CN107944638A - A kind of new energy based on temporal correlation does not know set modeling method - Google Patents

A kind of new energy based on temporal correlation does not know set modeling method Download PDF

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CN107944638A
CN107944638A CN201711349694.XA CN201711349694A CN107944638A CN 107944638 A CN107944638 A CN 107944638A CN 201711349694 A CN201711349694 A CN 201711349694A CN 107944638 A CN107944638 A CN 107944638A
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文劲宇
张艺镨
艾小猛
姚伟
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Huazhong University of Science and Technology
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Abstract

The invention discloses a kind of new energy based on temporal correlation not to know set modeling method, belongs to electrical engineering technical field.The method of the present invention collects the historical data that all new energy stations are contributed first, and daily historical data is a historic scenery；A minimum closure higher-dimension ellipsoid that can surround all historic sceneries is solved afterwards；Vertex of the multiple vertex of the ellipsoid as uncertain set is chosen again, that is, is asked for the polyhedral expression formula of broad sense that the vertex surrounds and described the uncertain set；Scaling coefficient amendment is finally introducing not know to gather.New uncertain set is made of the convex closure that limited a limit scene is surrounded in the method for the present invention, can be extended to easily in existing two benches robust Optimal methods；And the number of limit scene and the number of stochastic variable linearly increase, and ensure that the solving speed of robust Optimal methods；The method carried at the same time in the present invention can change coefficient according to the actual requirements to adjust the economy of set.

Description

A kind of new energy based on temporal correlation does not know set modeling method
Technical field
The invention belongs to electrical engineering technical field, more particularly, to a kind of new energy based on temporal correlation not Determine set modeling method.
Background technology
As energy crisis and the continuous of problem of environmental pollution aggravate, the permeability of the new energy in power grid such as photovoltaic wind Be continuously increased, and new energy shows very strong randomness, intermittent and fluctuation, and its precision of prediction is relatively low, this without suspected of Traditional operation of power networks pattern brings huge challenge, more in traditional scheduling to ask economic load dispatching from the point of view of scheduling aspect Topic, a few days ago Optimization of Unit Commitment By Improved are described as a deterministic mathematical optimization problem, and after random new energy introduces, this is true Qualitative question gradually becomes stochastic optimization problems.For this problem, existing document is more excellent using chance constrained programming, robust The methods of change, handles the randomness in model.For chance constraint method, it is constrained is met in the form of probability, this is just meaned The scheduling strategy for finally solving and obtaining to be possible to be unsatisfactory for related constraint, and then threaten the safe operation of power grid.And robust Optimization method is then that uncertain parameter is described by establishing uncertain set, to ensure that it is all that scheduling strategy disclosure satisfy that Uncertain parameter value.But uncertain set is retouched using boxlike set more in existing robust Optimal methods State, i.e., without considering the spatial coherence between new energy station and the single game station temporal correlation of itself, this is undoubtedly increased not Determine the volume of set, it is overly conservative to result in the scheduling strategy obtained by traditional robust Optimization Solution, and then causes economy Decline, or even occur that no scheduling strategy meets the situation of constraint.
The content of the invention
For the disadvantages described above or Improvement requirement of the prior art, the present invention provides a kind of new energy based on temporal correlation Source does not know set modeling method, its object is to go out force data by collecting the relevant history of new energy, constructs a kind of new The uncertain set modeling method of the consideration new energy output correlation of type, the uncertain set thereby determined that is by limited a limit The convex closure that scene is surrounded is formed, and is the polyhedron of broad sense, can be extended to easily in existing two benches robust Optimal methods, And the number of limit scene and the number of stochastic variable linearly increase, and ensure that the solving speed of robust Optimal methods.
To achieve the above object, the present invention provides a kind of new energy based on temporal correlation not to know set modeling side Method, the described method includes：
(1) historical data contributed all new energy stations is collected, daily historical data is a historic scenery；
(2) a minimum closure higher-dimension ellipsoid that can surround all historic sceneries is solved；
(3) multiple vertex of the ellipsoid are chosen as uncertain set, ask for the broad sense polyhedron that the vertex surrounds Expression formula the uncertain set is described；
(4) scaling coefficient amendment is introduced not knowing to gather.
