CN109165785A - The method for improving extreme weather environment apparatus for lower wind utilization rate is dispatched using Risk Constraint - Google Patents
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Abstract
The invention discloses a kind of methods dispatched using Risk Constraint and improve extreme weather environment apparatus for lower wind utilization rate, by rapid starting/stopping unit, conventional power unit and Wind turbine compositional modeling are at mixed-integer programming model, the mixed-integer programming model is by scheduling model a few days ago, scheduling model and real-time cutting load model composition before hour, three models are subjected to sequential solution under Risk Constraint method, obtain global optimum's strategy in entire scheduling slot, then sequential scene tree is generated, mixed-integer programming model is solved using branch-and-bound and primal dual interior point method, obtain optimal unit combination scheme, it is utilized in wind electricity generating and finds optimal equalization point between potential risk.For routine dispactching method, method used in the present invention can be adjusted scheduling strategy with the update of weather forecasting information, therefore can avoid the premature excision of blower, improve wind power utilization.
Description
Technical field
The present invention relates to Operation of Electric Systems and dispatching technique field, and in particular to a kind of to be improved using Risk Constraint scheduling
The method of extreme weather environment apparatus for lower wind utilization rate.
Background technique
In recent years, the ratio of wind-power electricity generation was continuously improved, and economic and environment-friendly feature brings many to users
Benefit alleviates the energy shortage problem that the mankind are faced to a certain extent, at the same decrease the use of fossil energy to
The harm of environment bring.But influence of the wind-power electricity generation vulnerable to weather environment, it is crossed and is left early due to blower under extreme weather environment
Cause its utilization rate relatively low out.Therefore, improving wind power utilization rate becomes matter of utmost importance to be solved at this stage.
Dispatching method at this stage mainly has stochastic programming and advanced scheduling based on scene, the stochastic programming based on scene
Have the following disadvantages: that the growth of exponential form is presented with the increase of the quantity of scene for the complexity 1. calculated；2. not making
With continuous probability density function, it is possible to will lead to the loss of some important informations；3. its operate decision be it is static, fail
Reply random scene well.Advanced scheduling has the following disadvantages: 1. it is suitable for handling short-term traffic control problem, uncomfortable
For prolonged traffic control problem；2. its obtained solution is not globally optimal solution.
Summary of the invention
The technical problem to be solved by the present invention is to overcome above-mentioned the shortcomings of the prior art, provide a kind of using risk
Constrained dispatch improve extreme weather environment apparatus for lower wind utilization rate method, this method by the weather forecasting information of continuous renewal come
The operation reserve for formulating unit, avoids Wind turbine from exiting too early, to improve wind power utilization rate.
For achieving the above object, the present invention adopts the following technical scheme:
A method of it is dispatched using Risk Constraint and improves extreme weather environment apparatus for lower wind utilization rate, comprising the following steps:
Step 1: by rapid starting/stopping unit, conventional power unit and Wind turbine compositional modeling at mixed-integer programming model, institute
Model is stated by scheduling model a few days ago, scheduling model and real-time cutting load model form before hour；By three models in Risk Constraint
Sequential solution is carried out under method, obtains global optimum's strategy in entire scheduling slot；
Step 2: generating sequential scene tree, and the scene tree is divided into the scene of scheduling phase, scheduling phase before hour a few days ago
Scene and the scene in Real-Time Scheduling stage；The possible wind-force output scene of one group of node on behalf of scene tree and loading demand feelings
Scape, node are described as the sample size of probability density function or stochastic variable；The branching representation of scene tree is from stage
One scene to the latter half another scene possible transition, each scene transitions are multiplied by corresponding conditional probability；
Step 3: it using the mixed-integer programming model of branch-and-bound and primal dual interior point method solution procedure one, obtains most
Excellent Unit Combination scheme utilizes in wind electricity generating and finds optimal equalization point between potential risk.
Wherein, the objective function of the scheduling model a few days ago is defined as minimizing the operating cost of generating set, including hair
Motor group runs quadratic cost, an expense and constant expense；The operation constraint of scheduling model a few days ago is including equality constraint and not
Equality constraint, equality constraint be Power Systems Constraints of Equilibrium, inequality constraints be unit output constraint, Unit Commitment about
Beam, unit ramp loss and Unit Commitment expense restriction.
