CN107302231A - A kind of small hydropower system accesses the random economic load dispatching method of two targets of power network - Google Patents
A kind of small hydropower system accesses the random economic load dispatching method of two targets of power network Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a kind of random economic load dispatching method of two targets that small hydropower system accesses power network, it comprises the following steps：1) calculating parameter is obtained：Calculating parameter is obtained from the power system of the small hydropower system containing radial-flow type；2) the random economic load dispatching model of two targets is set up：Set up with the stochastic and dynamic economic load dispatching model of power purchase expense and the minimum target of dusty gas discharge capacity；3) the random economic load dispatching model of two targets is solved：Interior point method is improved using scene decoupling and asynchronous iteration to solve the random economic load dispatching model of two targets；4) economic dispatch program is obtained：By obtaining economic dispatch program based on the improvement Pareto frontal analysis of compromise optimal solution.The present invention solves radial-flow type small hydropower system stochastic optimization problems, largely improves randomness power supply predictablity rate.
Description
Technical field
The present invention relates to a kind of random economic load dispatching method of two targets that small hydropower system accesses power network, belong to power system fortune
Row service technique field.
Background technology
In recent years, as environmental pollution and lack of energy problem are increasingly severe, for the utilization of clean reproducible energy
More and more paid attention to by countries in the world.China's development and utilization regenerative resource, the dynamics of Optimization of Energy Structure
Also continue to increase.At present, using wind-powered electricity generation, every technology of photovoltaic has tended to be ripe.Water power is the renewable sources of energy of high-quality, same to wind
It is electric the same, it is also the clear energy sources of energy sustainable use.China's Small Hydropower Construction oneself through achieving very big effect, it is especially western
Concentrating type feature is presented in small hydropower plants in mountainous area.
As traditional water conservancy changes to resources WR, sustainable development water conservancy, how Effec-tive Function depth excavates small power station
Benefit is increasingly paid attention to.Small power station's operational management should also be managed from routine dispactching to be changed to Optimized Operation.Wind-powered electricity generation is come excellent
The thinking for changing scheduling is transplanted in the scheduling of small hydropower system, the positive role of small power station can be played to greatest extent, and promote
The sustainable development Optimal Scheduling of small power station is a stochastic optimization problems, at the same be also extensive, multiple target, it is non-linear
Optimization problem.
As energy-saving and emission-reduction policy is put into effect, variation is presented in optimal dispatch target, but not yet has for small water at present
The random economic dispatch program of electric group's access power network occurs.
The content of the invention
In view of the shortcomings of the prior art, the present invention proposes a kind of random economic tune of two targets of small hydropower system access power network
Degree method, it is presented the diversified random economic load dispatching for solving small hydropower system access power network for optimal dispatch target and asked
Topic.
The present invention solves its technical problem and adopted the technical scheme that：
A kind of small hydropower system provided in an embodiment of the present invention accesses the random economic load dispatching method of two targets of power network, and it includes
Following steps：
1) calculating parameter is obtained：Calculating parameter is obtained from the power system of the small hydropower system containing radial-flow type；
2) the random economic load dispatching model of two targets is set up：Set up with power purchase expense and the minimum target of dusty gas discharge capacity
Stochastic and dynamic economic load dispatching model；
3) the random economic load dispatching model of two targets is solved：Interior point method pair is improved using scene decoupling and asynchronous iteration
The random economic load dispatching model of two targets is solved；
4) economic dispatch program is obtained：By obtaining economic tune based on the improvement Pareto frontal analysis of compromise optimal solution
Degree scheme.
As a kind of possible implementation of the present embodiment, in step 1) in, the calculating parameter includes given conventional hair
Group of motors cost coefficient and bound of exerting oneself, transmission of electricity branch impedance and capacity parameter, Hydropower Unit operational factor and system loading.
