, generating set i is in harmonious proportion the maximum output value lowered respectively;

Generating set minimum running time constraints be $\underset{n=t}{\overset{t+T_{\min ,i}^{\mathrm{on}}-1}{\mathrm{\Σ}}}\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(n\right)\≥T_{\min ,i}^{\mathrm{on}}(\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}(t-1)),u_{i,j}\∈u_{\mathrm{thermal}};$

Generating set minimum downtime constraints be $\underset{n=t}{\overset{t+T_{\min ,i}^{\mathrm{off}}-1}{\mathrm{\Σ}}}(1-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(n\right))\≥T_{\min ,i}^{\mathrm{off}}(\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}(t-1)-\underset{j=1}{\overset{N_L}{\mathrm{\Σ}}}{u}_{i,j}\left(t\right)),u_{i,j}\∈u_{\mathrm{thermal}};$

Network constraint condition is: with wherein, with be respectively minimum limit value and the threshold limit value of grid branch or Section Tidal Current of Power Transmission, with the minimum value of the described trend probability distribution scope in the t period of being respectively in confidential interval α and maximum;

Preferably, described step 2 comprises:

Step 2-1: adopt three parameter Weibull models to obtain wind speed probability density function;

Step 2-2: the meritorious output probability distribution function building Wind turbines according to Wind turbines active power characteristic and described wind speed probability density function; The meritorious output probability density function of Wind turbines is obtained according to described meritorious output probability distribution function;

Step 2-3: the meritorious output moment of the orign α obtaining Wind turbines according to described meritorious output probability distribution function and described meritorious output probability density function _r;

Step 2-4: according to described moment of the orign α _rcalculate each rank cumulant γ of Wind turbines active power _r;

Step 2-5: calculate each rank cumulant that each node of electrical network injects active power, described each rank cumulant is the described each rank cumulant γ obtained by the disturbance of different random variable _rand;

Step 2-6: each rank cumulant calculating the active power of each described grid branch or transmission cross-section, and the active power probability distribution of each described grid branch or transmission cross-section is obtained by Gram-Charlier series expansion;

Preferably, the probability density function of active power probability distribution described in described step 2-6 is

The cumulative distribution function of described active power probability distribution is $F_{\mathrm{cum}}\left(x\right)=\mathrm{\φ}\left(x\right)+\frac{c_1}{1!}{\mathrm{\φ}}^{\′}\left(x\right)+\frac{c_2}{2!}{\mathrm{\φ}}^{\′\′}\left(x\right)+\frac{c_3}{3!}{\mathrm{\φ}}^{\left(3\right)}\left(x\right)+\·\·\·;$

Wherein, be desired value be 0, standard deviation is the probability density function of the standardized normal distribution stochastic variable of 1;

be desired value be 0, standard deviation is the cumulative distribution function of the standardized normal distribution stochastic variable of 1;

Preferably, the minimum value of described trend probability distribution scope in confidential interval α for described cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1-α)/2;

The maximum of described trend probability distribution scope in confidential interval α for described cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1+ α)/2;

Preferably, if described in or do not meet the requirement of confidential interval α, then make grid branch or section tidal current meet the requirement of confidential interval α by the trend limit value of corresponding grid branch or transmission cross-section in adjustment security constraint Unit Combination, be specially:

The trend limit value of adjustment grid branch or Section Tidal Current of Power Transmission, adjustment amount Δ P comprises:

Downward amount $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{up}}>P_{\lim }^{\mathrm{up}};$ Rise amount $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{down}}<P_{\lim }^{\mathrm{down}};$

When grid branch or Section Tidal Current of Power Transmission need to adjust downwards, the higher limit of trend limit value is set to after return step 1;

When grid branch or Section Tidal Current of Power Transmission need to adjust upward, the lower limit of trend limit value is set to after return step 1.

Compared with immediate prior art, excellent effect of the present invention is:

1, in technical solution of the present invention, adopt the Probabilistic Load Flow method based on becoming invariant and Gram-Charlier series expansion, the trend probability distribution scope of grid branch or transmission cross-section during calculating wind power integration, the uncertainty quantizing following multi-period wind-powered electricity generation on the impact of transmission of electricity nargin, for dispatching of power netwoks and staff planners provide the border of electric power netting safe running under probability environment;

2, in technical solution of the present invention, judge whether the trend probability distribution scope of grid branch or transmission cross-section meets the requirement of confidential interval, if discontented foot readjusts the start-stop state of conventional power generation usage unit and the plan of exerting oneself, the grid power transmission nargin realized under large-scale wind power access controls, and has ensured safe operation of electric network;

3, a kind of grid power transmission nargin control method being applicable to large-scale wind power access provided by the invention, take into full account the randomness of wind-powered electricity generation, perfect short term security constrained dispatch plans the result of use in wind power integration area, enhances the enforceability of short-term plan;

4, a kind of grid power transmission nargin control method being applicable to large-scale wind power access provided by the invention, propose the close-loop control scheme that security constraint Unit Combination combines with the transmission of electricity nargin quantitative evaluation based on Probabilistic Load Flow method, the grid power transmission nargin achieved under large-scale wind power access controls, for wind-powered electricity generation provides enough receiving spaces, comprise power delivery passage and margin of safety, realize the target guaranteeing network security and make full use of regenerative resource.