The objective function of scheduling model is defined as minimizing the operating cost of generating set, including generator before the hour
Group operation quadratic cost, an expense and constant expense；The operation constraint of scheduling model includes equality constraint and differs before hour
Formula constraint, equality constraint are Power Systems Constraints of Equilibrium, and inequality constraints is that unit output constrains, rapid starting/stopping unit opens
Stop constraint, unit ramp loss and Unit Commitment expense restriction.
The objective function of the real-time cutting load model is defined as minimizing load-shedding cost；The fortune of real-time cutting load model
Row is constrained to unit output constraint and Power Systems Constraints of Equilibrium.
The objective function expression formula of the scheduling model a few days ago is (1a):
WhereinIndicate active power output of the scheduling phase generating set i in t moment a few days ago, ai、bi、ciRespectively indicate power generation
Unit i runs quadratic cost coefficient, a cost coefficient and constant expense coefficient, ODAIndicate the expense dispatched a few days ago, CupWith
CdownThe starting expense and stopping expense of unit are respectively represented,Represent scheduling phase t moment a few days ago need to cut off it is negative
Lotus, δ are the cost coefficient of cutting load, and NT is the time range entirely dispatched, and time interval is one hour, and NG is unit number.
The Power Systems Constraints of Equilibrium of the scheduling model a few days ago, unit output constraint, Unit Commitment Constraint, unit
The concrete form of Climing constant and Unit Commitment expense restriction is as follows:
1. Power Systems Constraints of Equilibrium equation is (1b):
WhereinFor scheduling phase generator i a few days ago t moment state,For the wind of scheduling phase t moment a few days ago
Power generated energy,For scheduling phase a few days agotThe load value at moment,For the number of the cutting load of scheduling phase t moment a few days ago
Value；
2. unit output is constrained to (1c):
WhereinFor unit i contribute lower limit value,For the upper limit value of unit i power output；
3. Unit Commitment Constraint is (1d):
WhereinWithThe time that respectively a few days ago scheduling phase unit i starts or stops in t moment,WithThe minimum of respectively unit i starts and stops the time；
4. unit ramp loss is (1e):
WhereinWithFor the minimum climbing value of scheduling phase unit i a few days ago；
5. Unit Commitment expense restriction is (1f):
WhereinWithRespectively the starting expense of unit i and stopping expense.
The reduced form of the scheduling model a few days ago is as follows:
(1g): Min ODA=f (XDA)
(1i): G (XDA)≥0
Wherein ODAFor the expense dispatched, X a few days agoDAFor the decision variable dispatched a few days ago For a few days ago
In the wind-power electricity generation value of t moment in scheduling, H () is equality constraint, and G () is inequality constraints.
The reduced form of scheduling model is as follows before the hour:
(2a): Min OHA=f (XHA)
(2c): G (XHA)≥0
Wherein OHAIndicate the expense dispatched before hour, XHAFor the decision variable dispatched before hourWhereinIndicate hour before scheduling phase generating set i t moment active power output,It is scheduling phase generator i before hour in t
The state at moment,For the numerical value of the cutting load of scheduling phase t moment before hour；For before hour scheduling in t moment
Wind-power electricity generation value,For the load value of scheduling phase t moment before hour, H () is equality constraint, G () be inequality about
Beam；Unlike scheduling model a few days ago, the decision variable in model includes rapid starting/stopping Unit Commitment state and all units
Power output, time range is 5~6 hours, and time interval is half an hour.
The objective function expression formula of the real-time cutting load model is (3a):
Wherein ORTIndicate the expense in real-time cutting load stage, NTRTFor the when number of segment in real-time cutting load stage,It indicates
The load of real-time cutting load stage t moment excision, δ is corresponding cost coefficient.
The unit output constraint of the real-time cutting load model and the concrete form of Power Systems Constraints of Equilibrium are as follows:
1) units limits (3b) of unit:
Wherein NTRTFor the when number of segment in real-time cutting load stage,For unit i t moment power generating value,For unit i
Power output lower limit value,For the upper limit value of unit i power output, the shape at corresponding moment for having the start and stop state of unit and being dispatched before hour
State is identical；
2) Power Systems Constraints of Equilibrium (3c):
WhereinIndicate the blower power output of this stage t moment,Indicate this stage in the load value of t moment,Table
Show the excision load of this stage t moment.
