CN107171365A - Multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene - Google Patents
Multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention discloses a kind of multiple target stochastic and dynamic economic load dispatching method decoupled based on scene with asynchronous iteration, it comprises the following steps：1) correlation computations parameter is given；2) multiple target stochastic and dynamic economic load dispatching model is set up；3) interior point method is improved using scene decoupling and asynchronous iteration and solves multiple target stochastic and dynamic economic load dispatching model.Multiple target stochastic and dynamic Economic Dispatch Problem is converted into extensive multiple target certainty dynamic economic dispatch problem by the present invention using scene method, again a series of extensive single-objective nonlinear programming problems are translated into by normal boundary-intersected method, and solved with nonlinear primal-dual interior-point algorithm, avoid producing dense matrix, so that matrix is all sparse in whole calculating process, the economy and the feature of environmental protection of operation of power networks have preferably been taken into account, has been a scheduling scheme with higher on-road efficiency.
Description
Technical field
For Provincial Electric Power System, traffic control is decoupled and asynchronous iteration based on scene a few days ago the present invention relates to a kind of
Multiple target stochastic and dynamic economic load dispatching method, belongs to electrical engineering technical field.
Background technology
Dynamic economic dispatch problem after wind power plant access power system is actually a stochastic optimization problems.This respect
The research of early stage is improve the occasion restricted model and Robust Optimization Model.Chance-constrained Model considers that randomness is not accurate enough, and
The result of calculation of Robust Optimization Model is partial to conservative.In the recent period, U.S.'s Argonne National Laboratory is to the correlative study of stochastic programming
Thinking is to generate a large amount of random error scenes using scene tree-model, and the problem is accurately calculated with supercomputer.Mesh
Before, what domestic electric network active scheduling was performed is Energy-Saving Generation Dispatching Model, object function only one of which or several object functions
Weighted sum.Dispatching running way needs to take into account economy and the feature of environmental protection, but the two targets can conflict mutually sometimes, i.e. only
When considering some target, another target is just unable to reach preferably index.So the optimization two simultaneously in economic load dispatching model
Individual and two or more object function way can be that dispatcher or policymaker bring convenience.
By the stochastic and dynamic economic load dispatching model based on scene method when being applied to certain Chinese provincial power network, Monte Carlo is used
Method generates 1000 sampling scenes, and variable number has reached 16,432,417；Equality constraint quantity is 99,101；Inequality constraints number
Measure as 115,319,756, even if handling network constraint using critical circuits search method, inequality constraints quantity is also 49,686,
237.The scale of this nonlinear programming problem is still very huge.Conveniently consider many if to be provided for grid dispatching center
During target problem, calculation scale further expands.But, using some business softwares such as GAMS/CONOPT solvers or tradition
Interior point method can not solve this problem.
The content of the invention
In view of the shortcomings of the prior art, the present invention proposes a kind of random based on scene decoupling and the multiple target of asynchronous iteration
Dynamic economic dispatch method, it can effectively solve the multiple target stochastic and dynamic scheduling problem of Provincial Electric Power System.
The present invention solves its technical problem and adopted the technical scheme that：A kind of many mesh decoupled based on scene with asynchronous iteration
Stochastic and dynamic economic load dispatching method is marked, it comprises the following steps：
1) correlation computations parameter is given；
2) multiple target stochastic and dynamic economic load dispatching model is set up；
3) interior point method is improved using scene decoupling and asynchronous iteration and solves multiple target stochastic and dynamic economic load dispatching model.
Further, in step 1) in, the correlation computations parameter includes conventional generator group cost coefficient and on exerting oneself
The load and wind-powered electricity generation of lower limit parameter, transmission of electricity branch impedance and capacity parameter, pump-storage generator operational factor, and power system
Power parameter.
Further, it is characterized in that, in step 2) in,
Object function in the multiple target stochastic and dynamic economic load dispatching model is：
1. power purchase expense
First aim is the power purchase expense for minimizing prediction scene, is defined as follows：
Wherein, agIt is gthThe power purchase expense of platform conventional power unit；awindIt is the power purchase expense of wind power plant；For prediction scene
In gthPlatform unit is exerted oneself period t's；For w in prediction scenethIndividual wind power plant is exerted oneself in period t scheduling；NGAnd NW
It is the integrated wind plant number of conventional power unit number of units and system；
2. dusty gas discharge capacity
Second target is the dusty gas discharge capacity for minimizing conventional power unit, is defined as follows：
Wherein, b2,g, b1,gAnd b0,gIt is gthThe emission factor of platform conventional power unit；The dusty gas of the conventional power unit is extremely
Include SO less2With NOx gases；
Being constrained to substantially in the multiple target stochastic and dynamic economic load dispatching model：
1. power-balance constraint：
The active power balance constraint ignored in active power loss, prediction scene and error scene is defined as follows：
Wherein,It is g in prediction scene and error scenethThe exerting oneself in period t of platform conventional power unit；For prediction
W in scene and error scenethIndividual wind power plant is exerted oneself in period t scheduling；PmtFor load bus m period t load；NDIt is
Load bus number, T and NSIt is positive integer；
2. conventional power unit units limits：
A) prediction scene and error scene in conventional power unit exert oneself bound constraint, be expressed as follows：
Wherein, PgmaxAnd PgminIt is gthThe bound of exerting oneself of platform conventional power unit；
B) climbing in prediction scene and error scene/landslide constraint, is expressed as follows：
Wherein, rugAnd rdgIt is g respectivelythThe climbing of platform conventional power unit/landslide coefficient；When Δ T is that dynamic dispatching two is adjacent
The time interval at quarter；
C) scene transfer constraint
Scene shifts constraint representation from prediction scene to error scene, and the schedulable nargin of conventional power unit is expressed as follows：
Wherein, Δ T ' is gthPlatform conventional power unit is the dispatching response time needed for adapting to wind power output predicated error；
3. wind field units limits：
When system reserve is not enough or due to the limitation of the line transmission capacity close to the grid-connected place of wind field, abandon wind be can not
Avoid, so wind field units limits are expressed as follows：
4. the related constraint of hydroenergy storage station：
Pump-storage generator each moment can only work under one of which operating mode in generating electricity, draw water and shutting down, and expression is such as
Under：
Wherein,WithR is represented respectivelythGenerating and draw water power of the seat pump-up power station in the t periods；PrGmaxWith
PrPmaxRepresent to represent r respectively respectivelythSeat pump-up power station generates electricity and drawn water accordingly the upper limit of the power；NPSRepresent pump-up power station
Quantity；
5. network transmission is constrained：
Predict that the active transmission bound constraint representation in scene and error scene on power network line is：
Wherein, PlmaxFor the circuit l maximum transfer capacity upper limit；NLFor the number of lines；For prediction scene and error field
In scape on circuit l period t transimission power.