Accompanying drawing explanation

Below in conjunction with accompanying drawing, the present invention is further described.

Fig. 1 is: a kind of grid power transmission nargin control method flow chart being applicable to large-scale wind power access in the embodiment of the present invention.

Embodiment

Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.

As shown in Figure 1, the concrete steps being applicable to the grid power transmission nargin control method of large-scale wind power access in the present embodiment are:

Step 1: the basic data obtaining electrical network, the probability distribution comprising electric network model, the parameters of power equipment, the operational factor of generating set and wind energy turbine set describes;

Electric network model comprises the topological connection relation of power equipment in electrical network and equipment room;

Power equipment parameter comprises the electric pressure, impedance, thermally-stabilised limit value etc. of circuit;

The operational factor of generating set comprises generating set and to exert oneself bound, unit creep speed etc.;

The probability distribution of wind energy turbine set describes the weibull model parameter etc. comprising wind energy turbine set.

Adopt mixed integer programming approach to carry out the optimization of security constraint Unit Combination to calculate, obtain the unit start-stop state and the unit output plan that meet target function and constraints; Now wind power output gets its desired value;

(1) minimum for target function with the expense of generating electricity, comprise the machine that opens expense and the operating cost of conventional power generation usage unit, in order to improve the receiving ability of wind-powered electricity generation, the corresponding expense of wind power generator group is set to 0;

Target function is $F=\min [\underset{i=1}{\overset{N_T}{\mathrm{\&Sigma;}}}\underset{t=1}{\overset{N_t}{\mathrm{\&Sigma;}}}{B}_i(P_i\left(t\right),t)+\underset{i=1}{\overset{N_T}{\mathrm{\&Sigma;}}}\underset{t=1}{\overset{N_t}{\mathrm{\&Sigma;}}}{\mathrm{Cu}}_i\left(t\right)];$ Wherein, N _tfor generating set number, i is generating set sequence number, N _tfor time hop count, t is period sequence number, and every 15 minutes is a period, hop count N during by 00:15 to 14:00 then _r=96; P _it () to be t period generating set i meritorious goes out force value, Cu _it () is the machine that the opens expense of t period generating set i, B _i(P _it (), t) is the generating expense of t period generating set i;

1.: t period generating set i meritorious go out force value P _it the computing formula of () is:

wherein, u _i,jthe flag bit of t sectional charge tiny increment curve j section that () is t period generating set i, for t period generating set i sectional charge tiny increment curve j section initially go out force value, P _i,j(t) for t period generating set i sectional charge tiny increment curve j section go out force value with difference, N _lfor the hop count of sectional charge tiny increment curve, for t period generating set i exerts oneself lower limit;

When application linear mixed integer programing method solves Optimization of Unit Commitment By Improved, require that target function is linear, but the unit cost curve usually obtained is all conic section form, for the unit cost curve of single hop or multistage conic section form, need to be approximately multistage linear to discount represent when directly calling linear mixed integer programing algorithm, i.e. sectional charge tiny increment curve; u _i,jthe flag bit of t sectional charge tiny increment curve j section that () is t period generating set i, namely shows unit i is in which section of sectional charge tiny increment curve, if unit i is in k section, and k≤N _l, then $\left\{\begin{array}{c}{u}_{i,j}\left(y\right)=1,j=k\\ u_{i,j}\left(t\right)=0,j\&NotEqual;k\end{array}\right..$

The restrictive condition of difference Pi, j (t) is: flag bit u _i,jt the restrictive condition of () is $\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)\&le;1;$ If $\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)=1$ Then generating set is open state, if $\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)=0$ For stopped status.

2.: the generating expense B of t period generating set i _i(P _i(t), computing formula t) is:

Wherein, K _i,jt () is for generating set i is at the slope of sectional charge tiny increment curve j section.

3.: the machine that the opens expense Cu of t period generating set i _it the defining method of () is:

Wherein, for running time, for the minimum running time of generating set i, for after minimum downtime to the time required for startup temperature, for after minimum downtime to the time required for cold start-up, Cu _i,c, Cu _i,wand Cu _i,hfor constant.

