CN105048491A - Multi-stage wind power accepted range calculating method based on unit combination and economic dispatching - Google Patents

Abstract

The invention discloses a multi-stage wind power accepted range calculating method based on unit combination and economic dispatching, comprising an annual planning stage, a monthly planning stage, and a current planning stage. A, in the annual planning stage, a mathematical model for solving the maximum wind power installed capacity which can be accepted by a region and the minimum wind power installed capacity which must be accessed to the region is established by using regional typical load curve data and based on an economic dispatching optimization method; B, in the monthly planning stage, a mathematical model for solving the maximum wind power which can be accepted by the region and the minimum wind power which must be accessed to the region is established by using monthly load prediction data and based on the economic dispatching optimization method; and C, in the current planning stage, a mathematical model for solving the maximum wind power which can be accepted by the region next day and the minimum wind power which must be accessed to the region next day is established by using recent load prediction data, recent wind power prediction data and a monthly thermal power unit start-stop optimization result and based on the economic dispatching optimization method.

Description

Multistage wind-powered electricity generation based on Unit Combination and economic dispatch receives range computation method

Technical field

The present invention relates to a kind of multistage wind-powered electricity generation based on Unit Combination and economic dispatch and receive range computation method.

Background technology

Increasingly serious along with energy and environment problem, environmentally friendly renewable resource receives publicity gradually.The inherent advantages such as wind energy is low with its operating cost, pollute less, rich reserves, potentiality to be exploited are huge, obtain in recent years and greatly develop, the randomness intrinsic due to nature apoplexy and fluctuation, make grid-connected wind-powered electricity generation have following deficiency:

1) strong uncertainty: namely wind power output is difficult to accurately predicting by air speed influence;

2) anti-peak-shaving capability: namely Wind turbines is exerted oneself and had negative correlation with system loading, especially load level are lower and wind power output is higher time can produce great problems;

3) power output of Wind turbines depends on the size of wind speed, and current predicting wind speed of wind farm level is very limited, and its predicated error is generally about 25% ~ 40%, and therefore the grid-connected stable operation to electric power system of large-scale wind power brings huge challenge.

The regulating power of existing energy resource structure is not enough, in order to balance the fluctuation of electrical network, the economy of system is deteriorated, even when wind-powered electricity generation is sent out greatly, system has to abandon wind, and along with the lasting raising of wind-powered electricity generation permeability in electrical network, it is day by day serious that electrical network abandons wind problem, therefore, the wind-powered electricity generation studying electrical network receives ability very important for the fail safe and economy improving electrical network.

In addition, receive wind-powered electricity generation ability to there is no clear and definite definition and the computational methods of standard about electrical network, for different field, the factor that assessment wind-powered electricity generation ability of receiving is considered is different, determines the difference of appraisal procedure.Traditional measurements electrical network receives ability normally from wind-powered electricity generation planning angle, considers the factor such as operational mode, perturbation scheme, and for the installed capacity of decision-making maximum wind, this kind of appraisal procedure is not suitable for short term scheduling and runs field; Some research methods comprise the analysis etc. based on peak regulation in addition, analyze, but be difficult to the validity and the practicality that ensure assessment result by setting up algebraic model to power grid wind ability of receiving.

Summary of the invention

For the problems referred to above, the invention provides a kind of multistage wind-powered electricity generation based on Unit Combination and economic dispatch and receive range computation method, for planning-monthly-multistage wind-powered electricity generation receiving range computation a few days ago, this scope comprises maximum and minimum value, receive ability significant to the wind-powered electricity generation analyzing electrical network, to the planning construction of electrical network and economic dispatch, there is reference value.