Further, in conventional power unit units limits condition, the time interval Δ T of the adjacent moment of dynamic dispatching two is 15
Minute, gthPlatform conventional power unit is 15 minutes for the dispatching response time Δ T ' needed for adapting to wind power output predicated error.
Further, in the relevant constraint of hydroenergy storage station, draw water consumption electricity and the balance for sending electricity
Relation constraint is represented by：
Wherein, there is energy loss between drawing water and generate electricity, ξ is energy conversion efficiency parameter, takes ξ=75%；
Network transmission constraints is expressed as using DC flow model：
Wherein, Glg, Flw, HlgAnd DldCircuit l and conventional power unit, wind power plant, between pump-up power station and load are represented respectively
The active power transfer factor.
Further, the compact expression-form of multiple target stochastic and dynamic economic load dispatching model is：
min F(x0)={ f1(x0),f2(x0)} (12)
s.t.g0(x0)=0 (13)
gs(xsThe s=1,2 of)=0 ..., NS (14)
Wherein, f1() and f2() is respectively object function (1) and object function (2)；x0Represent conventional in prediction scene
Unit, wind power plant and hydroenergy storage station go out force vector；xsRepresent conventional power unit in error scene, wind power plant and water-storage electricity
Stand out force vector；Formula (13) represents formula (3), formula (8) and the formula (9) during the equality constraint, i.e. s=0 in prediction scene；Formula (14)
Represent the equality constraint in error scene, i.e. s=1,2 ..., NSWhen formula (3), formula (8) and formula (9)；Formula (15) representative formula formula
(4) inequality constraints in-formula (7), formula (10) and formula (8).
Further, the step 3) comprise the following steps：
31) from two target problems to the conversion of single-objective problem；
32) single-objective problem is solved using interior point method；
33) update equation is solved using scene decoupling and asynchronous iteration；
34) Parallel implementation optimal solution.
Further, the step 31) detailed process be：
1. the normalization of object function：
Because two object functions have different dimensions, following normalization processing is done：
Wherein,WithRepresent two normalized object functions；f1(x1*) and f2(x2*) represent optimal solution, i.e. Utopia's point；
f1(x2*) and f2(x1*) represent object space in most inferior solution；
2. equally distributed point on Utopia's line is generated：
Assuming that Utopia's line is divided into the equidistant points on m sections, Utopia's lineGenerated according to equation below：
Wherein, β1And β2It is the weight coefficient of two-end-point, j=0,1,2 ..., m：
Wherein, m=20；
3. equally distributed Pareto optimal solution is searched for
Intersected by the normal vector of the equidistant points produced by formula (17) with Pareto forward position, the Pareto being evenly distributed
Optimal solution, normal vectorIt is from Utopia's lineOnto Pareto forward positionThe vector of point, normal vectorExpression formula it is as follows：
Wherein, e=[1,1]T；
Further derive, the point on Pareto forward positionIt is expressed from the next：
Wherein, λ is representedWithThe distance between；
Pareto pointIt can be obtained by solving following single goal model：
min(-λ) (21)
g0(x0)=0 (23)
gs(xsThe s=1,2 of)=0 ..., NS (24)
Such as fruit dotMove, obtained by solving the single-objective problem being made up of formula (21)~formula (25) along Utopia's line
One group of equally distributed point is obtained, these points are exactly Pareto optimal solution, wherein, j=0,1,2 ..., 20.
Further, the step 32) detailed process be：
Combination type (22) and formula (23), and slack variable s is introduced to inequality (25)h, sh>=0, by formula (21)~formula
(25) single-objective problem of composition is rewritten into following form：
min(-λ) (26)
s.t.g(x0, λ) and=0 (27)
gs(xsThe s=1,2 of)=0 ..., NS (28)
Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm are expressed as follows：
Wherein, y, ysAnd yhFor Lagrange multiplier vector；NhIt is the quantity of inequality constraints in formula (29)；μ is barrier ginseng
Number, μ >=0；S=1,2 ..., NS。
According to Karush-Kuhn-Tucker optimality conditions, local derviation is asked to Augmented Lagrangian Functions formula (30), obtained
One group of Nonlinear System of Equations, then can be obtained with Newton Algorithm simplifying update equation group；
The update equation and variable after simplification are ranked up by prediction scene and the order of error scene, obtain it is following its
Coefficient matrix has the simplification update equation group of diagonal edged structure：
Wherein, Δ Z0=[(Δ x0)T,(Δy)T,Δλ]T；ΔZs=[(Δ xs)T,(Δys)T]T；L0、LsAnd MsIt is symmetrical
Sparse matrix；S=1,2 ..., NS。
Further, the step 33) detailed process be：
Formula (31) equation is equally decoupled as NSThe equation group of+1 low-dimensional, i.e.,：
Formula (32) and formula (33) are converted into following form with the method for synchronous iteration：
In formula, s=1,2 ..., NS。
Further, the step 34) detailed process be：Two end points are calculated according to NBI methods first, then calculated again
The Pareto optimal solution of other points on Utopia's line.