(2) constraints comprises power-balance constraints, Reserve Constraint condition, generating set units limits condition, generating set load increase and decrease rate constraints, generating set constraints minimum running time, generating set constraints minimum downtime and network constraint condition;

1.: power-balance constraints p _loadt () is t period Load Prediction In Power Systems value, N _gfor generating set number, comprise conventional power unit and Wind turbines;

2.: Reserve Constraint condition is: with be respectively positive and negative stand-by requirement, for t period generating set i exerts oneself higher limit; N _tfor conventional power generation usage unit number;

Positive and negative stand-by requirement is namely in order to prevent generating set catastrophic failure and load fluctuation from causing active power not enough, and electric power system needs the pondage possessed.

3.: generating set units limits condition

4.: generating set load increase and decrease rate constraints is:

$\left\{\begin{array}{c}{P}_i\left(t\right)-P_i(t-1)\&le;(2-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}(t-1))\stackrel{\&OverBar;}{P_i}\left(t\right)+P_i^{\mathrm{up}}\left(t\right),u_{i,j}\&Element;u_{\mathrm{thermal}}\\ P_i(t-1)-P_i\left(t\right)\&le;(2-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}(t-1))\stackrel{\&OverBar;}{P_i}\left(t\right)+P_i^{\mathrm{down}}\left(t\right),u_{i,j}\&Element;u_{\mathrm{thermal}}\end{array}\right.,$ with in the t period, generating set i is in harmonious proportion the maximum output value lowered respectively;

5.: generating set minimum running time constraints be:

$\underset{n=t}{\overset{t+T_{\min ,i}^{\mathrm{on}}-1}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(n\right)\&GreaterEqual;T_{\min ,i}^{\mathrm{on}}(\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}(t-1)),u_{i,j}\&Element;u_{\mathrm{thermal}};$

6.: generating set minimum downtime constraints be:

$\underset{n=t}{\overset{t+T_{\min ,i}^{\mathrm{off}}-1}{\mathrm{\&Sigma;}}}(1-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(n\right))\&GreaterEqual;T_{\min ,i}^{\mathrm{off}}(\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}(t-1)-\underset{j=1}{\overset{N_L}{\mathrm{\&Sigma;}}}{u}_{i,j}\left(t\right)),u_{i,j}\&Element;u_{\mathrm{thermal}};$

7.: network constraint condition is: $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{up}}\left(t\right)<P_{\lim }^{\mathrm{up}}$ With $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{down}}\left(t\right)>P_{\lim }^{\mathrm{down}};$

Wherein, with be respectively minimum limit value and the threshold limit value of grid branch or Section Tidal Current of Power Transmission, with the minimum value of the trend probability distribution scope in the t period of being respectively in confidential interval α and maximum.

Step 2: adopt the Probabilistic Load Flow method based on becoming invariant and Gram-Charlier series expansion, the trend probability distribution scope of following multi-period grid branch or transmission cross-section during calculating wind power integration, namely the random fluctuation of wind-powered electricity generation is on the impact of following day part electric power system tide;

Multi-periodly in the present embodiment correspond to the different planning cycles of electrical network, plan a few days ago usually with 15 minutes for the cycle, comprise 96 periods every day; Real-time plan usually with 5 minutes for the cycle, comprise 288 periods every day.

Step 2-1: adopt three parameter Weibull models to obtain the probability density function of wind speed; Location parameter in three parameter Weibull models is set as the on-site minimum windspeed of wind field, and wind speed probability density is:

$f_{\mathrm{wv}}\left(v_w\right)=\frac{c}{b}{\left(\frac{v_w-v_0}{b}\right)}^{c-1}\exp [-{\left(\frac{v_w-v_0}{b}\right)}^c]---\left(1\right)$

In formula (1), v _wfor wind speed, v ₀for location parameter;

for scale parameter, for reflecting the mean wind speed of wind energy turbine set, Γ is Gamma function;

for form parameter, μ _vfor mean wind speed, σ _vfor standard deviation.

Step 2-2: according to the active power characteristic of Wind turbines and the meritorious output probability distribution function of wind speed probability density function structure Wind turbines; According to the meritorious output probability density function of the meritorious output probability distributed acquisition Wind turbines of Wind turbines;

The near-linear Mathematical Modeling that structure wind speed is variable, generating set power output is dependent variable, incision wind speed v _cito rated wind speed v _rinterval between linear, then the actual active power of output P of Wind turbines _wwith specified active-power P _rpass be:

$P_w=\left\{\begin{array}{cc}0& v\&le;v_{\mathrm{ci}}\\ k_{1}v+k_2& v_{\mathrm{ci}}\&le;v\&le;v_r\\ P_r& v_r<v\&le;v_{\mathrm{co}}\\ 0& v>v_{\mathrm{co}}\end{array}\right.---\left(2\right)$