Term definition:

(1) the maximum installed capacity of wind-driven power that can receive refers to by fired power generating unit start and stop, unit output etc., the maximum wind installed capacity that this electrical network can be received;

(2) the minimum installed capacity of wind-driven power that must access refers to when fired power generating unit installation in this area's can not meet workload demand, in order to ensure the equilibrium of supply and demand, and the minimum installed capacity of wind-driven power that must access;

(3) the monthly maximum wind-powered electricity generation electricity that can receive refers to that this electrical network is at this monthly maximum wind electricity that can receive by fired power generating unit start and stop, unit output etc.;

(4) the monthly minimum wind-powered electricity generation electricity that must access refer to when this area's fired power generating unit exert oneself can not meet workload demand time, in order to ensure the equilibrium of supply and demand, this monthly minimum wind-powered electricity generation electricity that must access;

(5) next day, the maximum wind-powered electricity generation electricity that can receive referred to by fired power generating unit start and stop, unit output etc., the maximum wind electricity that this electrical network can be received next day;

(6) next day the minimum wind-powered electricity generation electricity that must access refer to when this area's fired power generating unit next day exert oneself can not meet workload demand time, in order to ensure the equilibrium of supply and demand, next day the minimum wind-powered electricity generation electricity that must access.

For realizing above-mentioned technical purpose, reach above-mentioned technique effect, the present invention is achieved through the following technical solutions:

Multistage wind-powered electricity generation based on Unit Combination and economic dispatch receives range computation method, it is characterized in that, comprises annual planning stage, monthly planning stage and planning stage a few days ago, is respectively:

A: in the annual planning stage:

A01: adopt regional typical load curve data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access;

A02: solving model, obtains the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access respectively;

B: in the monthly planning stage:

B01: adopt monthly load prediction data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access;

B02: solving model, obtains the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access respectively;

C: in planning stage a few days ago:

C01: adopt load prediction data, a few days ago wind-powered electricity generation prediction data, monthly fired power generating unit start and stop optimum results a few days ago, based on economic dispatch optimization method, set up solve this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access Mathematical Modeling;

C02: solving model, obtain respectively this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access.

Preferably, in steps A 01, the model of the maximum installed capacity of wind-driven power that can receive is:

Target function:

${\mathrm{maxP}}_{W,Install}^{up}---\left(1\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t},\left(t=1,\mathrm{...},NT\right)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{\mathrm{Gi},t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{up}---\left(8\right)$

Wherein: formula (1) is the receivability installed capacity of wind-driven power upper limit, and NT is the time hop count comprised research cycle, and NG is the fired power generating unit number of units of system, and NW is the Wind turbines number of units of system, P _{wi, t}for period t receivability wind power output value, for all the period of time electrical network receivability installed capacity of wind-driven power upper limit, P _{gi, t}for fired power generating unit i exerting oneself at moment t, P _{wj, t}for wind energy turbine set _jexerting oneself of moment t, P _l,tfor system is at the workload demand of moment t, P _{line, t}for the interconnection plan at moment t, for the minimum technology of fired power generating unit i is exerted oneself, for the maximum technology of fired power generating unit i is exerted oneself, μ _i,tfor conventional power unit i is in the start and stop mode of moment t, μ _i,t=1 start, μ _i,t=0 shuts down, for the rising climbing rate of fired power generating unit i limits, for the decline climbing rate of fired power generating unit i limits, λ ₁, λ ₂for the load reserve factor of system, for the minimum available machine time of unit i, for the minimum downtime of unit i, for unit i is in the continuous available machine time of period t-1, for unit i is in the continuous downtime of period t-1, P _{flow, l, t}for transmission line l is at the DC power flow of moment t, for the DC power flow of transmission line l limits.

The model of the minimum installed capacity of wind-driven power that must access is:

Target function:

${\mathrm{minP}}_{W,Install}^{dn}---\left(9\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{Gi,t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{dn}---\left(10\right)$

Wherein, formula (9) is accessible installed capacity of wind-driven power lower limit, for installed capacity of wind-driven power lower limit.