Further, the step (1) specifically includes：
(11) historical data that each new energy station for being collected into is contributed and to power generating value standardization, historical data is used Matrix PWRepresent：
Wherein, NWFor new energy station quantity；T1For the when hop count of collected historical data；
(12) historical data being collected into daily is divided, it is a historic scenery P to remember daily historical dataW′：
Wherein, NdRepresent history number of days；T represent one day in when hop count, T1=NdT；
(13) above-mentioned matrix is written as to the form of matrix in block form,Wherein, ginseng is not known Number, k=1,2,3 ..., Nd。
Further in the equation of minimum closure higher-dimension ellipsoid is in the step (2)：
Wherein, Q is positive definite matrix, represents departure degree of the symmetry axis to reference axis of the higher-dimension ellipsoid,R represents real number set；For the central point of higher-dimension ellipsoid, []TRepresent to turn Put；ω represents the value of uncertain parameter；NWFor new energy station quantity；T represent one day in when hop count.
Further, the step (3) specifically includes：
(31) decomposition, Q=P are orthogonalized to positive definite matrix QTDP=P-1DP, wherein, D matrix is diagonal matrix, noteλ represents the value on diagonalmatrix, its size is equal to the feature of positive definite matrix Q Value；P represents the coefficient matrix of positive definite matrix Q orthogonalizations；NWFor new energy station quantity；T represent one day in when hop count；
(32) ellipsoid is rotated and translated, make its symmetry axis and coordinate overlapping of axles, it is obtained symmetrical after rotation translation The equation of axial higher-dimension ellipsoid is：
E ' (D)={ ω ' ∈ Rn|ω′TD ω '≤1 },
Wherein, ω ' expressions uncertain parameter ω rotations move to the coordinate value under another coordinate；RnRepresent the reality of n dimensions Manifold is closed；[]TRepresent transposition；
The vertex equation of ellipsoid is after rotation translation：
Wherein, e represents the vertex of ellipsoid, i.e. limit scene point；NeRepresent the number on ellipsoid vertex, i.e. limit scene Number；
Inverse transformation is carried out to the vertex equation, obtains the vertex equation of former ellipsoid：
ωe,i=c+P-1ω′e,i,
Wherein, ωe,iRepresent the value of the uncertain parameter under limit scene；C represents the central point of ellipsoid；
(33) the polyhedron equation that the vertex of former ellipsoid surrounds is：
Wherein, piBe expressed as being more than 0 positive number for being less than 1, and itself and for 1.
Further, the step (4) specifically includes：
The amplification factor of setting is selected, the uncertain set finally obtained can be defined as：
Wherein：piBe expressed as being more than 0 positive number for being less than 1, and itself and for 1；RnRepresent the real number set of n dimensions；NeRepresent ellipsoid The number of the number on vertex, i.e. limit scene；In formula, kmaxFor setting Amplification factor；C represents the central point of ellipsoid；P represents the coefficient matrix of positive definite matrix Q orthogonalizations；ωe,iRepresent in limiting field Scape
The value of lower uncertain parameter；ω′e,iRepresent that the value of the corresponding uncertain parameter of limit scene is new after rotation translates Value under coordinate.
In general, by the contemplated above technical scheme of the present invention compared with prior art, there is following technology spy Sign and beneficial effect：
(1) convex closure that set is surrounded by limited a limit scene is not known in the method for the present invention to form, be the more of broad sense Face body, can be extended in existing two benches robust Optimal methods easily；
(2) the method for the present invention can correspondingly change coefficient according to the purpose of decision-maker and be determined to adjust by what set influenced The economy of plan；
(3) number of the method for the present invention limit scene and the number of stochastic variable linearly increase, and are ensureing robust optimization The solving speed of method.
Brief description of the drawings
Fig. 1 is the step flow chart of the method for the present invention；
Fig. 2 is the historic scenery schematic diagram being collected into present example；
Fig. 3 is the ellipsoid schematic diagram that closure ellipsoid algorithm determines in present example；
Fig. 4 is the schematic diagram that rotation transformation is translated in the method for the present invention；
Fig. 5 is original convex closure and revised convex closure schematic diagram in the method for the present invention example.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, it is right The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, not For limiting the present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below that Not forming conflict between this can be mutually combined.
As shown in Figure 1, the present invention comprises the following steps：
Step 1：Relevant historical data is collected, each new energy station history being collected into goes out force data and to each station The processing of output standardization, the matrix P being expressed asWForm：
For the temporal correlation of the new energy station of consideration each scheduling time, the historical data being collected into daily is drawn Point, it is a scene to remember daily processing data, as follows：
For convenience of description, in a particular embodiment, illustrated using two wind power plant list periods, i.e. wind power plant number NW=2, scheduling time T=1, the when hop count T of collected historical data1=1300, Nd=1300, P at this timeW′For 2 × 1300 The matrix of dimension.The matrix the first row (PW) is selected to be used as x-axis, the second row (PV) is used as y-axis, and historical data is made two dimension goes out Power, as shown in Figure 2.