In formula (2), $k_1=\frac{P_r}{v_r-v_{\mathrm{ci}}},k_2=-k_1{v}_{\mathrm{ci}}.$

In the present embodiment, the active power characteristic of Wind turbines is such as formula shown in (2), and the probability density of stochastic variable wind speed such as formula shown in (1), thus can obtain the meritorious output probability distribution function of Wind turbines:

$F_{\mathrm{wp}}\left(P_w\right)={\&Integral;}_{v_o}^{v_{\mathrm{ci}}}{f}_{\mathrm{wv}}\left(v\right)\mathrm{dv}+{\&Integral;}_{v_{\mathrm{ci}}}^{\frac{P_w-k_2}{k_1}}{f}_{\mathrm{wv}}\left(v\right)\mathrm{dv}---\left(3\right)$

Calculate stochastic variable at incision wind speed v _cito rated wind speed v _rinterval in Wind turbines to gain merit output probability density function

$f_{\mathrm{wp}}\left(P_w\right)={F_{\mathrm{wp}}}^{\&prime;}\left(P_w\right)=\frac{c}{\mathrm{\&beta;}}{\left(\frac{P_w-a}{\mathrm{\&beta;}}\right)}^{c-1}\exp [-{\left(\frac{P_w-a}{\mathrm{\&beta;}}\right)}^c]---\left(4\right)$

Wherein, a=k ₁v ₀+ k ₂, β=k ₁b.

Step 2-3: the meritorious output moment of the orign α obtaining Wind turbines according to meritorious output probability distribution function and meritorious output probability density function _r; In the present embodiment, through type (3) and (4) obtain the meritorious output moment of the orign of Wind turbines:

${\mathrm{\&alpha;}}_r=\underset{k=0}{\overset{r}{\mathrm{\&Sigma;}}}\left(\begin{array}{c}r\\ k\end{array}\right)b^k{a}^{r-k}\mathrm{\&Gamma;}(1+\frac{k}{c});$ Wherein, $\left(\begin{array}{c}r\\ k\end{array}\right)=\frac{r(r-1)(r-2)\&CenterDot;\&CenterDot;\&CenterDot;(r-k+1)}{k!}$ It is the combination that r gets k.

Step 2-4: according to moment of the orign α _rcalculate Wind turbines gain merit export each rank cumulant γ _r; According to moment of the orign α in the present embodiment _rrelation with becoming invariant, obtains each rank cumulant γ that Wind turbines is exerted oneself _rfor:,

γ ₁＝α ₁，

${\mathrm{\&gamma;}}_2={\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1^2,$

${\mathrm{\&gamma;}}_3={\mathrm{\&alpha;}}_3-3{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2+2{\mathrm{\&alpha;}}_1^3,$

${\mathrm{\&gamma;}}_4={\mathrm{\&alpha;}}_4-3{\mathrm{\&alpha;}}_2^2-4{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_3+12{\mathrm{\&alpha;}}_1^2{\mathrm{\&alpha;}}_3-6{\mathrm{\&alpha;}}_1^4,$

${\mathrm{\&gamma;}}_5={\mathrm{\&alpha;}}_5-5{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_4-10{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_3+20{{\mathrm{\&alpha;}}_1}^2{\mathrm{\&alpha;}}_3+30{{\mathrm{\&alpha;}}_2}^2{\mathrm{\&alpha;}}_1-60{{\mathrm{\&alpha;}}_1}^3{\mathrm{\&alpha;}}_2+24a_1^5,$

$\begin{array}{c}{\mathrm{\&gamma;}}_6={\mathrm{\&alpha;}}_6-6{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_5-15{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_4+30{{\mathrm{\&alpha;}}_1}^2{\mathrm{\&alpha;}}_4+30{{\mathrm{\&alpha;}}_2}^2{\mathrm{\&alpha;}}_1-10{{\mathrm{\&alpha;}}_3}^2+120{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_3+120{{\mathrm{\&alpha;}}_1}^3{\mathrm{\&alpha;}}_3\\ +30a_2^3+270{\mathrm{\&alpha;}}_2^2{\mathrm{\&alpha;}}_1^2+360{\mathrm{\&alpha;}}_1^4{\mathrm{\&alpha;}}_2-120{\mathrm{\&alpha;}}_1^6\end{array},$

$\begin{array}{c}{\mathrm{\&gamma;}}_7={\mathrm{\&alpha;}}_7-7{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_6-21{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_5+42{{\mathrm{\&alpha;}}_1}^2{\mathrm{\&alpha;}}_5-35{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_4+210{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_4+210{{\mathrm{\&alpha;}}_1}^3{\mathrm{\&alpha;}}_4+140{{\mathrm{\&alpha;}}_3}^2{\mathrm{\&alpha;}}_1+210{{\mathrm{\&alpha;}}_2}^2{\mathrm{\&alpha;}}_3\\ -1260{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_1^2+840{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_1^4-630{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2^3+2520{\mathrm{\&alpha;}}_2^2{\mathrm{\&alpha;}}_1^3-2520{\mathrm{\&alpha;}}_1^5{\mathrm{\&alpha;}}_2+720{\mathrm{\&alpha;}}_1^7\end{array},$