The invention has the beneficial effects as follows:

This method is based on Unit Combination and economic dispatch, in planning-monthly-multistage establishes the Optimized model being target with installed capacity of wind-driven power, wind-powered electricity generation electricity to the maximum respectively a few days ago, adopt mixed integer programming Algorithm for Solving, contribute to the level that becomes more meticulous improving dispatching of power netwoks.Reference can be provided when wind-powered electricity generation planning construction, the grid-connected risk that electric power netting safe running is brought of large-scale wind power can be taken precautions against in advance, guidance can be provided for dispatcher carries out generation schedule formulation a few days ago, improve fail safe and economy that large-scale wind power gets involved rear electrical network.

Embodiment

Below in conjunction with specific embodiment, technical solution of the present invention is described in further detail, can better understand the present invention to make those skilled in the art and can be implemented, but illustrated embodiment is not as a limitation of the invention.

Multistage wind-powered electricity generation based on Unit Combination and economic dispatch receives range computation method, comprise annual planning stage, monthly planning stage and planning stage a few days ago, power grid wind of the present invention is received ability optimization evaluation method to propose research wind-powered electricity generation and is received interval, mainly based on Unit Combination and economic dispatch technology, maximum or minimum for optimization aim with wind power output sum, set up Optimization Solution model, solve model by business software (ILOG/CPLEX), the wind-powered electricity generation obtaining electrical network receives capability result.

Below each planning stage is described in detail:

A: in the annual planning stage:

A01: adopt regional typical load curve data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access;

A02: solving model, obtains the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access respectively.

Year maximumly can receive installed capacity of wind-driven power model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable; To maximize installed capacity of wind-driven power for optimization aim; Constraints comprise active power balance constraint, fired power generating unit exert oneself bound constraint, fired power generating unit Climing constant, stand-by requirement constraint, the minimum start-off time constraints of fired power generating unit, Line Flow retrain, wind power range constraint.Preferably, Optimized model is as follows:

Target function:

${\mathrm{maxP}}_{W,Install}^{up}---\left(1\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,...,NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{\mathrm{Gi},t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{up}---\left(8\right)$

Wherein: formula (1) is the receivability installed capacity of wind-driven power upper limit, and NT is the time hop count comprised research cycle, and NG is the fired power generating unit number of units of system, and NW is the Wind turbines number of units of system, P _{wi, t}for period t receivability wind power output value, for all the period of time electrical network receivability installed capacity of wind-driven power upper limit, P _{gi, t}for fired power generating unit i exerting oneself at moment t, P _{wj, t}for wind energy turbine set _jexerting oneself of moment t, P _l,tfor system is at the workload demand of moment t, P _{line, t}for the interconnection plan at moment t, for the minimum technology of fired power generating unit i is exerted oneself, for the maximum technology of fired power generating unit i is exerted oneself, μ _i,tfor conventional power unit i is in the start and stop mode of moment t, μ _i,t=1 start, μ _i,t=0 shuts down, for the rising climbing rate of fired power generating unit i limits, for the decline climbing rate of fired power generating unit i limits, λ ₁, λ ₂for the load reserve factor of system, for the minimum available machine time of unit i, for the minimum downtime of unit i, for unit i is in the continuous available machine time of period t-1, for unit i is in the continuous downtime of period t-1, P _{flow, l, t}for transmission line l is at the DC power flow of moment t, for the DC power flow of transmission line l limits.

It should be noted that: in above-mentioned Optimized model, decision variable to be optimized is Unit Commitment state and size of exerting oneself, wherein existing continuous variable has discrete variable again, the Linear Constraints such as existing power-balance constraint, unit ramp loss in constraints, comprise again the Nonlinear Constraints such as the minimum start-off time constraints of conventional power unit, also will consider the coupling in different time sections such as conventional power unit minimum start-stop time and Climing constant, therefore Optimized model belongs to multivariable, Nonlinear Mixed Integer Programming Problem simultaneously.Because ILOG/CPLEX can only solve linear mixed-integer programming model, before if therefore call, ILOG/CPLEX solves the Optimized model set up herein, the non-linear factor in model must be carried out linearisation, result is as follows:

$\left\{\begin{array}{cc}{\mathrm{\μ}}_{i,t}=1,& 1\≤t\≤U_i\\ \underset{\mathrm{\τ}=t}{\overset{t+T_i^{on}-1}{\Σ}}{\mathrm{\μ}}_{i,\mathrm{\τ}}\≥T_i^{on}\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)& U_i\≤t\≤NT-T_i^{on}+1\\ \underset{\mathrm{\τ}=t}{\overset{NT}{\Σ}}\[{\mathrm{\μ}}_{i,\mathrm{\τ}}-\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\]\≥0& NT-T_i^{on}+2\≤t\≤NT\end{array}\right.---\left(I\right)$

$\left\{\begin{array}{cc}{\mathrm{\μ}}_{i,t}=0,& 1\≤t\≤D_i\\ \underset{\mathrm{\τ}=t}{\overset{t+T_i^{off}-1}{\Σ}}(1-{\mathrm{\μ}}_{i,\mathrm{\τ}})\≥T_i^{\mathrm{off}}\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)& D_i\≤t\≤NT-T_i^{off}+1\\ \underset{\mathrm{\τ}=t}{\overset{NT}{\Σ}}\[1-{\mathrm{\μ}}_{i,\mathrm{\τ}}-\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\]\≥0& NT-T_i^{off}+2\≤t\≤NT\end{array}\right.---\left(II\right)$

The linearisation that formula (I) retrains for minimum boot running time, the linearisation that formula (II) retrained for minimum downtime.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain maximum receiving installed capacity of wind-driven power optimal solution be

Minimumly must access installed capacity of wind-driven power model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable; To minimize installed capacity of wind-driven power for optimization aim, preferably, Optimized model is as follows:

Target function:

${\mathrm{minP}}_{W,Install}^{dn}---\left(9\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,...,NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{\mathrm{Gi},t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{dn}---\left(10\right)$

Wherein, formula (9) is accessible installed capacity of wind-driven power lower limit, for installed capacity of wind-driven power lower limit.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain minimum access installed capacity of wind-driven power optimal solution be

B: in the monthly planning stage:

B01: adopt monthly load prediction data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access;

B02: solving model, obtains the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access respectively.

Monthlyly maximumly can receive wind-powered electricity generation electricity model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable, with at this monthly maximum wind electricity that can receive for optimization aim; Constraints comprise active power balance constraint, fired power generating unit exert oneself bound constraint, fired power generating unit Climing constant, stand-by requirement retrain, fired power generating unit start-off time constraints, Line Flow retrain, installed capacity of wind-driven power retrain.Preferably, Optimized model is as follows:

Target function:

${\mathrm{maxP}}_{Wm,Sum}^{up}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(11\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,...,NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{Gi,t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤{\stackrel{\‾}{P}}_{Wi,install}---\left(12\right)$

Wherein, formula (11) is the monthly wind-powered electricity generation receiving upper limit, for monthly wind-powered electricity generation receives the capacity sum upper limit, for the installed capacity of known power grid wind field i; NT is the time hop count comprised research cycle, and generally speaking, in the monthly planning stage, every 4h gets a period, one day 6 period, and the numerical value being multiplied by that namely of that month number of days is NT.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain the maximum receiving of monthly wind-powered electricity generation optimal solution be

Monthlyly minimumly must access wind-powered electricity generation electricity model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable; With this monthly minimum wind-powered electricity generation electricity that must receive for optimization aim, preferably, Optimized model is as follows:

Target function:

${\mathrm{minP}}_{Wm,Sum}^{dn}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(13\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{Gi,t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

$P_{Wi,t}\≤{\stackrel{\‾}{P}}_{Wi,install}\left(12\right).$

Formula (13) is monthly wind power integration lower limit, for monthly wind-powered electricity generation receives capacity sum lower limit.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain that monthly wind-powered electricity generation is minimum must receive electricity optimal solution be

C: in planning stage a few days ago:

C01: adopt load prediction data, a few days ago wind-powered electricity generation prediction data, monthly fired power generating unit start and stop optimum results a few days ago, based on economic dispatch optimization method, set up solve this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access Mathematical Modeling;

C02: solving model, obtain respectively this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access.