As a kind of possible implementation of the present embodiment, in step 2) in, the random economic load dispatching model of two targets
In object function include：
1. power purchase the goal of cost function：
In formula：agIt is gthPlatform conventional power generation usage unit expense；ahydroIt is the power purchase expense of small power station, it is considered to small power station's power purchase
Expense is because country encourages power network to access new energy by the way of subsidy now；Represent g in prediction scenethPlatform is conventional
Unit is exerted oneself t；Represent h in prediction scenethIndividual small hydropower station cluster is exerted oneself t；NGAnd NHIt is corresponding
Conventional power unit number of units and small hydropower station cluster number；
2. dusty gas discharge capacity object function：
In formula, b2,g、b1,gAnd b0,gIt is gthThe dusty gas emission factor of platform conventional power unit.
As a kind of possible implementation of the present embodiment, in step 2) in, the random economic load dispatching model of two targets
In constraints include：
1. power-balance constraint：
Active power balance constraint in prediction scene and error scene is expressed as follows：
In formula：It is g in prediction scene and error scenethActive power output of the platform conventional power unit in t；Represent
Predict h in scene and error scenethActive power output of the individual small hydropower station cluster in t；PmtLoad bus m is represented in t
Load；NDIt is load bus number, NS=NG+NH；
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：
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 is the dispatching response time needed for adapting to wind power output predicated error；
3. small power station's units limits：
4. network transmission is constrained：
Predict that the active power constraint expression in scene and error scene on transmission line is as follows：
In formula：PlmaxIt is circuit l maximum transfer capacity；NLIt is the number of lines；It is prediction scene
With the active power transmitted on error scene center line road l, its DC power flow expression formula is expressed as follows：
In formula, Glg、FlhAnd DldIt is active power transfers of the circuit l for conventional power unit, small power station's group of planes and load respectively
The factor.
Be used as a kind of possible implementation of the present embodiment, the step 3) detailed process comprise the following steps：
31) from two target problems to the conversion of single-objective problem
Two objective optimisation problems are converted to by a series of single-object problems using normal boundary-intersected method, and using non-
Linear prim al- dual interior point m ethod is solved；
32) single-objective problem is solved using interior point method
It is expressed as follows using the Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm：
Wherein, y, ysAnd yhFor Lagrange multiplier vector；NhIt is inequality constraints in the random economic load dispatching model of two targets
Quantity；μ is barrier parameter, μ >=0；
According to Karush-Kuhn-Tucker (KKT) optimality condition, local derviation is asked to Augmented Lagrangian Functions, one is obtained
Group Nonlinear System of Equations, then can be obtained with Newton Algorithm simplifying update equation group：
In formula：ΔZ0For the vector of the increment composition with predicting scene correlated variables, Δ Z0=[Δ x0,Δy3,Δλ]T；Δ
Zs(i=1,2 ..., S) is the vector constituted with the increment of error scene correlated variables,L0、
Ls(s=1,2 ..., S), Ms(s=1,2 ..., S) is symmetrical and sparse, and their dimension is respectively 96 (N+1)+3,96 (N
+1)、96(N+1)；
33) the scene decoupling of update equation is simplified：
By formula (11) expansion, obtain：
It is as follows with synchronous iteration derived expression：
During with synchronous iteration calculating formula (12) and formula (13), L0、LsAnd MsAll it is sparse matrix；
Assuming that first time iteration Δ Z0It is zero, and is completed to kth time iteration, then k+1 times iteration expression formula is：
The dimension of equation (16) and the coefficient matrix of (17) is respectively 96 (N+1)+3 and 96 (N+1).
As a kind of possible implementation of the present embodiment, in step 4) in, the improvement handkerchief based on compromise optimal solution
The process of tired support frontal analysis is：Pareto optimality disaggregation is tried to achieve using NBI methods, and the entropy weight double base points method of integrated weighting is carried out
Correct, excavate Pareto forward position solution and concentrate the different information for each solving and being contained, one group of acquisition is reasonable, effective, can be scheduled for
The scheduling strategy of personnel's decision-making.