$\begin{array}{c}{\mathrm{\&gamma;}}_8={\mathrm{\&alpha;}}_8-8{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_7-28{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_6-56{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_5+56{{\mathrm{\&alpha;}}_1}^2{\mathrm{\&alpha;}}_6+560{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_4{\mathrm{\&alpha;}}_1+336{\mathrm{\&alpha;}}_1{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_5-336{{\mathrm{\&alpha;}}_1}^3{\mathrm{\&alpha;}}_5\\ -35{{\mathrm{\&alpha;}}_4}^2+420{{\mathrm{\&alpha;}}_2}^2{\mathrm{\&alpha;}}_4-2520{{\mathrm{\&alpha;}}_1}^2{\mathrm{\&alpha;}}_2{\mathrm{\&alpha;}}_4+1680{{\mathrm{\&alpha;}}_1}^4{\mathrm{\&alpha;}}_4+560{{\mathrm{\&alpha;}}_3}^2{\mathrm{\&alpha;}}_2-1680{{\mathrm{\&alpha;}}_3}^2{{\mathrm{\&alpha;}}_1}^2\\ -5040{\mathrm{\&alpha;}}_3{{\mathrm{\&alpha;}}_2}^2{\mathrm{\&alpha;}}_1+13440{\mathrm{\&alpha;}}_3{\mathrm{\&alpha;}}_2{{\mathrm{\&alpha;}}_1}^3-6720{\mathrm{\&alpha;}}_3{{\mathrm{\&alpha;}}_1}^5-630{{\mathrm{\&alpha;}}_2}^4+10080{{\mathrm{\&alpha;}}_2}^3{{\mathrm{\&alpha;}}_1}^2-25200{{\mathrm{\&alpha;}}_2}^2{{\mathrm{\&alpha;}}_1}^4\\ -20160{\mathrm{\&alpha;}}_2{{\mathrm{\&alpha;}}_1}^6-5040{{\mathrm{\&alpha;}}_1}^8\end{array},$

Step 2-5: each rank cumulant calculating each node active power in electrical network; The character 1 of cumulant is utilized to calculate each rank cumulant of electrical network each node active power;

1.: the content of the character 1 of cumulant is:

If there be n mutually independent random variables x ⁽¹⁾, x ⁽²⁾..., x ⁽ⁿ⁾, and each own r rank cumulant (v=1,2 ..., r) exist, x is this n stochastic variable sum, then the r rank cumulant of stochastic variable x equals the r rank cumulant sum of each independent random variable, that is:

${\mathrm{\&gamma;}}_v={\mathrm{\&gamma;}}_v^{\left(1\right)}+{\mathrm{\&gamma;}}_v^{\left(2\right)}+\&CenterDot;\&CenterDot;\&CenterDot;+{\mathrm{\&gamma;}}_v^{\left(n\right)},(v=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,r).$

2.: in the present embodiment, the power flow equation of grid branch is:

A, V _i, V _jbe respectively the voltage of node i and node j, θ _ijfor the phase angle difference of branch road two ends node i j voltage, G _ijfor the real part of node admittance matrix element; If perunit value V _i=V _j=1, G _ij=0 and sin θ _ij=θ _ij, then DC power flow equation:

P＝Bθ(5)

Wherein, P is node injecting power vector, and θ is voltage phase angle vector, x _ijit is the reactance of ij branch road.

B, formal transformation is carried out to formula (5):

θ＝B ^-1P＝XP(6)

Wherein, X is the inverse matrix of B.

C, Branch Power Flow equation are:

P _Line＝Tθ(7)

Wherein, T is the relational matrix of Branch Power Flow and voltage phase angle; Formula (6) is substituted into formula (7), obtains Branch Power Flow equation;

P _Line＝TXP＝HP(8)

Wherein, H is be the injecting power of grid nodes and the relational matrix of grid branch trend;

3.: each rank cumulant γ of the injecting power disturbance utilizing the character 1 of cumulant to be caused by different random variable on each node _rbe added, try to achieve each rank cumulant of each node active power; In the present embodiment, stochastic variable is only Wind turbines and exerts oneself.