Next day is maximum can receive wind-powered electricity generation electricity model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable, the maximum wind electricity can received with next day is optimization aim; Constraints comprises active power balance constraint, fired power generating unit exerts oneself bound constraint, fired power generating unit Climing constant, stand-by requirement constraint, the minimum start-off time constraints of fired power generating unit, Line Flow retrains, wind-powered electricity generation dopes force constraint a few days ago.Preferably, Optimized model is as follows:

Target function:

${\mathrm{maxP}}_{Wd,Sum}^{up}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(14\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,...,NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{Gi,t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,...,NT)---\left(7\right)$

P _Wi,t≤P _Wi,forecast(15)

Wherein, formula (14) is the wind-powered electricity generation receiving next day upper limit, for wind-powered electricity generation next day receives the capacity sum upper limit, P _{wi, forecast}for wind energy turbine set i prediction is exerted oneself; NT is the time hop count comprised research cycle, and generally speaking, in planning stage a few days ago, every 15min gets a period, and one day totally 96 period, namely the numerical value of NT is 96.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain the maximum receiving of wind-powered electricity generation next day optimal solution be

Minimumly must access wind-powered electricity generation electricity model: this model with the Unit Commitment of fired power generating unit, fired power generating unit go out activity of force, wind energy turbine set go out activity of force for decision variable; With next day the minimum wind-powered electricity generation electricity that must receive for optimization aim, preferably, Optimized model is as follows:

Target function:

${\mathrm{minP}}_{Wd,Sum}^{dn}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(16\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,...,NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,...,NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT\right)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi,t}-P_{Gi}^{\min }\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}\left(P_{Gi}^{\max }-P_{Gi,t}\right){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.\left(t=1,\mathrm{...},NT\right)---\left(5\right)$

$\left\{\begin{array}{c}\left(X_{i,t-1}^{on}-T_i^{on}\right)\·\left({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t}\right)\≥0\\ \left(X_{i,t-1}^{off}-T_i^{off}\right)\·\left({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1}\right)\≥0\end{array}\right.\left(i=1,\mathrm{...},NG;t=1,\mathrm{...}NT\right)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow.l}^{\max }(t=1,...,NT)---\left(7\right)$

P _Wi,t≤P _Wi,forecast(15)

Wherein, formula (16) is wind-powered electricity generation receiving next day lower limit, for wind-powered electricity generation next day receives capacity sum lower limit.

ILOG/CPLEX can be adopted to solve above-mentioned inearized model, obtain the minimum access of wind-powered electricity generation next day optimal solution be

The present invention can under planning-monthly-multistage operation of power networks a few days ago, and the wind-powered electricity generation of dynamic analysis electrical network receives ability, and analyzes various factors in electrical network and wind-powered electricity generation is received to the impact of ability.To change single factors for principle, analysis load, fired power generating unit minimum load, fired power generating unit maximum output, fired power generating unit minimum start-stop time, fired power generating unit Climing constant, power network line transmittability receive the impact of ability bound on power grid wind respectively, the conclusion finally drawn is: suitably increase network load, reduction fired power generating unit minimum technology is exerted oneself and minimum start-stop time, the maximum technology of increase fired power generating unit are exerted oneself and Climing constant limits, improves power network line transmittability etc., be conducive to electrical network and receive more wind-powered electricity generation.

The present invention carries out under actual electric network data, based on Unit Combination and economic dispatch, take into full account and affect the various factors that electrical network receives wind-powered electricity generation, establish the Optimized model being target with wind power output to the maximum, call the mixed integer programming Algorithm for Solving in business software (ILOG/CPLEX), optimized evaluation planning-monthly-a few days ago many time periods wind-powered electricity generation receive ability, contribute to when Electric Power Network Planning is built for wind power integration provides reference, take precautions against the grid-connected risk that electric power netting safe running is brought of large-scale wind power in advance, guidance is provided for dispatcher carries out generation schedule formulation, improve fail safe and economy that large-scale wind power gets involved rear electrical network.