Beneficial effects of the present invention are as follows：
The higher-dimension update equation that the present invention has diagonal edged form to coefficient matrix carries out scene decoupling, by higher-dimension amendment
The solution equivalence of equation is decomposed into the solution of several (corresponding with sampling scene with prediction scene respectively) low-dimensional linear equations.
Asynchronous iteration technology is used when solving these low-dimensional linear equations, so as to avoid producing dense matrix so that whole calculating process
Middle matrix is all sparse, and implements parallel computation.
The present invention solves multi-objective optimization scheduling using normal boundary-intersected (NBI) method and interior point method, preferably simultaneous
The economy and the feature of environmental protection of operation of power networks have been turned round and look at, has been a scheduling scheme with higher on-road efficiency.
Multiple target stochastic and dynamic Economic Dispatch Problem is converted into extensive multiple target certainty by the present invention using scene method
Dynamic economic dispatch problem, then by normal boundary-intersected (NBI) method to be translated into a series of extensive single goals non-linear
Planning problem, and solved with nonlinear primal-dual interior-point algorithm.It is extensive single solving these using nonlinear primal-dual interior-point algorithm
During target nonlinear programming problem, the coefficient matrix of the simplification update equation arranged according to Episode sequences has diagonal edged
Structure.Therefore decoupling can be implemented to it, and the low-dimensional update equation group after decoupling is solved using asynchronous block iteration method, can
Applied to the multiple target stochastic and dynamic scheduling problem for solving certain Provincial Electric Power System.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the Pareto forward position in object space and Utopia's line schematic diagram；
Fig. 3 is the Pareto forward position in normalization object space and Utopia's line schematic diagram；
Fig. 4 is the Structure matrix schematic diagram of optimal solution on Pareto forward position；
Fig. 5 is the parallel computation configuration diagram of single Pareto optimal solution.
Embodiment
For the technical characterstic for illustrating this programme can be understood, below by embodiment, and its accompanying drawing is combined, to this hair
It is bright to be described in detail.Following disclosure provides many different embodiments or example is used for realizing the different knots of the present invention
Structure.In order to simplify disclosure of the invention, hereinafter the part and setting of specific examples are described.In addition, the present invention can be with
Repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not indicated
Relation between various embodiments are discussed and/or set.It should be noted that part illustrated in the accompanying drawings is not necessarily to scale
Draw.Present invention omits the description to known assemblies and treatment technology and process to avoid being unnecessarily limiting the present invention.
As shown in figure 1, a kind of multiple target stochastic and dynamic economic load dispatching decoupled based on scene with asynchronous iteration of the present invention
Method, it comprises the following steps：
1) correlation computations parameter is given；
2) multiple target stochastic and dynamic economic load dispatching model is set up；
3) interior point method is improved using scene decoupling and asynchronous iteration and solves multiple target stochastic and dynamic economic load dispatching model.
In step 1) in, the correlation computations parameter includes conventional generator group cost coefficient and bound parameter of exerting oneself,
The load and wind power parameter for branch impedance and capacity parameter, pump-storage generator operational factor, and the power system of transmitting electricity.
In step 2) in, the object function in Optimal Operation Model is：
1. power purchase expense
First aim is the power purchase expense for minimizing prediction scene, is defined as follows：
Wherein, agIt is gthThe power purchase expense of platform conventional power unit；awindIt is the power purchase expense of wind power plant；For prediction scene
In gthPlatform unit is exerted oneself period t's；For w in prediction scenethIndividual wind power plant is exerted oneself in period t scheduling；NGAnd NW
It is the integrated wind plant number of conventional power unit number of units and system.
2. dusty gas discharge capacity
Second target is that the dusty gas discharge capacity for minimizing conventional power unit (thinks SO2, NOx master), it is defined as follows：
Wherein, b2,g, b1,gAnd b0,gIt is gthThe emission factor of platform conventional power unit.
In the step (2), being constrained to substantially in Optimal Operation Model：
1. power-balance constraint：
The active power balance constraint ignored in active power loss, prediction scene and error scene is defined as follows：
Wherein,It is g in prediction scene and error scenethPlatform conventional power unit in period t
Exert oneself；For w in prediction scene and error scenethIndividual wind power plant goes out in period t scheduling
Power；PmtFor load bus m period t load；NDIt is load bus number.
2. conventional power unit units limits：
A) prediction scene and error scene in conventional power unit exert oneself bound constraint, be expressed as follows：
Wherein, PgmaxAnd PgminIt is gthThe bound of exerting oneself of platform conventional power unit.
B) climbing in prediction scene and error scene/landslide constraint, is expressed as follows：
Wherein, rugAnd rdgIt is g respectivelythThe climbing of platform conventional power unit/landslide coefficient；When Δ T is that dynamic dispatching two is adjacent
The time interval at quarter, in specific implementation process, Δ T takes 15 minutes.
C) scene transfer constraint
The constraint representation is from prediction scene to error scene, and the schedulable nargin of conventional power unit is expressed as follows：
Wherein, Δ T ' is gthPlatform conventional power unit for adapt to wind power output predicated error needed for the dispatching response time,
In specific implementation process, Δ T ' takes 15 minutes.
