CN111144544B - Flexible power flow control equipment selection evaluation method and system - Google Patents

Flexible power flow control equipment selection evaluation method and system Download PDF

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CN111144544B
CN111144544B CN202010263157.9A CN202010263157A CN111144544B CN 111144544 B CN111144544 B CN 111144544B CN 202010263157 A CN202010263157 A CN 202010263157A CN 111144544 B CN111144544 B CN 111144544B
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weight
flow control
power flow
index
flexible power
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CN111144544A (en
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李群
张宁宇
孙国强
李程
刘建坤
李文平
赵静波
刘力强
李鹏
臧海祥
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Hohai University HHU
NR Electric Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

The invention discloses an evaluation method for flexible power flow control equipment selection, which comprises the steps of determining evaluation indexes of various alternative flexible power flow control equipment; solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight; according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device; and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes. A corresponding system is also disclosed. The method determines evaluation indexes based on the requirements of power grid construction development and safe and stable operation, adopts an improved tree growth algorithm to solve the weight of each evaluation index, and scientifically and objectively obtains the selected flexible power flow control equipment based on the weighted sum of the evaluation indexes.

Description

Flexible power flow control equipment selection evaluation method and system
Technical Field
The invention relates to an evaluation method and system for flexible power flow control equipment selection, and belongs to the technical field of flexible alternating current transmission.
Background
The flexible ac transmission system concept was originally proposed by american scholars n.g. higorani, often referred to as FACTS in english acronym. The technology is widely applied to the power system at present, and makes important contributions to the stability, the power flow control and the power sparse optimization of the power system. Common FACTS equipment at present is UPFC, TCPST, STATCOM, SSSC and the like. The devices have different characteristics and different technical levels, and how to scientifically and objectively select proper FACTS devices according to the actual requirements of the system becomes a problem to be solved urgently in modern power systems.
Disclosure of Invention
The invention provides an evaluation method and system for flexible power flow control equipment selection, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for evaluating selection of a flexible power flow control device comprises the following steps,
determining evaluation indexes of all the alternative flexible power flow control devices;
solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight;
according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device;
and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes.
The evaluation indexes comprise a firmness index, an economic index, a cleanliness index and a full-period cost index; the robustness index comprises the passing rate of a power grid N-1 and the qualification rate of a power supply voltage, the economic index comprises a capacity-load ratio, a comprehensive line loss rate and average power failure time, the cleanliness index comprises the utilization rate of renewable energy and the electric quantity ratio of new energy, and the cost index of a full period comprises initial investment cost, operation cost, maintenance cost and maintenance cost.
The weight model of the evaluation index is,
Figure 579766DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 208193DEST_PATH_IMAGE002
to evaluate the objective function of the metric weight model,
Figure 57332DEST_PATH_IMAGE003
in order to balance the factors, the method comprises the following steps of,
Figure 745802DEST_PATH_IMAGE004
is a variable ofjTerm weight
Figure 945970DEST_PATH_IMAGE005
The probability corresponding to the prior weight of (c),Ris a correlation coefficient array of the index data array,pis the probability corresponding to the prior weight to be solved,wfor the weight to be sought, the weight is,lis the number of the prior weights,
Figure 112509DEST_PATH_IMAGE006
are respectively aspAndwthe transposing of (1).
The improved tree growth algorithm is based on the traditional tree growth algorithm, and improves the iteration process of the optimal group of trees and the movement formula of the group of trees; in the improved tree growing algorithm, one group is
Figure 81734DEST_PATH_IMAGE007
A group ofw j And regarding the objective function optimization solution of the evaluation index weight model as a tree in the forest, and regarding the objective function of the evaluation index weight model as the fitness of the tree.
The improved iterative process is that,
traversing the optimal group, and synthesizing one tree with other two different trees randomly selected to obtain a new generation of the tree;
randomly selecting part of new generation trees to replace corresponding old generation trees to obtain a new optimal group;
if the new optimal group is more optimal than the old optimal group, the old optimal group is replaced with the new optimal group.