Be used as a kind of possible implementation of the present embodiment, the step 4) process comprise the following steps：
41) 10 points, are calculated with NBI methods, acquisition optimal solution is estimated with entropy assessment；
42) the upper and subsequent point for, connecting optimal solution point is Utopia's line, calculates 10 points with NBI methods again；
43) optimal solution obtained on Utopia's line is estimated with entropy assessment again.
The technical scheme of the embodiment of the present invention can have the advantage that as follows：
The random Economic Dispatch Problem of two targets is converted into extensive two by technical scheme of the embodiment of the present invention using scene method
Target certainty dynamic scheduling problem, then by normal boundary-intersected (normal boundary intersect ion, NBI)
Method is translated into a series of extensive single-objective nonlinear programming problems, and is solved with nonlinear primal-dual interior-point algorithm.Answering
During solving these extensive single-objective nonlinear programming problems with nonlinear primal-dual interior-point algorithm, arranged according to Episode sequences
The coefficient matrix of simplification update equation there is diagonal edged structure.Therefore decoupling can be implemented to it, and use asynchronous block iteration
Method is solved to the low-dimensional update equation group after decoupling, and obtains a series of equally distributed Pareto optimal solutions, forms handkerchief
Tired support forward position curve, then selects compromise optimal solution using entitled entropy weight double base points method from Pareto forward position solution concentration.This
Invention solves radial-flow type small hydropower system stochastic optimization problems, largely improves randomness power supply predictablity rate；Pass through
The High-dimensional Linear update equation group with diagonal edged structure is decoupled with asynchronous block iteration method, so as to be greatly lowered
Storage demand, enhances the big system of adaptation, the computing capability of many scenes, and lifting calculating speed uses entitled entropy weight double base points
Method, and Pareto optimal solution is modified with reference to NBI methods, it can fully excavate information and tune that Pareto forward position disaggregation contains
The experience of degree personnel, obtains one group of operating point that is considerable, reasonable, needing, is scheduled for personnel's decision-making.
Brief description of the drawings
Fig. 1 is a kind of random economic load dispatching of two targets of small hydropower system access power network according to an exemplary embodiment
Method flow diagram；
Fig. 2 is a kind of improvement Pareto frontal analysis based on compromise optimal solution according to an exemplary embodiment
The schematic diagram of optimal solution is obtained in journey first；
Fig. 3 is a kind of improvement Pareto frontal analysis based on compromise optimal solution according to an exemplary embodiment
The schematic diagram of Cheng Zhong Utopias line；
Fig. 4 is a kind of improvement Pareto frontal analysis based on compromise optimal solution according to an exemplary embodiment
The schematic diagram of the optimal solution on Utopia's line is obtained in journey.
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.
Fig. 1 is a kind of random economic load dispatching of two targets of small hydropower system access power network according to an exemplary embodiment
Method flow diagram.As shown in figure 1, a kind of random economic load dispatching method of two targets of small hydropower system access power network can include it is following
Step：
1) calculating parameter is obtained：
Calculating parameter is obtained from the power system of the small hydropower system containing radial-flow type；The calculating parameter includes given conventional power generation usage
Unit cost coefficient and bound of exerting oneself, transmission of electricity branch impedance and capacity parameter, Hydropower Unit operational factor and system loading；
2) the random economic load dispatching model of two targets is set up：
Set up with the stochastic and dynamic economic load dispatching model of power purchase expense and the minimum target of dusty gas discharge capacity；
3) the random economic load dispatching model of two targets is solved：
Interior point method is improved using scene decoupling and asynchronous iteration to solve the random economic load dispatching model of two targets；
4) economic dispatch program is obtained：
By obtaining economic dispatch program based on the improvement Pareto frontal analysis of compromise optimal solution.