These are only the preferred embodiments of the present invention; not thereby the scope of the claims of the present invention is limited; every utilize description of the present invention to do equivalent structure or equivalent flow process conversion; or be directly or indirectly used in the technical field that other are relevant, be all in like manner included in scope of patent protection of the present invention.

Claims (8)

1. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch receives range computation method, it is characterized in that, comprises annual planning stage, monthly planning stage and planning stage a few days ago, is respectively:

A: in the annual planning stage:

A01: adopt regional typical load curve data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access;

A02: solving model, obtains the maximum installed capacity of wind-driven power that can receive in this area and the minimum installed capacity of wind-driven power that must access respectively;

B: in the monthly planning stage:

B01: adopt monthly load prediction data, based on economic dispatch optimization method, sets up the Mathematical Modeling solving the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access;

B02: solving model, obtains the monthly maximum wind-powered electricity generation electricity that can receive in this area and the monthly minimum wind-powered electricity generation electricity that must access respectively;

C: in planning stage a few days ago:

C01: adopt load prediction data, a few days ago wind-powered electricity generation prediction data, monthly fired power generating unit start and stop optimum results a few days ago, based on economic dispatch optimization method, set up solve this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access Mathematical Modeling;

C02: solving model, obtain respectively this area next day the maximum wind-powered electricity generation electricity that can receive and next day the minimum wind-powered electricity generation electricity that must access.

2. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 1 receives range computation method, and it is characterized in that, in steps A 01, the model of the maximum installed capacity of wind-driven power that can receive is:

Target function:

${\mathrm{maxP}}_{W,Install}^{up}---\left(1\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Ci}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{up}---\left(8\right)$

Wherein: formula (1) is the receivability installed capacity of wind-driven power upper limit, and NT is the time hop count comprised research cycle, and NG is the fired power generating unit number of units of system, and NW is the Wind turbines number of units of system, P _{wi, t}for period t receivability wind power output value, for all the period of time electrical network receivability installed capacity of wind-driven power upper limit, P _{gi, t}for fired power generating unit i exerting oneself at moment t, P _{wj, t}for wind energy turbine set _jexerting oneself of moment t, P _l,tfor system is at the workload demand of moment t, P _{line, t}for the interconnection plan at moment t, for the minimum technology of fired power generating unit i is exerted oneself, for the maximum technology of fired power generating unit i is exerted oneself, μ _i,tfor conventional power unit i is in the start and stop mode of moment t, μ _i,t=1 start, μ _i,t=0 shuts down, for the rising climbing rate of fired power generating unit i limits, for the decline climbing rate of fired power generating unit i limits, λ ₁, λ ₂for the load reserve factor of system, for the minimum available machine time of unit i, for the minimum downtime of unit i, for unit i is in the continuous available machine time of period t-1, for unit i is in the continuous downtime of period t-1, P _{flow, l, t}for transmission line l is at the DC power flow of moment t, for the DC power flow of transmission line l limits.

3. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 2 receives range computation method, and it is characterized in that, in steps A 01, the model of the minimum installed capacity of wind-driven power that must access is:

Target function:

${\mathrm{minP}}_{W,Install}^{dn}---\left(9\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }\≤{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Gi}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

$P_{Wi,t}\≤P_{W,Install}^{dn}---\left(10\right)$

Wherein, formula (9) is accessible installed capacity of wind-driven power lower limit, for installed capacity of wind-driven power lower limit.

4. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 1 receives range computation method, and it is characterized in that, in step B01, the model of the monthly maximum wind-powered electricity generation electricity that can receive is:

Target function:

${\mathrm{maxP}}_{Wm,Sum}^{up}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(11\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Gi}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

$P_{Wi,t}\≤{\stackrel{\‾}{P}}_{Wi,install}---\left(12\right)$

Wherein, formula (11) is the monthly wind-powered electricity generation receiving upper limit, for monthly wind-powered electricity generation receives the capacity sum upper limit, for the installed capacity of known power grid wind field i; NT is the time hop count comprised research cycle.

5. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 4 receives range computation method, and it is characterized in that, in step B01, the model of the monthly minimum wind-powered electricity generation electricity that must access is:

Target function:

${\mathrm{minP}}_{Wm,Sum}^{dn}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(13\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{up}\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Gi}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

$P_{Wi,t}\≤{\stackrel{\‾}{P}}_{Wi,install}---\left(12\right).$

6. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 1 receives range computation method, and it is characterized in that, in step C01, next day, the model of the maximum wind-powered electricity generation electricity that can receive was: target function:

${\mathrm{maxP}}_{Wd,Sum}^{up}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(14\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Gi}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

P _Wi,t≤P _Wi,forecast(15)

Wherein, formula (14) is the wind-powered electricity generation receiving next day upper limit, for wind-powered electricity generation next day receives the capacity sum upper limit, P _{wi, forecast}for wind energy turbine set i prediction is exerted oneself; NT is the time hop count comprised research cycle.

7. the multistage wind-powered electricity generation based on Unit Combination and economic dispatch according to claim 6 receives range computation method, and it is characterized in that, in step C01, next day, the model of the minimum wind-powered electricity generation electricity that must access was:

Target function:

${\mathrm{minP}}_{Wd,Sum}^{dn}=\underset{i=1}{\overset{NW}{\Σ}}\underset{t=1}{\overset{NT}{\Σ}}{P}_{Wi,t}---\left(16\right)$

Constraints:

$\underset{i=1}{\overset{NG}{\Σ}}{P}_{Gi,t}+\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}=P_{L,t}+P_{line,t}(t=1,\mathrm{...},NT)---\left(2\right)$

$P_{Gi}^{\min }{\mathrm{\μ}}_{i,t}\≤P_{Gi,t}\≤P_{Gi}^{\max }{\mathrm{\μ}}_{i,t}(t=1,\mathrm{...},NT)---\left(3\right)$

$\left\{\begin{array}{c}{P}_{Gi,t}-P_{Gi,t-1}\≤{\mathrm{\ΔP}}_{Gi}^{up}\\ P_{Gi,t-1}-P_{Gi,t}\≤{\mathrm{\ΔP}}_{Gi}^{dn}\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(4\right)$

$\left\{\begin{array}{c}\underset{i=1}{\overset{NG}{\Σ}}(P_{Gi,t}-P_{Gi}^{\min }){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\\ \underset{i=1}{\overset{NG}{\Σ}}(P_{Gi}^{\max }-P_{Gi,t}){\mathrm{\μ}}_{i,t}\≥{\mathrm{\λ}}_1{P}_{L,t}+{\mathrm{\λ}}_2\underset{j=1}{\overset{NW}{\Σ}}{P}_{Wj,t}\end{array}\right.(t=1,\mathrm{...},NT)---\left(5\right)$

$\left\{\begin{array}{c}(X_{i,t-1}^{on}-T_i^{on})\·({\mathrm{\μ}}_{i,t-1}-{\mathrm{\μ}}_{i,t})\≥0\\ (X_{i,t-1}^{off}-T_i^{off})\·({\mathrm{\μ}}_{i,t}-{\mathrm{\μ}}_{i,t-1})\≥0\end{array}\right.(i=1,\mathrm{...},NG;t=1,\mathrm{...},NT)---\left(6\right)$

$P_{flow,l,t}\≤P_{flow,l}^{\max }(t=1,\mathrm{...},NT)---\left(7\right)$

P _Wi,t≤P _Wi,forecast(15)

Wherein, formula (16) is wind-powered electricity generation receiving next day lower limit, for wind-powered electricity generation next day receives capacity sum lower limit.

8. according to claim 1 ?the multistage wind-powered electricity generation based on Unit Combination and economic dispatch described in 7 any one receive range computation method, it is characterized in that, adopt ILOG/CPLEX solving model.