The mixed-integer programming model is (4a):
Min ODA+OHA+ORT
s.t.(1h),(1i),(2b),(2c),(3b),(3c)
Electric system keeps the probability of power-balance to be greater than η under extreme weather environment, and η indicates the stabilization of electric system
Property, the stability requirement of the conditional probability constraint representation electric system in (4a).
Compared with prior art, the invention has the following advantages:
(1) present invention has used Risk Constraint scheduling, with the continuous renewal of weather forecasting information, constantly updates scheduling meter
It draws, and only needs to consider the risk boundary at the last one moment.
(2) model uses Dynamic Programming, and the form of solution is simple and clear, and operation plan update times more multi-model is more quasi-
Really.
(3) solution that method of the invention obtains is globally optimal solution.
(4) by the operation plan of the update of weather forecasting information constantly adjustment unit, rapid starting/stopping machine is flexibly used
Group and conventional power unit are the place of method of the invention better than two methods of stochastic programming and advanced scheduling based on scene.
(5) model of method building of the invention is by scheduling model a few days ago, scheduling model and as emergency measure before hour
Real-time cutting load model three models are called in order by the update of weather forecasting information, when finding out given scheduling
Interior optimal scheduling strategy, that is, the cost that runs minimized (including unit operating cost and cutting load failure costs).Relatively
For routine dispactching, method of the invention no longer considers the most baneful influence of extreme weather at the very start, but with weather
The continuous renewal of information gradually considers the influence of extreme weather, avoids that operation reserve is overly conservative, cuts off blower too early, mentions
The high utilization efficiency of blower.
Detailed description of the invention
Fig. 1 is model flow figure of the invention；
Fig. 2 is scene tree graph of the invention；
Fig. 3 is the flow chart for carrying out regular processing to integer variable using the method for branch-and-bound；
Fig. 4 is generating set output power curve figure under method of the invention；
Fig. 5 is generating set output power curve figure under conventional method.
Specific embodiment
In order to which the above objects, features and advantages of the present invention is more clearly understood, below with reference to the specific implementation of model
Technical solution of the present invention is further described in detail in form.
The method for improving extreme weather environment apparatus for lower wind utilization rate is dispatched using Risk Constraint, comprising the following steps:
Step 1: by rapid starting/stopping unit, conventional power unit and Wind turbine compositional modeling at mixed-integer programming model, institute
Model is stated by scheduling model a few days ago, scheduling model and real-time cutting load model form before hour；By three models in Risk Constraint
Sequential solution is carried out under method, obtains global optimum's strategy in entire scheduling slot；
Step 2: generating sequential scene tree, and the scene tree is divided into the scene of scheduling phase, scheduling phase before hour a few days ago
Scene and the scene in Real-Time Scheduling stage；The possible wind-force output scene of one group of node on behalf of scene tree and loading demand feelings
Scape, node are described as the sample size of probability density function or stochastic variable；The branching representation of scene tree is from stage
One scene to the latter half another scene possible transition, each scene transitions are multiplied by corresponding conditional probability；
Step 3: it using the mixed-integer programming model of branch-and-bound and primal dual interior point method solution procedure one, obtains most
Excellent Unit Combination scheme utilizes in wind electricity generating and finds optimal equalization point between potential risk.
The model of method building of the invention is by scheduling model a few days ago, scheduling model and as the reality of emergency measure before hour
When cutting load model three models are called in order by the update of weather forecasting information, to find out in given scheduling time
Optimal scheduling strategy, that is, the cost that runs minimized (including unit operating cost and cutting load failure costs).Relative to normal
For rule scheduling, method of the invention no longer considers the most baneful influence of extreme weather at the very start, but with Weather information
Continuous renewal, gradually consider the influence of extreme weather, avoid that operation reserve is overly conservative, cuts off blower too early, improve
The utilization efficiency of blower.