In a kind of possible implementation, in step 2) described in target letter in the random economic load dispatching model of two targets
Number includes：
1. power purchase the goal of cost function：
First aim function is to minimize the power purchase expense under prediction scene：
In formula：agIt is gthPlatform conventional power generation usage unit expense；ahydroIt is the power purchase expense of small power station, it is considered to small power station's power purchase
Expense is because country encourages power network to access new energy by the way of subsidy now；Represent g in prediction scenethPlatform is normal
Advise unit exerting oneself in t；Represent h in prediction scenethIndividual small hydropower station cluster is exerted oneself t；NGAnd NHIt is phase
The conventional power unit number of units and small hydropower station cluster number answered.
2. dusty gas discharge capacity object function：
Second target is that the dusty gas discharge capacity for minimizing conventional power unit (thinks SO2, NOx master), it is defined as follows：
In formula, b2,g、b1,gAnd b0,gIt is gthThe dusty gas emission factor of platform conventional power unit.
In a kind of possible implementation, in step 2) in, the constraint in the random economic load dispatching model of two targets
Condition includes：
1. power-balance constraint：
The active power balance constraint ignored in the active power loss in power network, prediction scene and error scene is stated such as
Under：
In formula：It is g in prediction scene and error scenethPlatform conventional power unit has t
Work(is exerted oneself；Represent h in prediction scene and error scenethIndividual small hydropower station cluster is in the active of t
Exert oneself；PmtRepresent loads of the load bus m in t；NDIt is load bus number, NS=NG+NH。
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；Δ T takes 15 minutes in the present embodiment；
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 is the dispatching response time needed for adapting to wind power output predicated error, originally
Δ T ' takes 15 minutes in embodiment.
3. small power station's units limits：
When power system spare capacity deficiency or electrical grid transmission off-capacity, the electrical phenomena of abandoning in generation of electricity by new energy is not
It is evitable.Small power station is allowed to occur abandoning electricity herein, small power station's units limits are expressed as follows：
4. network transmission is constrained：
Predict that the active power constraint expression in scene and error scene on transmission line is as follows：
In formula：PlmaxIt is circuit l maximum transfer capacity；NLIt is the number of lines；It is prediction field
The active power transmitted on scape and error scene center line road l, its DC power flow expression formula is expressed as follows：
In formula, Glg、FlhAnd DldIt is active power transfers of the circuit l for conventional power unit, small power station's group of planes and load respectively
The factor.
A kind of in possible implementation, the step 3) detailed process comprise the following steps：
31) from two target problems to the conversion of single-objective problem：
Two objective optimisation problems are converted to by a series of single-object problems using normal boundary-intersected method, and using non-
Linear prim al- dual interior point m ethod is solved.
32) single-objective problem is solved using interior point method：
It is expressed as follows using the Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm：
Wherein, y, ysAnd yhFor Lagrange multiplier vector；NhIt is inequality constraints in the random economic load dispatching model of two targets
Quantity；μ is barrier parameter, μ >=0.
According to Karush-Kuhn-Tucker (KKT) optimality condition, local derviation is asked to Augmented Lagrangian Functions, one is obtained
Group Nonlinear System of Equations, then can be obtained with Newton Algorithm simplifying update equation group.By prediction scene and the order of error scene
Update equation and variable after simplification is ranked up, the simplification that can obtain its following coefficient matrix has diagonal edged structure is repaiied
Positive equation group：
In formula：ΔZ0For the vector of the increment composition with predicting scene correlated variables, Δ Z0=[Δ x0,Δy3,Δλ]T；Δ
Zs(i=1,2 ..., S) is the vector constituted with the increment of error scene correlated variables,L0、
Ls(s=1,2 ..., S), Ms(s=1,2 ..., S) is symmetrical and sparse, and their dimension is respectively 96 (N+1)+3,96 (N
+1)、96(N+1)。
33) the scene decoupling of update equation is simplified：
For big system, equation (11) is a sparse High-dimensional Linear equation group, and it is many mesh that it, which is implemented, effectively to solve
Mark random optimization dynamic economic dispatch the very corn of a subject.The dimension of the coefficient matrix of equation (11) is 96 (N+1) (S+1)+3.