Step 2：It is determined to surround the closure ellipsoid of all historical datas, after the processing for completing historical data, we recognize For in the case where historical data is enough, historic scenery can cover all output situations being likely to occur.Therefore we need A convex closure closed is determined to surround all historical datas, and to ensure the economy of follow-up decision, it is selected convex The volume of bag should be as small as possible.Current algorithm is limited to, we by means of a kind of higher-dimension closure ellipsoid, i.e., solves an energy first Enough surround the closure higher-dimension ellipsoid of all scenes, i.e. solving-optimizing：
s.t.(ωh,1-c)TQ(ωh,1-c)≤1
(ωh,2-c)TQ(ωh,2-c)≤1
…
Wherein, Q ∈ R2×2For positive definite matrix, it represents departure degree of the symmetry axis of higher-dimension ellipsoid to reference axis, c= [c1, c2]TFor the central point of ellipsoid, after completing Optimization Solution, the expression formula for obtaining the ellipsoid is：
Wherein, Q is positive definite matrix, represents departure degree of the symmetry axis to reference axis of the higher-dimension ellipsoid,R represents real number set；For the central point of higher-dimension ellipsoid, []TRepresent to turn Put；ω represents uncertain parameter；NWFor new energy station quantity；T represent one day in when hop count；
According to above-mentioned data, obtained matrix value is：
Ellipsoid expression formula is：
9.5048(ω1-0.5338)2+2×9.3509×(ω1-0.5338)(ω2-0.35928)+17.3008×(ω2- 0.35928)2=1
Obtained ellipsoid is as shown in Figure 3.
Step 3：Determine the broad sense polyhedron of encirclement historic scenery, although we have been completed asking for for ellipsoid equation, But since the mathematic(al) representation of the ellipsoid is the form of 2 times, solving particular problem (such as Economic Dispatch Problem and Unit Combination Problem) when, the mathematical property which may result in model changes, therefore we are it needs to be determined that a broad sense Polyhedron surround historic scenery.Simplest 4 vertex for choosing the ellipsoid (are denoted as limit scene ωe) conduct is true Fixed set, the method for solving the initial uncertain set are as follows：
Decomposition is orthogonalized to positive definite matrix Q first：Q=PTDP=P-1DP, obtained D matrix are diagonal square Battle array, and be positive number on diagonal, noteWherein, λ is represented on diagonalmatrix Value, its size are equal to the characteristic value of positive definite matrix Q；P represents the coefficient matrix of positive definite matrix Q orthogonalizations.To obtain higher-dimension ellipsoid Corresponding vertex, which is rotated and is translated, as shown in figure 4, make its symmetry axis and coordinate overlapping of axles, the rotationally-varying side of the translation Cheng Wei：
ω′i=P × (ωi-c)
Wherein,
The equation of obtained symmetrical axial higher-dimension ellipsoid is after rotation translation：
I.e.：
Wherein, e represents the vertex of ellipsoid, and we term it limit scene point；NeRepresent the number on ellipsoid vertex, i.e., The number of limit scene；
Similarly, using inverse transformation：
ωi=c+P-1ω′i
Wherein, ωe,iRepresent the value of the uncertain parameter under limit scene；C represents the central point of ellipsoid；
The polyhedron equation surrounded by the ellipsoid vertex is：
Wherein, ω represents the value of uncertain parameter；piBe expressed as being more than 0 positive number for being less than 1, and itself and for 1；ω′e,i Represent value of the value of the corresponding uncertain parameter of limit scene under rotational coordinates.