Method of the invention considers the characteristics of conventional power unit and rapid starting/stopping unit, the moving back due to blower during storm
Cause in the case where there is electric power notch out, rapid starting/stopping unit and active discharge mechanism are called, to guarantee the function of system
Rate balance；Risk Constraint is efficiently played an important role using the tradeoff between potential risk in realization wind-force.
Method of the invention, the specific steps are as follows:
Step 1: establishing model
1, scheduling model a few days ago:
Objective function is defined as minimizing the operating cost of generating set, including generating set runs quadratic cost, once
Expense and constant expense.The objective function expression formula of scheduling model is (1a) a few days ago:
WhereinIndicate active power output of the scheduling phase generating set i in t moment a few days ago, ai、bi、ciRespectively indicate power generation
Unit i runs quadratic cost coefficient, a cost coefficient and constant expense coefficient, ODAIndicate the expense dispatched a few days ago, CupWith
CdownThe starting expense and stopping expense of unit are respectively represented,Represent scheduling phase t moment a few days ago need to cut off it is negative
Lotus, δ are the cost coefficient of cutting load, and NT is the time range entirely dispatched, and time interval is one hour, and NG is unit number.
The operation constraint of scheduling model includes equality constraint and inequality constraints a few days ago, and equality constraint is Power Systems
Constraints of Equilibrium, inequality constraints be unit output constraint, Unit Commitment Constraint, unit ramp loss and Unit Commitment expense about
Beam.Concrete form is as follows:
1. Power Systems Constraints of Equilibrium equation is (1b):
WhereinFor scheduling phase generator i a few days ago t moment state,For the wind of scheduling phase t moment a few days ago
Power generated energy,For scheduling phase a few days agotThe load value at moment,For the number of the cutting load of scheduling phase t moment a few days ago
Value；
2. unit output is constrained to (1c):
WhereinFor unit i contribute lower limit value,For the upper limit value of unit i power output；
3. Unit Commitment Constraint is (1d):
WhereinWithThe time that respectively a few days ago scheduling phase unit i starts or stops in t moment,WithThe minimum of respectively unit i starts and stops the time；
4. unit ramp loss is (1e):
WhereinWithFor the minimum climbing value of scheduling phase unit i a few days ago；
5. Unit Commitment expense restriction is (1f):
WhereinWithRespectively the starting expense of unit i and stopping expense.
The reduced form of scheduling model is as follows a few days ago:
(1g): Min ODA=f (XDA)
(1i): G (XDA)≥0
Wherein ODAFor the expense dispatched, X a few days agoDAFor the decision variable dispatched a few days ago For a few days ago
In scheduling the wind-power electricity generation value H () of t moment be equality constraint, G () be inequality constraints.
2, scheduling model before hour:
Scheduling model is similar with scheduling model a few days ago before hour, and reduced form is as follows:
(2a): Min OHA=f (XHA)
(2c): G (XHA)≥0
Wherein OHAIndicate the expense dispatched before hour, XHAFor the decision variable dispatched before hourWhereinIndicate hour before scheduling phase generating set i t moment active power output,It is scheduling phase generator i before hour in t
The state at moment,For the numerical value of the cutting load of scheduling phase t moment before hour；For before hour scheduling in t moment
Wind-power electricity generation value,For the load value of scheduling phase t moment before hour, H () is equality constraint, G () be inequality about
Beam；Unlike scheduling model a few days ago, the decision variable in model includes rapid starting/stopping Unit Commitment state and all units
Power output, time range is 5~6 hours, and time interval is half an hour.
3, real-time cutting load model:
Come temporarily in storm, if the power output of all units has reached maximum value and can't reach system power balance, needs
Cut off a part of load.
The objective function of real-time cutting load model is defined as minimizing load-shedding cost, the target letter of real-time cutting load model
Number expression formula is (3a):
Wherein ORTIndicate the expense in real-time cutting load stage, NTRTFor the when number of segment in real-time cutting load stage,It indicates
The load of real-time cutting load stage t moment excision, δ is corresponding cost coefficient.
The operation of real-time cutting load model is constrained to unit output constraint and Power Systems Constraints of Equilibrium, concrete form
It is as follows:
1) units limits (3b) of unit:
Wherein NTRTFor the real-time cutting load stage when number of segment (4 periods, be spaced 15 minutes),It is unit i in t moment
Power generating value,For unit i contribute lower limit value,For the upper limit value of unit i power output, the start and stop state of all units and hour
The state at the correspondence moment of preceding scheduling is identical.