During actual example is calculated, when big system, many scenes, possibly it can not be solved.Coefficient square in equation (11)
Battle array has diagonal edged structure, and similar structure has application in trend and optimal load flow are calculated, therefore the present invention utilizes this
Plant after design feature is decoupled to it and solve again.
By formula (11) expansion, obtain：
It is as follows with synchronous iteration derived expression：
During with synchronous iteration calculating formula (12) and formula (13), L0、LsAnd MsAll it is sparse matrix；But formula
(14) inIt is a full battle array, its calculating can take substantial amounts of internal memory and calculating time.Solving High-dimensional Linear side
During journey group, the efficiency of asynchronous iteration is more much higher than synchronous iteration sometimes.If with asynchronous iteration calculating formula (12) and formula (13),
Then the matrix in calculating process is all sparse matrix.
Assuming that first time iteration Δ Z0It is zero, and is completed to kth time iteration, then k+1 times iteration expression formula is：
It is separate between each scene during calculating formula (16), can be with parallel computation, equation (16) when scene number is more
(17) dimension of coefficient matrix is respectively 96 (N+1)+3 and 96 (N+1), based on this, you can realized to big system, many scenes
Situation effective solution.
Because the calculating of different Pareto (Pareto) optimal solutions is separate, therefore, when asking for Pareto forward positions
Can also use be not present between parallel computation, and each Pareto optimal solutions communication issue, i.e. host process and from process it
Between variable transferring is not present, then, parallel computation is carried out to Pareto forward positions and will increase substantially the computational efficiency of algorithm.
In a kind of possible implementation, in step 4) in, the improvement Pareto forward position based on compromise optimal solution
The process of analysis is：Pareto optimality disaggregation is tried to achieve using NBI methods, and the entropy weight double base points method of integrated weighting is modified, and is dug
Pick Pareto forward position solution concentrates the different information for each solving and being contained, and one group of acquisition is reasonable, effective, be available for dispatcher's decision-making
Scheduling strategy.
For the model and data of better astringency, what NBI methods were obtained is to be uniformly distributed very much Pareto forward position.But simultaneously
Not all optimal solution is involved in the decision-making of dispatcher.After the preference of known dispatcher, Pareto optimal solution can be caused
Distribution more becomes more meticulous.
A kind of in possible implementation, the step 4) process comprise the following steps：
41) 10 points, are calculated with NBI methods, acquisition optimal solution is estimated with entropy assessment；As shown in Fig. 2 optimal solution
For the 5th point, complex optimum degree is 0.706 4 (compromise optimal solutions 2).
42) the upper and subsequent point for, connecting optimal solution point is Utopia's line, as shown in figure 3, being calculated again with NBI methods
10 points.
43) optimal solution obtained on Utopia's line is estimated with entropy assessment again.As shown in figure 4, optimal solution is newly-increased
The 6th point (circle is marked) in point, complex optimum degree is 0.710 5 (being designated as compromise optimal solution 3).With single object optimization institute
The target function value for obtaining scheduling scheme compares such as table 1.
The Pareto endpoint value of table 1 and compromise optimal solution
From table 1 it follows that compromise optimal solution 3 corresponding two target function values and the phases of two single object optimization solutions
Answer desired value to be all closer to, be that two targets are carried out to coordinate and optimize obtain preferably solution.