Step 2-6: each rank cumulant calculating the active power of each grid branch or transmission cross-section, and the active power probability distribution of each grid branch or transmission cross-section is obtained by Gram-Charlier series expansion;

1.: utilize the character 2 of cumulant to calculate each rank cumulant of every bar grid branch active power;

The content of the character 2 of a, cumulant is:

Stochastic variable y is the linear function of stochastic variable x, y=ax+b, for each rank cumulant of x, then each rank cumulant of stochastic variable y ${\mathrm{\&gamma;}}_v^y=\left\{\begin{array}{cc}a{\mathrm{\&gamma;}}_1^x+b& (v=1)\\ a^v{\mathrm{\&gamma;}}_v^x& (v>1)\end{array}\right.,(v=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,r);$

B, each rank cumulant of electrical network each node injecting power obtained by step 2-5 calculate each rank cumulant of every bar grid branch or transmission cross-section active power

Transmission cross-section is the passway for transmitting electricity of one or more grid branch composition, the cumulant of transmission cross-section be its member Tributary's cumulant and.

2.: the active power probability distribution being obtained each grid branch or transmission cross-section by Gram-Charlier series expansion;

Grid branch or stochastic variable ξ corresponding to transmission cross-section, its average is μ, and standard deviation is σ, then its normalization stochastic variable is x=(ξ-μ)/σ; .

The probability density function of active power probability distribution

The cumulative distribution function of active power probability distribution

Wherein, be desired value be 0, standard deviation is the probability density function of the standardized normal distribution stochastic variable of 1;

be desired value be 0, standard deviation is the cumulative distribution function of the standardized normal distribution stochastic variable of 1;

Constant coefficient c _rby each rank cumulant of each grid branch or transmission cross-section active power ask for:

c ₀＝1

c ₁＝c ₂＝0

$c_3=-\frac{{\mathrm{\&gamma;}}_3^y}{{\left({\mathrm{\&gamma;}}_2^y\right)}^{3/2}}$

$c_4=\frac{{\mathrm{\&gamma;}}_4^y+3{\left({\mathrm{\&gamma;}}_2^y\right)}^2}{{\left({\mathrm{\&gamma;}}_2^y\right)}^2}-3.$

$c_5=-\frac{{\mathrm{\&gamma;}}_5^y+10{\mathrm{\&gamma;}}_2^y{\mathrm{\&gamma;}}_3^y}{{\left({\mathrm{\&gamma;}}_2^y\right)}^{5/2}}+10\frac{{\mathrm{\&gamma;}}_3^y}{{\left({\mathrm{\&gamma;}}_2^y\right)}^{3/2}}$

(3) judge whether trend probability distribution scope meets the requirement of confidential interval, if do not meet, re-start conventional power unit Combinatorial Optimization and calculate, the plan of adjustment unit output;

The minimum value of trend probability distribution scope in confidential interval α for cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1-α)/2; The maximum of trend probability distribution scope in confidential interval α for cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1+ α)/2;

If or do not meet the requirement of confidential interval α, because network model is constant, the probability nature of Wind turbines is constant, only need adjust the desired value of grid branch or Section Tidal Current of Power Transmission, can meet the safe operation requirement under confidential interval α; The adjustment of grid branch or Section Tidal Current of Power Transmission can be realized by the trend limit value of corresponding grid branch or transmission cross-section in adjustment security constraint Unit Combination, is specially:

The trend limit value of a, adjustment grid branch or Section Tidal Current of Power Transmission, adjustment amount Δ P comprises:

Downward amount $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{up}}>P_{\lim }^{\mathrm{up}};$ Rise amount $P_{\lim }^{1-\mathrm{\&alpha;},\mathrm{down}}<P_{\lim }^{\mathrm{down}};$

B, when grid branch or Section Tidal Current of Power Transmission need to adjust downwards, the higher limit of trend limit value to be set to after re-start the optimization of security constraint Unit Combination calculate;

When grid branch or Section Tidal Current of Power Transmission need to adjust upward, the lower limit of trend limit value is set to after re-start the optimization of security constraint Unit Combination calculate.

(4) result is shown and is exported, and comprises the start-stop state of generating set, the plan of exerting oneself and grid branch and Section Tidal Current of Power Transmission curve.

Finally should be noted that: described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.

Claims (8)

1. be applicable to a grid power transmission nargin control method for large-scale wind power access, comprising adjusting the start-stop state of conventional power generation usage unit and exerting oneself plan to control grid power transmission nargin, it is characterized in that, described method comprises:

Step 1: carry out the optimization of security constraint Unit Combination with mixed integer programming approach and calculate, obtains the start-stop state and the plan of exerting oneself that meet the conventional power generation usage unit of target function and constraints;

Step 2: by the Probabilistic Load Flow method based on cumulant and Gram-Charlier series expansion, the trend probability distribution scope of following multi-period grid branch or transmission cross-section during calculating wind power integration; And

Step 3: judge whether described trend probability distribution scope meets the requirement of confidential interval, if do not meet, returns step 1, re-starts described security constraint Unit Combination optimization and calculates, plan of exerting oneself described in adjustment;