2) Power Systems Constraints of Equilibrium (3c):
WhereinIndicate the blower power output of this stage t moment,Indicate this stage in the load value of t moment,Table
Show the excision load of this stage t moment.
4, overall model of the invention: mixed-integer programming model is (4a):
Min ODA+OHA+ORT
s.t.(1h),(1i),(2b),(2c),(3b),(3c)
Electric system keeps the probability of power-balance to be greater than η under extreme weather environment, and η indicates the stabilization of electric system
Property, the stability requirement of the conditional probability constraint representation electric system in (4a).
Step 2: generating sequential scene tree
In view of the linking of the scene of various scheduling phases, scene is generated by the mode of scene tree, and in scene transitions
Multiplied by corresponding conditional probability.Detailed process is as follows:
In scheduling phase a few days ago respectively with P (H1)=0.6 and P (L1The probability of)=0.4 generates two high prediction errors and low
The scene for predicting error, is denoted as { m11,m12}.Equally, the scheduling phase before hour, in m11Under conditions of, two high prediction error
With the scene { m of low prediction error21,m22Respectively with conditional probability P (H2|H1) and P (L2|H1) be generated.With same method,
In m12Under conditions of with P (H3|L1) and P (L3|L1) generate scene { m23,m24}.Finally, in the real-time cutting load stage, 8 scenes
{m31,m32}、{m33,m34}、{m35,m36And { m37,m38Respectively with conditional probability P (H4|H2H1)、P(L4|H2H1)、P(H5|
L2H1)、P(L5|L2H1)、P(H6|H3L1)、P(L6|H3L1)、P(H7|L3L1) and P (L7|L3L1) be generated.Scene tree graph such as Fig. 2 institute
Show.
Step 3: solving model
Mixed-integer programming model is solved using branch-and-bound and primal dual interior point method, obtains optimal unit combination scheme.
Specific step is as follows:
Model (4a) is written as follow compact models (5a):
minf(x)
G (x)=0
Wherein, x ∈ R(n), h (x)=[h1(x),…,hmIt (x)] is inequality constraints,Withh=
[h 1,…,h m]TIt is its bound, g (x)=[g1,…,gk]TFor equality constraint.
Equality constraint is converted by the inequality constraints of former problem (5a) first:
(5c): h (x)-l-h=0
Wherein slack variable (l, u) ∈ R(m), should meet
U > 0, l > 0
In this way, former problem turns to (5d):
minf(x)
h(x)-l-h=0
G (x)=0
U > 0
L > 0
Then, logarithmic barrier function is introduced to slack variable, the objective function of new problem is made to be similar to original in feasible zone
Objective function f (x).Therefore available (5e):
h(x)-l-h=0
G (x)=0
Wherein μ > 0 is Discontinuous Factors, and (5e) is solved with lagrange's method of multipliers, and Lagrangian is (5f):
Wherein, α ∈ R(k),β∈R(m),δ∈R(m)For Lagrange's multiplier, so KKT condition are as follows:
(5h): Lα=g (x)=0
(5i): Lβ=h (x)-l-h=0
(5k): Ll=β-μ L-1E=0
(5l): Lu=-δ-μ U-1E=0
(l,u；β) 0 >, δ < 0, α ≠ 0
Wherein L-1=diag (l1,…,lm)-1, U-1=diag (u1,…,um)-1, e=[1 ..., 1]T∈R(m)。
Using the equation group of the above KKT condition of interior point method solution based on Newton-Raphson approach solution, the pine of model (5d) is obtained
Optimal solution after relaxation；When being unsatisfactory for integer variable constraint, regular processing is carried out to integer variable using the method for branch-and-bound,
Process is as shown in Figure 3.
It is application example of the invention below:
It is illustrated by taking the system of 5 conventional power units as an example, mainly there is following three parts or step.
1, unit parameter is set:
Conventional power unit parameter is as shown in table one and table two:
Table one: the characteristic parameter of unit
Table two: the cost parameters of unit
Assuming that storm is in 19 points of arrival wind power plant locations.