3. wind field units limits：
When system reserve is not enough or due to the limitation of the line transmission capacity close to the grid-connected place of wind field, abandon wind be can not
Avoid.So, wind field units limits are expressed as follows：
4. the related constraint of hydroenergy storage station：
Pump-storage generator each moment can only work under a kind of operating mode：Generate electricity, draw water, shut down.On mathematical expression
It is as follows：
Wherein,WithR is represented respectivelythSeat pump-up power station the t periods generating and draw water
Power；PrGmaxAnd PrPmaxRepresent to represent r respectively respectivelythSeat pump-up power station generates electricity and drawn water accordingly the upper limit of the power；NPSRepresent
The quantity of pump-up power station.
In actual motion, bound constraint of exerting oneself not only is met, it is also to be ensured that generated energy and pump-out in one day
Matching, that is, draw water and consume electricity and send electricity and to balance.The relation of balance is taken out and (drawn water and consume electricity and send electricity) to hair about
Beam is represented by：
Wherein, there is energy loss between drawing water and generate electricity, ξ is energy conversion efficiency parameter, in specific implementation process, is taken
ξ=75%.
5. network transmission is constrained：
Active transmission bound constraint in prediction scene and error scene on power network line is represented by：
Wherein, PlmaxFor the circuit l maximum transfer capacity upper limit；NLFor the number of lines；To be pre-
Survey the transimission power of period t on scene and error scene center line road l.Constraint is expressed as using DC flow model：
Wherein, Glg, Flw, HlgAnd DldCircuit l and conventional power unit, wind power plant, between pump-up power station and load are represented respectively
The active power transfer factor.
Following dense list can be rewritten into by the multiple target stochastic and dynamic economic load dispatching model of formula (1)~formula (11) description to reach
Form：
min F(x0)={ f1(x0),f2(x0)} (12)
s.t.g0(x0)=0 (13)
gs(xsThe s=1,2 of)=0 ..., NS (14)
Wherein, f1() and f2() represents two object function equation (1)~(2)；x0Represent conventional machine in prediction scene
Group, wind power plant and hydroenergy storage station go out force vector；xs(s=1,2 ..., NS) represent conventional power unit, wind power plant in error scene
Go out force vector with hydroenergy storage station；Formula (13) represent prediction scene in equality constraint, i.e. s=0 when formula (3), (8) and
(9)；Formula (14) represents the equality constraint in error scene, i.e. s=1,2 ..., NSWhen formula (3), (8) and (9)；Formula (15) generation
Inequality constraints in tabular form (4)-(7), (10) and (8).
The step 3) comprise the following steps：
(1) from two target problems to the conversion of single-objective problem
2 show Pareto forward position and Utopia in the object space of two target problems described by formula (12)~(15)
Line, object function f1And f2It is two axles of coordinate system.x1*And x2*Represent in the optimal solution of two single-object problems, corresponding diagram
Two point φ1(f1(x1*),f2(x1*)) and φ2(f1(x2*),f2(x2*)).The two single-objective problems be consider constraint (12)~
(15) f is minimized in the case of respectively1And f2。φ1And φ2Two end points in Pareto forward position are constituted, their line is called
Utopia's line.
1. the normalization of object function：
Because two object functions have different dimensions, to avoid producing numerical problem, object space needs to do as follows
Normalization is handled：
Wherein,WithRepresent two normalized object functions；f1(x1*) and f2(x2*) represent optimal solution, i.e. Utopia
Point；f1(x2*) and f2(x1*) represent object space in most inferior solution.
So the scope of object function is [0,1] after normalization, as shown in Figure 3.
2. equally distributed point on Utopia's line is generated：
As shown in figure 3,Be fromArriveVector.Assuming that Utopia's line is divided into equidistant on m sections, Utopia's line
PointGenerated according to equation below：
Wherein, β1And β2It is the weight coefficient of two-end-point：
In specific implementation process, m is set to just have 21 equidistant points on 20, such Utopia's line.
3. equally distributed Pareto optimal solution is searched for
Intersected by the normal vector of the equidistant points produced by formula (17) with Pareto forward position, we are with regard to that can be evenly distributed
Pareto optimal solution.In Fig. 3, normal vectorIt is from Utopia's lineOnto Pareto forward positionThe vector of point, expression formula is such as
Under：
Wherein, e=[1,1]T。
Further derive, the point on Pareto forward positionIt can be expressed from the next：
Wherein, λ is representedWithThe distance between；
In summary, former multi-objective problem conversion is for a series of single-objective problems.Pareto point YjCan be following by solving
Single goal model is obtained：
min(-λ) (21)
g0(x0)=0 (23)
gs(xsThe s=1,2 of)=0 ..., NS (24)
Such as fruit dotMove, be made up of by solving formula (21)~(25) along Utopia's line
Single-objective problem obtains one group of equally distributed point, and these points are exactly Pareto optimal solution.
(2) single-objective problem is solved using interior point method
Combination type (22) and (23), and slack variable s is introduced to inequality (25)h(sh≥0).So, by formula (21)~
(25) single-objective problem of composition can be rewritten into following form：
min(-λ) (26)
s.t.g(x0, λ) and=0 (27)
gs(xsThe s=1,2 of)=0 ..., NS (28)
Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm are expressed as follows：
Wherein, y, ys(s=1,2 ..., NS) and yhFor Lagrange multiplier vector；NhIt is inequality constraints in formula (29)
Quantity；μ is barrier parameter, μ >=0.
According to Karush-Kuhn-Tucker (KKT) optimality condition, local derviation is asked to Augmented Lagrangian Functions (30), obtained
To one group of Nonlinear System of Equations, then it can be obtained with Newton Algorithm simplifying update equation group.By prediction scene and error scene
Order is ranked up to the update equation and variable after simplification, and we, which can obtain its following coefficient matrix, has diagonal edged knot
Structure[88]Simplification update equation group：
Wherein, Δ Z0=[(Δ x0)T,(Δy)T,Δλ]T；ΔZs=[(Δ xs)T,(Δys)T]T(s=1,2 ..., NS)；
L0, Ls(s=1,2 ..., NS) and Ms(s=1,2 ..., NS) it is symmetrical and sparse.