Described in described step 1, target function is $F=\min \&lsqb;\underset{i=1}{\overset{N_T}{\&Sigma;}}\underset{t=1}{\overset{N_t}{\&Sigma;}}{B}_i\left(P_i\left(t\right),t\right)+\underset{i=1}{\overset{N_T}{\&Sigma;}}\underset{t=1}{\overset{N_t}{\&Sigma;}}{\mathrm{Cu}}_i\left(t\right)\&rsqb;;$

Wherein, N _tfor generating set number, i is generating set sequence number, N _tfor time hop count, t is period sequence number, and every 15 minutes is a period, then hop count N time _t=96; P _it () to be t period generating set i meritorious goes out force value, Cu _it () is the machine that the opens expense of t period generating set i, B _i(P _it (), t) is the generating expense of t period generating set i;

Described t period generating set i meritorious go out force value P _it the computing formula of () is: $P_i\left(t\right)=u_{i,1}\left(t\right){\underset{\&OverBar;}{P}}_i\left(t\right)+P_{i,1}\left(t\right)+\underset{j=2}{\overset{N_L}{\&Sigma;}}\left(u_{i,j}\left(t\right)P_{i,j}^{seg}\left(t\right)+P_{i,j}\left(t\right)\right);$

Wherein, u _i,jthe flag bit of t sectional charge tiny increment curve j section that () is t period generating set i, for t period generating set i sectional charge tiny increment curve j section initially go out force value, P _i,jt () is t period generating set i going out force value and initially going out force value in sectional charge tiny increment curve j section difference, N _lfor the hop count of sectional charge tiny increment curve, p _i t () is the lower limit of exerting oneself of t period generating set i;

Described difference P _i,jt the restrictive condition of () is: $\left\{\begin{array}{c}{P}_{i,j}\left(t\right)\&GreaterEqual;0\\ P_{i,1}\left(t\right)\&le;u_{i,1}\left(t\right)\left(P_{i,1}^{seg}\right(t)-\underset{\&OverBar;}{P_i}(t\left)\right)\\ P_{i,j}\left(t\right)\&le;u_{i,j}\left(t\right)\left(P_{i,j}^{seg}\right(t)-P_{i,j-1}^{seg}(t\left)\right)\end{array}\right.;$ Described flag bit $\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)\&le;1;$

If flag bit then generating set is open state, if flag bit for stopped status.

2. the method for claim 1, is characterized in that, the generating expense B of described t period generating set i _i(P _i(t), computing formula t) is: $B_i\left(P_i\left(t\right),t\right)=B_i\left({\underset{\&OverBar;}{P}}_i\left(t\right),t\right)u_{i,1}\left(t\right)+K_{i,1}\left(t\right)P_{i,1}\left(t\right)+\underset{j=2}{\overset{N_L}{\&Sigma;}}\left(B_i\left(P_{i,j}^{seg}\left(t\right),t\right)u_{i,j}\left(t\right)+K_{i,j}\left(t\right)P_{i,j}\left(t\right)\right);$

Wherein, K _i,jt () is the slope of generating set i sectional charge tiny increment curve j section.

3. the method for claim 1, is characterized in that, the machine that the opens expense Cu of described t period generating set i _it the defining method of () is:

If $T{\left(t\right)}_i^{on}\&GreaterEqual;\left(T_{\min ,i}^{on}+T_i^{cold}\right)+1,$ Then Cu _i(t)=Cu _i,c;

If $T{\left(t\right)}_i^{on}\&GreaterEqual;\left(T_{\min ,i}^{on}+T_i^{warm}\right)+1$ And $T{\left(t\right)}_i^{on}\&le;\left(T_{\min ,i}^{on}+T_i^{cold}\right),$ Then Cu _i(t)=Cu _i,w;

If $T{\left(t\right)}_i^{on}\&GreaterEqual;\left(T_{\min ,i}^{on}\right)+1$ And $T{\left(t\right)}_i^{on}\&le;\left(T_{\min ,i}^{on}+T_i^{warm}\right),$ Then Cu _i(t)=Cu _i,h;

Otherwise, Cu _i(t)=0;

Wherein, for the running time of generating set i, for the minimum running time of generating set i, for after minimum downtime to the time required for startup temperature, for after minimum downtime to the time required for cold start-up, Cu _i,c, Cu _i,wand Cu _i,hfor constant.