2, model is built:
Objective function is the operating cost of unit, and constraint condition is the operation constraint of unit, including equality constraint and is differed
Formula constraint.The mathematical model of the method for the invention and conventional method is as follows:
Method of the invention:
Scheduling expense+real-time the load-shedding cost of scheduling expense before min days+before hour
S.t. the generating set operation constraint dispatched before day
The generating set dispatched before hour runs constraint
The generating set power constraint of real-time cutting load and power-balance constraint
Conditional probability constraint under real-time cutting load
Conventional method:
Scheduling expense before min days
S.t. generating set operation constraint
The case where being calculated here for simplifying, only considering single scene.
3, solving model:
It is solved in Matlab by above two model modeling, and with cplex12.5 by yalmip, obtains conventional power unit
Power output situation, as a result as shown in Figure 4, Figure 5.
It is compared by Fig. 4 and Fig. 5, it can be seen that the method for the Risk Constraint scheduling used through the invention, conventional power unit exist
Power output before storm arrives is substantially reduced, this is because the method for Risk Constraint scheduling has postponed the mute time of blower, so that
The wind resource before storm arrives is utilized in blower well, improves the utilization rate of wind-force.
Claims (8)
1. a kind of dispatch the method for improving extreme weather environment apparatus for lower wind utilization rate using Risk Constraint, which is characterized in that including
Following steps:
Step 1: by rapid starting/stopping unit, conventional power unit and Wind turbine compositional modeling at mixed-integer programming model, the mould
Type is by scheduling model a few days ago, scheduling model and real-time cutting load model form before hour；By three models in Risk Constraint method
It is lower to carry out sequential solution, obtain global optimum's strategy in entire scheduling slot；
Step 2: generating sequential scene tree, the scene tree be divided into the scene of scheduling phase a few days ago, before hour scheduling phase field
The scene of scape and Real-Time Scheduling stage；The possible wind-force output scene of one group of node on behalf of scene tree and loading demand scene,
Node is described as the sample size of probability density function or stochastic variable；One from a stage of the branching representation of scene tree
Scene to the latter half another scene possible transition, each scene transitions are multiplied by corresponding conditional probability；
Step 3: using the mixed-integer programming model of branch-and-bound and primal dual interior point method solution procedure one, optimal machine is obtained
Group assembled scheme, utilizes in wind electricity generating and finds optimal equalization point between potential risk.
2. the method according to claim 1, wherein the objective function of the scheduling model a few days ago is defined as minimum
The operating cost of elelctrochemical power generation unit, including generating set run quadratic cost, an expense and constant expense；Scheduling model a few days ago
Operation constraint include equality constraint and inequality constraints, equality constraint be Power Systems Constraints of Equilibrium, inequality constraints
For unit output constraint, Unit Commitment Constraint, unit ramp loss and Unit Commitment expense restriction.
3. the method according to claim 1, wherein the objective function of scheduling model is defined as most before the hour
The operating cost of small elelctrochemical power generation unit, including generating set run quadratic cost, an expense and constant expense；Scheduling before hour
The operation constraint of model includes equality constraint and inequality constraints, and equality constraint is Power Systems Constraints of Equilibrium, inequality
It is constrained to unit output constraint, rapid starting/stopping Unit Commitment Constraint, unit ramp loss and Unit Commitment expense restriction.
4. the method according to claim 1, wherein the objective function of the real-time cutting load model is defined as most
Smallization load-shedding cost；The operation of real-time cutting load model is constrained to unit output constraint and Power Systems Constraints of Equilibrium.