For the power system of the small hydropower system containing radial-flow type, establish minimum with power purchase expense and dusty gas discharge capacity
The stochastic and dynamic economic load dispatching model of target.By scene method by the model conversation be extensive two targets certainty dynamic dispatching
Model.Two objective optimisation problems are converted to by a series of single-object problems using normal boundary-intersected method, and use non-thread
Property prim al- dual interior point m ethod solve.In an iterative process, the coefficient matrix for simplifying update equation is arranged as pair according to Episode sequences
Angle edged form, it is convenient to implement decoupling to it, and solved with asynchronous block iteration method, so that by one group of higher-dimension update equation group
Solve the solution for being converted into several respectively with prediction scene and the corresponding low-dimensional update equation group of error scene.To aqueous
When the data of the abundant provincial power network of electric resources carry out simulation calculation, parallel computation frame is established in High-Performance Computing Cluster with slow
Solution calculates committed memory and improves calculating speed.By this computing architecture, one group of operation plan a few days ago can be obtained.
The present invention solves radial-flow type small hydropower system stochastic optimization problems, largely improves the prediction of randomness power supply accurate
True rate；By being decoupled with asynchronous block iteration method to the High-dimensional Linear update equation group with diagonal edged structure, so that
Storage demand is considerably reduced, the big system of adaptation, the computing capability of many scenes is enhanced, lifting calculating speed is using entitled
Entropy weight double base points method, and Pareto optimal solution is modified with reference to NBI methods, it can fully excavate Pareto forward position disaggregation and contain
Information and dispatcher experience, obtain one group it is considerable, reasonable, need operating points, be scheduled for personnel's decision-making.
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 (7)
1. a kind of small hydropower system accesses the random economic load dispatching method of two targets of power network, it is characterized in that, comprise the following steps：
1) calculating parameter is obtained：Calculating parameter is obtained from the power system of the small hydropower system containing radial-flow type；
2) the random economic load dispatching model of two targets is set up：Set up with power purchase expense and the minimum target of dusty gas discharge capacity with
Machine dynamic economic dispatch model；
3) the random economic load dispatching model of two targets is solved：Interior point method is improved to two mesh using scene decoupling and asynchronous iteration
Random economic load dispatching model is marked to be solved；
4) economic dispatch program is obtained：By obtaining economic load dispatching side based on the improvement Pareto frontal analysis of compromise optimal solution
Case.
2. a kind of small hydropower system as claimed in claim 1 accesses the random economic load dispatching method of two targets of power network, it is characterized in that,
In step 1) in, the calculating parameter includes given conventional generator group cost coefficient and bound of exerting oneself, transmission of electricity branch impedance
And capacity parameter, Hydropower Unit operational factor and system loading.
3. a kind of small hydropower system as claimed in claim 1 accesses the random economic load dispatching method of two targets of power network, it is characterized in that,
In step 2) in, the object function in the random economic load dispatching model of two targets includes：
1. power purchase the goal of cost function：
<mrow>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<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>h</mi>
<mi>y</mi>
<mi>d</mi>
<mi>r</mi>
<mi>o</mi>
</mrow>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>h</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>H</mi>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>h</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>
In formula：agIt is gthPlatform conventional power generation usage unit expense；ahydroIt is the power purchase expense of small power station；Represent the in prediction scene
gthPlatform conventional power unit is exerted oneself t；Represent h in prediction scenethIndividual small hydropower station cluster is exerted oneself t；NGWith
NHIt is corresponding conventional power unit number of units and small hydropower station cluster number；
2. dusty gas discharge capacity object function：
<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>
<mo>,</mo>
<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>
In formula, b2,g、b1,gAnd b0,gIt is gthThe dusty gas emission factor of platform conventional power unit.