4. the method for claim 1, is characterized in that, the constraints in described step 1 comprises;

Power-balance constraints described P _loadt () is t period Load Prediction In Power Systems value, described N _gfor conventional power generation usage unit and Wind turbines and number;

Reserve Constraint condition is: described with be respectively positive and negative stand-by requirement, described in for t period generating set i exerts oneself higher limit; Described N _tfor conventional power generation usage unit number;

Generating set is exerted oneself P _it the constraints of () is: ${\underset{\&OverBar;}{P}}_i\left(t\right)\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)\&le;P_i\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_i\left(t\right)\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right);$

Generating set load increase and decrease rate constraints is $\left\{\begin{array}{c}{P}_i\left(t\right)-P_i\left(t-1\right)\&le;\left(2-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t-1\right)\right){\stackrel{\&OverBar;}{P}}_i\left(t\right)+P_i^{up}\left(t\right),u_{i,j}\&Element;u_{thermal}\\ P_i\left(t-1\right)-P_i\left(t\right)\&le;\left(2-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t-1\right)\right){\stackrel{\&OverBar;}{P}}_i\left(t\right)+P_i^{down}\left(t\right),u_{i,j}\&Element;u_{thermal}\end{array}\right.,$ Described with in the t period, generating set i is in harmonious proportion the maximum output value lowered respectively;

Generating set minimum running time constraints be $\underset{n=t}{\overset{t+T_{\min ,i}^{on}-1}{\&Sigma;}}\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(n\right)\&GreaterEqual;T_{\min ,i}^{on}\left(\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t-1\right)\right),u_{i,j}\&Element;u_{thermal};$

Generating set minimum downtime constraints be $\underset{n=t}{\overset{t+T_{\min ,i}^{off}-1}{\&Sigma;}}\left(1-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(n\right)\right)\&GreaterEqual;T_{\min ,i}^{off}\left(\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t-1\right)-\underset{j=1}{\overset{N_L}{\&Sigma;}}{u}_{i,j}\left(t\right)\right),u_{i,j}\&Element;u_{thermal};$

Network constraint condition is: with wherein, with be respectively minimum limit value and the threshold limit value of grid branch or Section Tidal Current of Power Transmission, with the minimum value of the described trend probability distribution scope in the t period of being respectively in confidential interval α and maximum.

5. method as claimed in claim 4, it is characterized in that, described step 2 comprises:

Step 2-1: adopt three parameter Weibull models to obtain wind speed probability density function;

Step 2-2: the meritorious output probability distribution function building Wind turbines according to Wind turbines active power characteristic and described wind speed probability density function; The meritorious output probability density function of Wind turbines is obtained according to described meritorious output probability distribution function;

Step 2-3: the meritorious output moment of the orign α obtaining Wind turbines according to described meritorious output probability distribution function and described meritorious output probability density function _r;

Step 2-4: according to described moment of the orign α _rcalculate each rank cumulant γ of Wind turbines active power _r;

Step 2-5: calculate each rank cumulant that each node of electrical network injects active power, described each rank cumulant is the described each rank cumulant γ obtained by the disturbance of different random variable _rand;

Step 2-6: each rank cumulant calculating the active power of each described grid branch or transmission cross-section, and the active power probability distribution of each described grid branch or transmission cross-section is obtained by Gram-Charlier series expansion.

6. method as claimed in claim 5, it is characterized in that, the probability density function of active power probability distribution described in described step 2-6 is

The cumulative distribution function of described active power probability distribution is $F_{cum}\left(x\right)=\mathrm{\&phi;}\left(x\right)+\frac{c_1}{1!}{\mathrm{\&phi;}}^{\&prime;}\left(x\right)+\frac{c_2}{2!}{\mathrm{\&phi;}}^{\&prime;\&prime;}\left(x\right)+\frac{c_3}{3!}{\mathrm{\&phi;}}^{\left(3\right)}\left(x\right)+...;$

Wherein, be desired value be 0, standard deviation is the probability density function of the standardized normal distribution stochastic variable of 1;

The cumulative distribution function that φ (x) is desired value is 0, standard deviation is the standardized normal distribution stochastic variable of 1.

7. method as claimed in claim 6, is characterized in that,

The minimum value of described trend probability distribution scope in confidential interval α for described cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1-α)/2;

The maximum of described trend probability distribution scope in confidential interval α for described cumulative distribution function F _cumx trend value that () is corresponding when cumulative probability is (1+ α)/2.

8. method as claimed in claim 7, is characterized in that, if described in or do not meet the requirement of confidential interval α, then make grid branch or section tidal current meet the requirement of confidential interval α by the trend limit value of corresponding grid branch or transmission cross-section in adjustment security constraint Unit Combination, be specially:

The trend limit value of adjustment grid branch or Section Tidal Current of Power Transmission, adjustment amount Δ P comprises:

Downward amount rise amount

When grid branch or Section Tidal Current of Power Transmission need to adjust downwards, the higher limit of trend limit value is set to after return step 1;

When grid branch or Section Tidal Current of Power Transmission need to adjust upward, the lower limit of trend limit value is set to after return step 1.