5. according to the method described in claim 2, it is characterized in that, the objective function expression formula of the scheduling model a few days ago is
(1a):
WhereinIndicate active power output of the scheduling phase generating set i in t moment a few days ago, ai、bi、ciRespectively indicate generating set i
Run quadratic cost coefficient, a cost coefficient and constant expense coefficient, ODAIndicate the expense dispatched a few days ago, CupAnd CdownRespectively
The starting expense and stopping expense of unit are represented,It represents scheduling phase t moment a few days ago and needs the load cut off, δ is negative to cut
The cost coefficient of lotus, NT are the time range entirely dispatched, and time interval is one hour, and NG is unit number；
The Power Systems Constraints of Equilibrium of the scheduling model a few days ago, unit output constraint, Unit Commitment Constraint, unit climbing
The concrete form of constraint and Unit Commitment expense restriction is as follows:
1. Power Systems Constraints of Equilibrium equation is (1b):
(i=1,2 ..., NG, t=1,2 ..., NT)
WhereinFor scheduling phase generator i a few days ago t moment state,It is sent out for the wind-force of scheduling phase t moment a few days ago
Electricity,For the load value of scheduling phase t moment a few days ago,For the numerical value of the cutting load of scheduling phase t moment a few days ago；
2. unit output is constrained to (1c):
(i=1,2 ..., NG, t=1,2 ..., NT)
WhereinFor unit i contribute lower limit value,For the upper limit value of unit i power output；
3. Unit Commitment Constraint is (1d):
(i=1,2 ..., NG, t=1,2 ..., NT)
WhereinWithThe respectively time that scheduling phase unit i is started and stopped in t moment a few days ago, Ti onAnd Ti offPoint
Not Wei the minimum of unit i start and stop the time；
4. unit ramp loss is (1e):
(i=1,2 ..., NG, t=1,2 ..., NT)
WhereinWithFor the minimum climbing value of scheduling phase unit i a few days ago；
5. Unit Commitment expense restriction is (1f):
(i=1,2 ..., NG, t=1,2 ..., NT)
WhereinWithRespectively the starting expense of unit i and stopping expense；
The reduced form of the scheduling model a few days ago is as follows:
(1g): Min ODA=f (XDA)
(1h):
(1i): G (XDA)≥0
Wherein ODAFor the expense dispatched, X a few days agoDAFor the decision variable dispatched a few days ago To dispatch a few days ago
In t moment wind-power electricity generation value, H () be equality constraint, G () be inequality constraints.
6. according to the method described in claim 3, it is characterized in that, the reduced form of scheduling model is as follows before the hour:
(2a): Min OHA=f (XHA)
(2b):
(2c): G (XHA)≥0
Wherein OHAIndicate the expense dispatched before hour, XHAFor the decision variable dispatched before hourWhereinTable
Show scheduling phase generating set i before hour in the active power output of t moment,It is scheduling phase generator i before hour in t moment
State,For the numerical value of the cutting load of scheduling phase t moment before hour；For the wind before hour in scheduling in t moment
Power power generation values,For the load value of scheduling phase t moment before hour, H () is equality constraint, and G () is inequality constraints；
Unlike scheduling model a few days ago, the decision variable in model includes going out for rapid starting/stopping Unit Commitment state and all units
Power, time range are 5~6 hours, and time interval is half an hour.
7. according to the method described in claim 4, it is characterized in that, the objective function expression formula of the real-time cutting load model is
(3a):
Wherein ORTIndicate the expense in real-time cutting load stage, NTRTFor the when number of segment in real-time cutting load stage,Expression is cut in real time
The load of load stage t moment excision, δ is corresponding cost coefficient；
The unit output constraint of the real-time cutting load model and the concrete form of Power Systems Constraints of Equilibrium are as follows:
1) units limits (3b) of unit:
(i=1,2 ..., NG, t=1,2 ..., NTRT)
Wherein NTRTFor the when number of segment in real-time cutting load stage,For unit i t moment power generating value,For under unit i power output
Limit value,For the upper limit value of unit i power output, the state phase of the start and stop state of all units and the corresponding moment dispatched before hour
Together；
2) Power Systems Constraints of Equilibrium (3c):
(i=1,2 ..., NG, t=1,2 ..., NTRT)
WhereinIndicate the blower power output of this stage t moment,Indicate this stage in the load value of t moment,Indicate this
The excision load value of stage t moment.
8. according to method described in claim 5,6,7, which is characterized in that the mixed-integer programming model is (4a):
Min ODA+OHA+ORT
s.t.(1h),(1i),(2b),(2c),(3b),(3c)
Electric system keeps the probability of power-balance to be greater than η under extreme weather environment, and η indicates the stability of electric system,
The stability requirement of conditional probability constraint representation electric system in (4a).
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