4. a kind of small hydropower system as claimed in claim 3 accesses the random economic load dispatching method of two targets of power network, it is characterized in that,
In step 2) in, the constraints in the random economic load dispatching model of two targets includes：
1. power-balance constraint：
Active power balance constraint in prediction scene and error scene is expressed as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<munderover>
<mi>&Sigma;</mi>
<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>
<mi>&Sigma;</mi>
<mrow>
<mi>h</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mi>H</mi>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<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>
<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>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：It is g in prediction scene and error scenethActive power output of the platform conventional power unit in t；Represent prediction field
H in scape and error scenethActive power output of the individual small hydropower station cluster in t；PmtLoad bus m is represented in the negative of t
Lotus；NDIt is load bus number, NS=NG+NH；
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：
The constraint representation is 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>
<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>1</mn>
<mo>,</mo>
<mn>2</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>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. small power station's units limits：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
<mo>&le;</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
<mi>s</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>h</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>N</mi>
<mi>H</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. network transmission is constrained：
Predict that the active power constraint expression in scene and error scene on transmission line is as follows：
<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>8</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：PlmaxIt is circuit l maximum transfer capacity；NLIt is the number of lines；It is prediction scene and error scene center line road l
The active power of upper transmission, its DC power flow expression formula is expressed as follows：
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<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>
<mi>&Sigma;</mi>
<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>h</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<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>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, Glg、FlhAnd DldCircuit l respectively for conventional power unit, small power station's group of planes and load active power transfer because
Son.
5. a kind of small hydropower system as claimed in claim 1 accesses the random economic load dispatching method of two targets of power network, it is characterized in that,
The step 3) detailed process comprise the following steps：
31) from two target problems to the conversion of single-objective problem：
Two objective optimisation problems are converted to by a series of single-object problems using normal boundary-intersected method, and using non-linear
Prim al- dual interior point m ethod is solved；
32) single-objective problem is solved using interior point method：
It is expressed as follows using the Augmented Lagrangian Functions in nonlinear primal-dual interior-point algorithm：
<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>
<mo>&Sigma;</mo>
<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>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mi>s</mi>
</msup>
<mo>)</mo>
</mrow>
</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>
<mo>&Sigma;</mo>
<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>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, y, ysAnd yhFor Lagrange multiplier vector；NhIt is the number of inequality constraints in the random economic load dispatching model of two targets
Amount；μ is barrier parameter, μ >=0；
According to Karush-Kuhn-Tucker optimality conditions, local derviation is asked to Augmented Lagrangian Functions, obtain one group it is non-linear
Equation group, then can be obtained with Newton Algorithm simplifying update equation group：
<mrow>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>M</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>M</mi>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>M</mi>
<mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>M</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>M</mi>
<mn>1</mn>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<msub>
<mi>L</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>M</mi>
<mn>2</mn>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<msub>
<mi>L</mi>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>M</mi>
<mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<msub>
<mi>L</mi>
<mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>M</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<msub>
<mi>L</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&CenterDot;</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;Z</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;Z</mi>
<mn>1</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;Z</mi>
<mn>2</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>...</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;Z</mi>
<mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;Z</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mn>0</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mn>1</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>...</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mrow>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：ΔZ0For the vector of the increment composition with predicting scene correlated variables, Δ Z0=[Δ x0,Δy3,Δλ]T；ΔZs(i
=1,2 ..., S) it is the vector constituted with the increment of error scene correlated variables,L0、Ls(s
=1,2 ..., S), Ms(s=1,2 ..., S) is symmetrical and sparse, and their dimension is respectively 96 (N+1)+3,96 (N+
1)、96(N+1)；
33) the scene decoupling of update equation is simplified：
By formula (11) expansion, obtain：
<mrow>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
<mo>&CenterDot;</mo>
<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>
<mi>S</mi>
</munderover>
<msub>
<mi>M</mi>
<mi>s</mi>
</msub>
<mo>&CenterDot;</mo>
<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>12</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>M</mi>
<mi>s</mi>
<mi>T</mi>
</msubsup>
<mo>&CenterDot;</mo>
<msub>
<mi>&Delta;Z</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<msub>
<mi>L</mi>
<mi>s</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>&Delta;Z</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<msub>
<mi>b</mi>
<mi>s</mi>
</msub>
<mo>,</mo>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>S</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
It is as follows with synchronous iteration derived expression：
<mrow>
<msubsup>
<mi>&Delta;Z</mi>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<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>
</mrow>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mrow>
<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>
</mrow>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>14</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>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<msub>
<mi>N</mi>
<mi>S</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
During with synchronous iteration calculating formula (12) and formula (13), L0、LsAnd MsAll it is sparse matrix；
Assuming that first time iteration Δ Z0It is zero, and is completed to kth time iteration, then k+1 times iteration expression formula is：
<mrow>
<msubsup>
<mi>&Delta;Z</mi>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>L</mi>
<mn>0</mn>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>b</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>
<msubsup>
<mi>&Delta;Z</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>16</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>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<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>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>17</mn>
<mo>)</mo>
</mrow>
</mrow>
The dimension of equation (16) and the coefficient matrix of (17) is respectively 96 (N+1)+3 and 96 (N+1).