That is, four apex coordinates of ellipsoid are respectively：
Step 4：Do not know to gather according to policymaker's demand correction, complete asking for afterwards, it is necessary to pay attention to for the uncertain convex closure Arrive：The convex closure only represents the vertex of ellipsoid, can not necessarily cover all historic sceneries, therefore proposed algorithm For：Scaling coefficient is introduced, appropriate zooms in or out former convex closure, can adapt to policymaker to decision-making robustness and economy The balance of property.Specific practice is as follows：
First, historic scenery need to rotate translation with original limit scene, and former limit scene is respectively positioned in reference axis at this time, By the correlation theory of convex optimization, if historic scenery, inside the convex closure being made of limit scene, it meets following formula：
The convex closure is to surround the point outside convex closure, it is necessary to introduce corresponding amplifying parameters, it will be assumed that the amplification Multiple is linear magnification, then should meet following formula：
Wherein kiFor the positive number more than 1, above formula can be equivalent to：
βj,i=kiαj,i≥0
Wherein, βJ, iFor more than 0, less than kiCoefficient, and itself and be ki, as can be seen from the above equation, kiRepresent history field The distance between scape and convex closure, therefore, can construct following optimization problem to judge a little position relationship with convex closure：
minki
It should be noted that for each historic scenery, it is required to make above-mentioned judgement, in practice for each history Scene, kiIt is independent, therefore optimization can be polymerized to following formula：
So far, we can select suitable amplification factor by actual conditions, and the uncertain set finally obtained can be with It is defined as：
Wherein,kmaxFor the amplification factor finally selected.
In this example, we select：Convex closure needs the historic scenery including at least 99.5%, needs at this time to kiDo and drop Sequence arranges, when we select the k of maximumiWhen, you can it is comprised in the historic scenery for ensureing all in convex closure, and to protect The point of card be up to 0.5% is not included, then can take in descending sequence preceding 0.5% value, and shares 1300 in this example A historic scenery, that is, take 6 values of the 1300th × 0.5% ≈ of descending sequence, at this time ki=1.1362, obtained after convex closure is amplified Convex closure apex coordinate be：
The schematic diagram of former convex closure and revised convex closure is as shown in Figure 5.
Above content as it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, It is not intended to limit the invention, all any modification, equivalent and improvement made within the spirit and principles of the invention etc., It should all be included in the protection scope of the present invention.

Claims (5)

1. a kind of new energy based on temporal correlation does not know set modeling method, it is characterised in that the method is specifically wrapped Include：
(1) historical data contributed all new energy stations is collected, daily historical data is a historic scenery；
(2) a minimum closure higher-dimension ellipsoid that can surround all historic sceneries is solved；
(3) multiple vertex of the ellipsoid are chosen as uncertain set, ask for the polyhedral table of broad sense that the vertex surrounds The uncertain set is described up to formula；
(4) amplification factor amendment is introduced not knowing to gather.
2. a kind of new energy according to claim 1 does not know set modeling method, it is characterised in that the step (1) Specifically include：
(11) historical data that each new energy station for being collected into is contributed and to power generating value standardization, by historical data matrix PWRepresent：
Wherein, NWFor new energy station quantity；T1For the when hop count of collected historical data；
(12) historical data being collected into daily is divided, it is a historic scenery P to remember daily historical dataW′：
<mrow> <msup> <mi>P</mi> <mi>W</mi> </msup> <mo>=</mo> <msub> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>k</mi> <mi>T</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>d</mi> </msub> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>T</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>W</mi> </msubsup> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>k</mi> <mi>T</mi> <mo>+</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <mn>1</mn> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>d</mi> </msub> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>T</mi> <mo>+</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msubsup> <mi>P</mi> <mrow> <msub> <mi>N</mi> <mi>w</mi> </msub> <mo>,</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <msub> <mi>N</mi> <mi>w</mi> </msub> <mo>,</mo> <mi>k</mi> <mi>T</mi> <mo>+</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>P</mi> <mrow> <msub> <mi>N</mi> <mi>w</mi> </msub> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>d</mi> </msub> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>T</mi> <mo>+</mo> <mi>T</mi> </mrow> <mi>W</mi> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mrow> <msub> <mi>N</mi> <mi>w</mi> </msub> <mi>T</mi> <mo>&amp;times;</mo> <msub> <mi>N</mi> <mi>d</mi> </msub> </mrow> </msub> <mo>,</mo> </mrow>
Wherein, NdRepresent history number of days；T represent one day in when hop count, T1=NdT；
(13) above-mentioned matrix is written as to the form of matrix in block form,Wherein, uncertain parameter
3. a kind of new energy according to claim 1 does not know set modeling method, it is characterised in that the step (2) The equation of middle minimum closure higher-dimension ellipsoid is：
<mrow> <mi>E</mi> <mrow> <mo>(</mo> <mi>Q</mi> <mo>,</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>{</mo> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <msup> <mi>R</mi> <mrow> <msub> <mi>N</mi> <mi>w</mi> </msub> <mi>T</mi> </mrow> </msup> <mo>|</mo> <msup> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>-</mo> <mi>c</mi> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>Q</mi> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>-</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mn>1</mn> <mo>}</mo> <mo>,</mo> </mrow>
Wherein, Q is positive definite matrix, represents departure degree of the symmetry axis to reference axis of the higher-dimension ellipsoid,R represents real number set；For the central point of higher-dimension ellipsoid, []TRepresent to turn Put；ω represents the value of uncertain parameter；NWFor new energy station quantity；T represent one day in when hop count.