Herein it should be noted that when solve two-end-point with solve forward position on other Pareto points when, formula (31) coefficient
The dimension of matrix is different, i.e. be respectively (T (NG+NW+2·NPS)+T+NPS+1)·(NS+ 1) and (T (NG+NW+2·
NPS)+T+NPS+1)·(NS+1)+3.When solving two-end-point, matrix L0, LsAnd MsAll it is identical, is T (NG+NW+2·
NPS)+T+NPS+1.But when solving other Pareto points on forward position, matrix L0Dimension be changed into T (NG+NW+2·NPS)+
T+NPS+ 4, now matrix LsAnd MsDimension it is constant.
(3) update equation is solved using scene decoupling and asynchronous iteration：
Although the multiple target stochastic and dynamic Economic Dispatch Problem of large-scale power system is a High-Dimensional Model, interior point is used
After method is solved, its coefficient matrix has the characteristic of diagonal edged.It therefore, it can equally decouple the equation as NS+ 1 low-dimensional
Equation group, i.e.,：
Formula (32) and (33) are converted into following form with the method for synchronous iteration：
Although L0, LsAnd MsAll it is that sparse matrix and formula (35) can be with parallel computations, but in (k+1) secondary iteration, calculates
Dense matrix in formula (34)Substantial amounts of internal memory and CPU will be taken.This is also the main drawback of synchronous iteration.So
And, asynchronous iteration[92]This inferior position can be avoided.If we solve institute in formula (32) and (33), iteration using asynchronous iteration
The matrix used will all be sparse.Assuming that Δ Z in first iteration0=0, during (k+1) secondary iteration, formula (32) and (33) can turn
Turn to following form：
Obviously, formula (37) can also parallel computation.
(4) Parallel implementation optimal solution：
Step 3) in the translation and compiling environment of algorithm be Matlab 2014b, parallel computing trunking includes 16 Dell M620 knives
Piece machine.Every rolling reamer machine is further equipped with E5-2650 processors (every piece of processor is the thread of 8 core 8) 128GB internal memories strong to the utmost
InfiniBand networks and 10,000,000,000 optical fiber.Data exchange when InfiniBand networks and 10,000,000,000 optical fiber are used to calculate, file is common
Enjoy and remote control.The Clustering OS is Windows Server 2012R2.
It is according to the calculating set up between multiple target stochastic and dynamic economic load dispatching model, different Pareto optimal solutions of foundation
Separate.Two-end-point is first calculated according to NBI methods, as shown in figure 4, calculating two end points according to NBI methods first, then counted again
Calculate other Pareto optimal solutions on Utopia's line.Solved during Pareto optimal solution is calculated using parallel thread.
When solving one of them in single Pareto optimal solution or two-end-point, according to progress calculate node and head node shown in Fig. 5
In data exchange process, wherein, NThrRepresent the parallel thread quantity required when solving single Pareto optimal solution.
The higher-dimension update equation that the present invention has diagonal edged form to coefficient matrix carries out scene decoupling, by higher-dimension amendment
The solution equivalence of equation is decomposed into the solution of several (corresponding with sampling scene with prediction scene respectively) low-dimensional linear equations.
Asynchronous iteration technology is used when solving these low-dimensional linear equations, so as to avoid producing dense matrix so that whole calculating process
Middle matrix is all sparse, it is possible to implement parallel computation.
The present invention solves multi-objective optimization scheduling using normal boundary-intersected (NBI) method and interior point method, preferably simultaneous
The economy and the feature of environmental protection of operation of power networks have been turned round and look at, has been a scheduling scheme with higher on-road efficiency.
The present invention sets up model and simulation calculation is carried out on cluster be made up of 16 Dell M620 rolling reamer machines, parallel reality
Scene decoupling and the asynchronous iteration of higher-dimension update equation are showed, this method can apply to a few days ago random economic tune of provincial power network
Degree is calculated.
Simply the preferred embodiment of the present invention described above, for those skilled in the art,
Without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also regarded as this hair
Bright protection domain.
Claims (10)
1. the multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene, it is characterized in that, including following step
Suddenly：
1) correlation computations parameter is given；
2) multiple target stochastic and dynamic economic load dispatching model is set up；
3) interior point method is improved using scene decoupling and asynchronous iteration and solves multiple target stochastic and dynamic economic load dispatching model.
2. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 1 decoupled based on scene with asynchronous iteration, its
It is characterized in, in step 1) in, the correlation computations parameter includes conventional generator group cost coefficient and bound parameter of exerting oneself, defeated
The load and wind power parameter of electric branch impedance and capacity parameter, pump-storage generator operational factor, and power system.
3. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 2 decoupled based on scene with asynchronous iteration, its
It is characterized in, in step 2) in,
Object function in the multiple target stochastic and dynamic economic load dispatching model is：
1. power purchase expense
First aim is the power purchase expense for minimizing prediction scene, is defined as follows：
<mrow>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
</munderover>
<mo>(</mo>
<mrow>
<msub>
<mi>a</mi>
<mi>g</mi>
</msub>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mn>0</mn>
</msubsup>
</mrow>
<mo>)</mo>
<mo>+</mo>
<msub>
<mi>a</mi>
<mrow>
<mi>w</mi>
<mi>i</mi>
<mi>n</mi>
<mi>d</mi>
</mrow>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>W</mi>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
<mn>0</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, agIt is gthThe power purchase expense of platform conventional power unit；awindIt is the power purchase expense of wind power plant；For in prediction scene the
gthPlatform unit is exerted oneself period t's；For w in prediction scenethIndividual wind power plant is exerted oneself in period t scheduling；NGAnd NWIt is normal
Advise the integrated wind plant number of unit number of units and system；
2. dusty gas discharge capacity
Second target is the dusty gas discharge capacity for minimizing conventional power unit, is defined as follows：
<mrow>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>b</mi>
<mrow>
<mn>2.</mn>
<mi>g</mi>
</mrow>
</msub>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mn>0</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msub>
<mi>b</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mi>g</mi>
</mrow>
</msub>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mn>0</mn>
</msubsup>
<mo>+</mo>
<msub>
<mi>b</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>g</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, b2,g, b1,gAnd b0,gIt is gthThe emission factor of platform conventional power unit；The dusty gas of the conventional power unit is at least wrapped
Include SO2With NOx gases；
Being constrained to substantially in the multiple target stochastic and dynamic economic load dispatching model：
1. power-balance constraint：
The active power balance constraint ignored in active power loss, prediction scene and error scene is defined as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>w</mi>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>D</mi>
</msub>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>t</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,It is g in prediction scene and error scenethThe exerting oneself in period t of platform conventional power unit；For prediction scene
With w in error scenethIndividual wind power plant is exerted oneself in period t scheduling；PmtFor load bus m period t load；NDIt is load
Interstitial content, T and NSIt is positive integer；
2. conventional power unit units limits：
A) prediction scene and error scene in conventional power unit exert oneself bound constraint, be expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, PgmaxAnd PgminIt is gthThe bound of exerting oneself of platform conventional power unit；
B) climbing in prediction scene and error scene/landslide constraint, is expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>d</mi>
<mi>g</mi>
</mrow>
</msub>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>u</mi>
<mi>g</mi>
</mrow>
</msub>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>2</mn>
<mo>,</mo>
<mn>3</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, rugAnd rdgIt is g respectivelythThe climbing of platform conventional power unit/landslide coefficient；Δ T is the adjacent moment of dynamic dispatching two
Time interval；
C) scene transfer constraint
Scene shifts constraint representation from prediction scene to error scene, and the schedulable nargin of conventional power unit is expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>d</mi>
<mi>g</mi>
</mrow>
</msub>
<msup>
<mi>&Delta;T</mi>
<mo>&prime;</mo>
</msup>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mn>0</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>u</mi>
<mi>g</mi>
</mrow>
</msub>
<msup>
<mi>&Delta;T</mi>
<mo>&prime;</mo>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Δ T ' is gthPlatform conventional power unit is the dispatching response time needed for adapting to wind power output predicated error；
3. wind field units limits：
When system reserve deficiency or the limitation due to the line transmission capacity close to the grid-connected place of wind field, it is inevitable to abandon wind
, so wind field units limits are expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>W</mi>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
4. the related constraint of hydroenergy storage station：
Pump-storage generator each moment can only work under one of which operating mode in generating electricity, draw water and shutting down, and be expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>G</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>;</mo>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>P</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<mo>&le;</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mrow>
<mi>P</mi>
<mi>S</mi>
</mrow>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mn>0</mn>
<mo>;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mrow>
<mi>P</mi>
<mi>S</mi>
</mrow>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,WithR is represented respectivelythGenerating and draw water power of the seat pump-up power station in the t periods；PrGmaxAnd PrPmaxRespectively
Expression represents r respectivelythSeat pump-up power station generates electricity and drawn water accordingly the upper limit of the power；NPSRepresent the quantity of pump-up power station；
5. network transmission is constrained：
Predict that the active transmission bound constraint representation in scene and error scene on power network line is：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>max</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>l</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>L</mi>
</msub>
<mo>;</mo>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>T</mi>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, PlmaxFor the circuit l maximum transfer capacity upper limit；NLFor the number of lines；For prediction scene and error scene center line
Period t transimission power on the l of road.
4. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 3 decoupled based on scene with asynchronous iteration, its
It is characterized in that in the relevant constraint of hydroenergy storage station, drawing water consumption electricity and the equilibrium relation constraint for sending electricity can
It is expressed as：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<mo>+</mo>
<mi>&xi;</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
<mi>s</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mrow>
<mi>P</mi>
<mi>S</mi>
</mrow>
</msub>
<mo>;</mo>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, there is energy loss between drawing water and generate electricity, ξ is energy conversion efficiency parameter, takes ξ=75%；
Network transmission constraints is expressed as using DC flow model：
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>g</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>G</mi>
</msub>
</munderover>
<msub>
<mi>G</mi>
<mi>lg</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>W</mi>
</msub>
</munderover>
<msub>
<mi>F</mi>
<mrow>
<mi>l</mi>
<mi>w</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mrow>
<mi>P</mi>
<mi>S</mi>
</mrow>
</msub>
</munderover>
<msub>
<mi>H</mi>
<mrow>
<mn>1</mn>
<mi>r</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>D</mi>
</msub>
</munderover>
<msub>
<mi>D</mi>
<mrow>
<mi>l</mi>
<mi>m</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Glg, Flw, HlgAnd DldCircuit l and conventional power unit are represented respectively, and wind power plant is active between pump-up power station and load
Power transmission factor.
5. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 4 decoupled based on scene with asynchronous iteration, its
It is characterized in that the compact expression-form of multiple target stochastic and dynamic economic load dispatching model is：
min F(x0)={ f1(x0),f2(x0)} (12)
s.t.g0(x0)=0 (13)
gs(xsThe s=1,2 of)=0 ..., NS (14)
<mrow>
<mi>h</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>1</mn>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>2</mn>
</msup>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msup>
<mi>x</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</msup>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, f1() and f2() is respectively object function (1) and object function (2)；x0Represent conventional machine in prediction scene
Group, wind power plant and hydroenergy storage station go out force vector；xsRepresent conventional power unit, wind power plant and hydroenergy storage station in error scene
Go out force vector；Formula (13) represents formula (3), formula (8) and the formula (9) during the equality constraint, i.e. s=0 in prediction scene；Formula (14) table
Show the equality constraint in error scene, i.e. s=1,2 ..., NSWhen formula (3), formula (8) and formula (9)；Formula (15) representative formula formula
(4) inequality constraints in-formula (7), formula (10) and formula (8).
6. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 5 decoupled based on scene with asynchronous iteration, its
It is characterized in, the step 3) comprise the following steps：
31) from two target problems to the conversion of single-objective problem；
32) single-objective problem is solved using interior point method；
33) update equation is solved using scene decoupling and asynchronous iteration；
34) Parallel implementation optimal solution.
7. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 6 decoupled based on scene with asynchronous iteration, its
Be characterized in, the step 31) detailed process be：
1. the normalization of object function：
Because two object functions have different dimensions, following normalization processing is done：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>f</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>1</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>2</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>1</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>f</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>2</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>1</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mn>2</mn>
<mo>*</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>16</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,WithRepresent two normalized object functions；f1(x1*) and f2(x2*) represent optimal solution, i.e. Utopia's point；f1
(x2*) and f2(x1*) represent object space in most inferior solution；
2. equally distributed point on Utopia's line is generated：
Assuming that Utopia's line is divided into the equidistant points on m sections, Utopia's lineGenerated according to equation below：
<mrow>
<msub>
<mover>
<mi>X</mi>
<mo>&OverBar;</mo>
</mover>
<mi>j</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>,</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>17</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, β1And β2It is the weight coefficient of two-end-point, j=0,1,2 ..., m：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mi>j</mi>
<mi>m</mi>
</mfrac>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>0.05</mn>
<mo>,</mo>
<mn>0.1</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mi>j</mi>
<mi>m</mi>
</mfrac>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>0.95</mn>
<mo>,</mo>
<mn>0.9</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>18</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, m=20；
3. equally distributed Pareto optimal solution is searched for
Intersected by the normal vector of the equidistant points produced by formula (17) with Pareto forward position, the Pareto optimality being evenly distributed
Solution, normal vectorIt is from Utopia's lineOnto Pareto forward positionThe vector of point, normal vectorExpression formula it is as follows：
<mrow>
<mover>
<mi>n</mi>
<mo>&RightArrow;</mo>
</mover>
<mo>=</mo>
<mo>-</mo>
<mover>
<mi>&Phi;</mi>
<mo>&OverBar;</mo>
</mover>
<mi>e</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>19</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, e=[1,1]T；
Further derive, the point on Pareto forward positionIt is expressed from the next：
<mrow>
<mover>
<mi>F</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mover>
<mi>&Phi;</mi>
<mo>&OverBar;</mo>
</mover>
<mi>&beta;</mi>
<mo>+</mo>
<mi>&lambda;</mi>
<mover>
<mi>n</mi>
<mo>&RightArrow;</mo>
</mover>
<mo>=</mo>
<mover>
<mi>&Phi;</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>&beta;</mi>
<mo>-</mo>
<mi>&lambda;</mi>
<mi>e</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>20</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, λ is representedWithThe distance between；
Pareto pointIt can be obtained by solving following single goal model：
min(-λ) (21)
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>s</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mover>
<mi>F</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mover>
<mi>&Phi;</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>&beta;</mi>
<mo>-</mo>
<mi>&lambda;</mi>
<mi>e</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>22</mn>
<mo>)</mo>
</mrow>
</mrow>
g0(x0)=0 (23)
gs(xsThe s=1,2 of)=0 ..., NS (24)
<mrow>
<mi>h</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>,</mo>
<mi>&lambda;</mi>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>1</mn>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>2</mn>
</msup>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msup>
<mi>x</mi>
<msub>
<mi>N</mi>
<mi>s</mi>
</msub>
</msup>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>25</mn>
<mo>)</mo>
</mrow>
</mrow>
Such as fruit dotMoved along Utopia's line, one is obtained by solving the single-objective problem being made up of formula (21)~formula (25)
The equally distributed point of group, these points are exactly Pareto optimal solution, wherein, j=0,1,2 ..., 20.
8. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 7 decoupled based on scene with asynchronous iteration, its
Be characterized in, the step 32) detailed process be：
Combination type (22) and formula (23), and slack variable s is introduced to inequality (25)h, sh>=0, by formula (21)~formula (25) group
Into single-objective problem be rewritten into following form：
min(-λ) (26)
s.t.g(x0, λ) and=0 (27)
gs(xsThe s=1,2 of)=0 ..., NS (28)
<mrow>
<mi>h</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>,</mo>
<mi>&lambda;</mi>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>1</mn>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>2</mn>
</msup>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msup>
<mi>x</mi>
<msub>
<mi>N</mi>
<mi>s</mi>
</msub>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>s</mi>
<mi>h</mi>
</msub>
<mo>=</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>29</mn>
<mo>)</mo>
</mrow>
</mrow>
Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm are expressed as follows：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>L</mi>
<mo>=</mo>
<mo>-</mo>
<mi>&lambda;</mi>
<mo>-</mo>
<msup>
<mi>y</mi>
<mi>T</mi>
</msup>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>,</mo>
<mi>&lambda;</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</munderover>
<msubsup>
<mi>y</mi>
<mi>s</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>g</mi>
<mi>s</mi>
</msub>
<mo>(</mo>
<msup>
<mi>x</mi>
<mi>s</mi>
</msup>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<msubsup>
<mi>y</mi>
<mi>h</mi>
<mi>T</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mrow>
<mi>h</mi>
<mrow>
<mo>(</mo>
<mrow>
<msup>
<mi>x</mi>
<mn>0</mn>
</msup>
<mo>,</mo>
<mi>&lambda;</mi>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>1</mn>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mn>2</mn>
</msup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>x</mi>
<msub>
<mi>N</mi>
<mi>s</mi>
</msub>
</msup>
</mrow>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>s</mi>
<mi>h</mi>
</msub>
</mrow>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>&mu;</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>h</mi>
</msub>
</munderover>
<mi>ln</mi>
<mi> </mi>
<msub>
<mi>s</mi>
<mrow>
<mi>h</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>30</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, y, ysAnd yhFor Lagrange multiplier vector；NhIt is the quantity of inequality constraints in formula (29)；μ is barrier parameter, μ
≥0；S=1,2 ..., NS；
According to Karush-Kuhn-Tucker optimality conditions, local derviation is asked to Augmented Lagrangian Functions formula (30), one group is obtained
Nonlinear System of Equations, then can be obtained with Newton Algorithm simplifying update equation group；
The update equation and variable after simplification are ranked up by the order of prediction scene and error scene, its following coefficient is obtained
Matrix has the simplification update equation group of diagonal edged structure：
Wherein, Δ Z0=[(Δ x0)T,(Δy)T,Δλ]T；ΔZs=[(Δ xs)T,(Δys)T]T；L0、LsAnd MsIt is symmetrical sparse
Matrix；S=1,2 ..., NS。
9. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 8 decoupled based on scene with asynchronous iteration, its
Be characterized in, the step 33) detailed process be：
Formula (31) equation is equally decoupled as NSThe equation group of+1 low-dimensional, i.e.,：
<mrow>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
<msub>
<mi>&Delta;Z</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</munderover>
<msub>
<mi>M</mi>
<mi>s</mi>
</msub>
<msub>
<mi>&Delta;Z</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<msub>
<mi>b</mi>
<mn>0</mn>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>32</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>M</mi>
<mi>s</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>&Delta;Z</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<msub>
<mi>L</mi>
<mi>s</mi>
</msub>
<msub>
<mi>&Delta;Z</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<msub>
<mi>b</mi>
<mi>s</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>33</mn>
<mo>)</mo>
</mrow>
</mrow>
Formula (32) and formula (33) are converted into following form with the method for synchronous iteration：
<mrow>
<msubsup>
<mi>&Delta;Z</mi>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</munderover>
<msub>
<mi>M</mi>
<mi>s</mi>
</msub>
<msubsup>
<mi>L</mi>
<mi>s</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msubsup>
<mi>M</mi>
<mi>s</mi>
<mi>T</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<msub>
<mi>b</mi>
<mn>0</mn>
</msub>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</munderover>
<msub>
<mi>M</mi>
<mi>s</mi>
</msub>
<msubsup>
<mi>L</mi>
<mi>s</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>b</mi>
<mi>s</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>34</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>&Delta;Z</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>L</mi>
<mi>s</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>b</mi>
<mi>s</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>M</mi>
<mi>s</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>&Delta;Z</mi>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>35</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, s=1,2 ..., NS。
10. the multiple target stochastic and dynamic economic load dispatching method as claimed in claim 9 decoupled based on scene with asynchronous iteration, its
Be characterized in, the step 34) detailed process be：Two end points are calculated according to NBI methods first, then calculated again on Utopia's line
Other Pareto optimal solutions.
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| CN107657392A (en) * | 2017-10-26 | 2018-02-02 | 燕山大学 | A kind of Granule Computing method for power network economy of large scale scheduling problem |
| CN108321792A (en) * | 2018-01-15 | 2018-07-24 | 国网山东省电力公司莱芜供电公司 | The more scene Cooperative Optimization Algorithms of Electrical Power System Dynamic economic load dispatching of meter and wind-powered electricity generation |
| CN108667077A (en) * | 2018-04-10 | 2018-10-16 | 燕山大学 | A kind of wind storage association system Optimization Scheduling |
| CN109193756A (en) * | 2018-09-04 | 2019-01-11 | 华南理工大学 | A kind of scene decoupling dynamic economic dispatch model solution method of wind power integration system |
| CN109375588A (en) * | 2018-02-12 | 2019-02-22 | 浙江大学 | A kind of factory considering General Requirement response provides multiple forms of energy to complement each other Optimization Scheduling |
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| CN107657392A (en) * | 2017-10-26 | 2018-02-02 | 燕山大学 | A kind of Granule Computing method for power network economy of large scale scheduling problem |
| CN107657392B (en) * | 2017-10-26 | 2021-01-08 | 燕山大学 | Particle calculation method for large-scale economic dispatching problem of power grid |
| CN108321792A (en) * | 2018-01-15 | 2018-07-24 | 国网山东省电力公司莱芜供电公司 | The more scene Cooperative Optimization Algorithms of Electrical Power System Dynamic economic load dispatching of meter and wind-powered electricity generation |
| CN108321792B (en) * | 2018-01-15 | 2020-09-04 | 国网山东省电力公司莱芜供电公司 | Wind power-related dynamic economic dispatching multi-scene collaborative optimization algorithm for power system |
| CN109375588A (en) * | 2018-02-12 | 2019-02-22 | 浙江大学 | A kind of factory considering General Requirement response provides multiple forms of energy to complement each other Optimization Scheduling |
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| CN108667077A (en) * | 2018-04-10 | 2018-10-16 | 燕山大学 | A kind of wind storage association system Optimization Scheduling |
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