6. a kind of small hydropower system as described in claim 1 to 5 any one accesses the random economic load dispatching side of two targets of power network
Method, it is characterized in that, in step 4) in, the process of the improvement Pareto frontal analysis based on compromise optimal solution is：Using NBI
Method tries to achieve Pareto optimality disaggregation, and the entropy weight double base points method of integrated weighting is modified, and excavates Pareto forward position solution and concentrates every
It is individual to solve the different information contained, obtain one group of scheduling strategy that is reasonable, effective, being available for dispatcher's decision-making.
7. a kind of small hydropower system as claimed in claim 7 accesses the random economic load dispatching method of two targets of power network, it is characterized in that,
The step 4) process comprise the following steps：
41) 10 points, are calculated with NBI methods, acquisition optimal solution is estimated with entropy assessment；
42) the upper and subsequent point for, connecting optimal solution point is Utopia's line, calculates 10 points with NBI methods again；
43) optimal solution obtained on Utopia's line is estimated with entropy assessment again.
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| CN108155674A (en) * | 2018-02-01 | 2018-06-12 | 清华大学 | Consider the Hydro-Thermal Systems combined scheduling method and system of uncertain distribution character |
| CN108448650A (en) * | 2018-03-21 | 2018-08-24 | 广东电网有限责任公司电力科学研究院 | A kind of multiple target combined dispatching decision-making technique and system |
| CN109299531A (en) * | 2018-09-12 | 2019-02-01 | 清华四川能源互联网研究院 | Electromagnetical transient emulation method and device |
| CN110212533A (en) * | 2019-07-10 | 2019-09-06 | 南方电网科学研究院有限责任公司 | A kind of prosumer's power determining method and system |
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- 2017-08-23 CN CN201710730890.5A patent/CN107302231A/en active Pending
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| CN108155674A (en) * | 2018-02-01 | 2018-06-12 | 清华大学 | Consider the Hydro-Thermal Systems combined scheduling method and system of uncertain distribution character |
| CN108155674B (en) * | 2018-02-01 | 2020-02-21 | 清华大学 | Water, fire and electricity combined dispatching method and system considering uncertain distribution characteristics |
| CN108448650A (en) * | 2018-03-21 | 2018-08-24 | 广东电网有限责任公司电力科学研究院 | A kind of multiple target combined dispatching decision-making technique and system |
| CN109299531A (en) * | 2018-09-12 | 2019-02-01 | 清华四川能源互联网研究院 | Electromagnetical transient emulation method and device |
| CN110212533A (en) * | 2019-07-10 | 2019-09-06 | 南方电网科学研究院有限责任公司 | A kind of prosumer's power determining method and system |
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| CN110929913B (en) * | 2019-09-27 | 2022-04-19 | 河海大学 | Multi-target power generation plan decomposition coordination calculation method for direct-current cross-district interconnected power grid |
| CN110829502A (en) * | 2019-10-17 | 2020-02-21 | 广西电网有限责任公司电力科学研究院 | Multi-target interval power generation scheduling method considering new energy |
| CN110829502B (en) * | 2019-10-17 | 2022-06-21 | 广西电网有限责任公司电力科学研究院 | Multi-target interval power generation scheduling method considering new energy |
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