4. a kind of new energy according to claim 1 does not know set modeling method, it is characterised in that the step (3) Specifically include：
(31) decomposition, Q=P are orthogonalized to positive definite matrix QTDP=P-1DP, wherein, D matrix is diagonal matrix, noteλ represents the value on diagonalmatrix, its size is equal to the feature of positive definite matrix Q Value；P represents the coefficient matrix of positive definite matrix Q orthogonalizations；NWFor new energy station quantity；T represent one day in when hop count；
(32) ellipsoid is rotated and translated, make its symmetry axis and coordinate overlapping of axles, it is obtained symmetrical axial after rotation translation The equation of higher-dimension ellipsoid is：
E ' (D)={ ω ' ∈ Rn|ω′TD ω '≤1 },
Wherein, ω ' expressions uncertain parameter ω rotations move to the coordinate value under another coordinate；RnRepresent the set of real numbers of n dimensions Close；[]TRepresent transposition；
The vertex equation of ellipsoid is after rotation translation：
<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>e</mi> <mo>,</mo> <mn>1</mn> </mrow> <mo>&amp;prime;</mo> </msubsup> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>e</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>e</mi> </msub> </mrow> <mo>&amp;prime;</mo> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mo>&amp;PlusMinus;</mo> <mi>d</mi> <mi>i</mi> <mi>a</mi> <mi>g</mi> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <msqrt> <msub> <mi>&amp;lambda;</mi> <mn>1</mn> </msub> </msqrt> </mfrac> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mfrac> <mn>1</mn> <msqrt> <msub> <mi>&amp;lambda;</mi> <mi>n</mi> </msub> </msqrt> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
Wherein, e represents the vertex of ellipsoid, i.e. limit scene point；NeRepresent of the number on ellipsoid vertex, i.e. limit scene Number；
Inverse transformation is carried out to the vertex equation, obtains the vertex equation of former ellipsoid：
ωe,i=c+P-1ω′e,i,
Wherein, ωe,iRepresent the value of the uncertain parameter under limit scene；C represents the central point of ellipsoid；
(33) the polyhedron equation that the vertex of former ellipsoid surrounds is：
<mrow> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>{</mo> <mrow> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <msup> <mi>R</mi> <mi>n</mi> </msup> <mfenced open = "|" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>&amp;omega;</mi> <mo>=</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>e</mi> </msub> </msubsup> <msub> <mi>p</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>c</mi> <mo>+</mo> <msup> <mi>P</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>e</mi> <mo>,</mo> <mi>i</mi> </mrow> <mo>&amp;prime;</mo> </msubsup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>e</mi> </msub> </msubsup> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> <mo>}</mo> </mrow> <mo>,</mo> </mrow>
Wherein, piBe expressed as being more than 0 positive number for being less than 1, and itself and for 1.
5. a kind of new energy according to claim 1 does not know set modeling method, it is characterised in that the step (4) Specifically include：
The amplification factor of setting is selected, the uncertain set finally obtained can be defined as：
<mrow> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <msub> <mi>W</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>{</mo> <mrow> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <msup> <mi>R</mi> <mi>n</mi> </msup> <mfenced open = "|" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>&amp;omega;</mi> <mo>=</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>e</mi> </msub> </msubsup> <msub> <mi>p</mi> <mi>i</mi> </msub> <msub> <mover> <mi>&amp;omega;</mi> <mo>~</mo> </mover> <mrow> <mi>e</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>e</mi> </msub> </msubsup> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> <mo>}</mo> </mrow> <mo>,</mo> </mrow>
Wherein：piBe expressed as being more than 0 positive number for being less than 1, and itself and for 1；RnRepresent the real number set of n dimensions；NeRepresent ellipsoid vertex Number, i.e. limit scene number；In formula, kmaxFor putting for setting Big multiple；C represents the central point of ellipsoid；P represents the coefficient matrix of positive definite matrix Q orthogonalizations；ωe,iRepresent under limit scene The value of uncertain parameter；ω′e,iRepresent value of the value of the corresponding uncertain parameter of limit scene under new coordinate after rotation translates.
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