The tree synthesis formula is as follows,
Figure 941105DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 549941DEST_PATH_IMAGE009
is the first in the optimal groupcGeneration by generationiA plurality of trees are arranged in the container,
Figure 598800DEST_PATH_IMAGE010
for the other two different trees that were randomly selected,Fin order to be a scaling factor, the scaling factor,
Figure 609481DEST_PATH_IMAGE011
is composed of
Figure 408064DEST_PATH_IMAGE012
To a corresponding secondc+1 generation of trees.
The improved formula for the movement is that,
Figure 379563DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 887904DEST_PATH_IMAGE014
as the parameter(s) is (are),R 0-1is a random number between 0 and 1, is subject to uniform distribution,
Figure 894038DEST_PATH_IMAGE015
for trees in the competing group to be moved,
Figure 845944DEST_PATH_IMAGE016
the two trees closest to the moved tree,
Figure 491689DEST_PATH_IMAGE017
for the trees in the moved debate group,
Figure 554454DEST_PATH_IMAGE018
is the increment of the vector of the transition.
An evaluation system for flexible power flow control equipment selection comprises,
an index determination module: determining evaluation indexes of all the alternative flexible power flow control devices;
a weight calculation module: solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight;
a weighted sum module: according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device;
a selection module: and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform an evaluation method selected by a flexible power flow control device.
A computing device comprising one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing an evaluation method selected by a flexible power flow control device.
The invention achieves the following beneficial effects: the method determines evaluation indexes based on the requirements of power grid construction development and safe and stable operation, adopts an improved tree growth algorithm to solve the weight of each evaluation index, and scientifically and objectively obtains the selected flexible power flow control equipment based on the weighted sum of the evaluation indexes.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of an evaluation index system;
fig. 3 is a flow chart of an improved tree growing algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a method for evaluating selection of a flexible power flow control device includes the following steps:
step 1, determining evaluation indexes of various alternative flexible power flow control devices.
Based on the requirements of power grid construction development and safe and stable operation, the evaluation indexes of the alternative flexible power flow control devices are determined by considering the control performance, economic benefit, installation and operation and maintenance cost and other information of different flexible power flow control devices aiming at the specific power grid.
The evaluation index system framework is shown in fig. 2 and includes a robustness index, an economic index, a cleanliness index, and a full cycle cost index.
The firmness index comprises the passing rate of the power grid N-1 and the qualification rate of the power supply voltage; this represents a safety and stability requirement for the power grid. The economic indexes comprise a capacity-load ratio, a comprehensive line loss rate and an average power failure time; the flexible power flow control equipment has the capabilities of improving the utilization rate of the existing equipment, reducing the active loss of a line and reducing the power failure time. The cleanliness indexes comprise the utilization rate of renewable energy and the electric quantity ratio of new energy; the flexible power flow control equipment controls the inclination of the new energy power supply, and has the capability of improving the cleanliness index. The full-cycle cost index includes initial investment cost, operating cost, maintenance cost and repair cost.
And 2, solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight.
Suppose to comprisenAlternative flexible power flow control devices (hereinafter simply referred to as "devices"), each havingmAn evaluation index of
Figure 602045DEST_PATH_IMAGE019
Is shown asiThe first of the devicejAn evaluation index, wherein,
Figure 787170DEST_PATH_IMAGE020
，
Figure 857894DEST_PATH_IMAGE021
。
the evaluation indexes are normalized, the negative indexes are all negative and have the same trend aiming at the different trends of the positive indexes and the negative indexes, and the processing formula is as follows:
Figure 344589DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 246686DEST_PATH_IMAGE023
，
Figure 540395DEST_PATH_IMAGE024
is composed of
Figure 895153DEST_PATH_IMAGE025
After normalization processingThe data after normalization processing is performed to obtain a data matrix
Figure 565300DEST_PATH_IMAGE026
Easy to know math expectation
Figure 321903DEST_PATH_IMAGE027
And variance
Figure 786514DEST_PATH_IMAGE028
，
Figure 894147DEST_PATH_IMAGE029
Is a data matrix ofjThe number of columns represents the total data of a certain index in different equipment.
For objectively determining the weight, extracting the comprehensive variables of the multivariable systemGThe following were used:
Figure 102406DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 713516DEST_PATH_IMAGE031
is as followsjThe weight of the term index is given to,
Figure 614607DEST_PATH_IMAGE032
，
Figure 881640DEST_PATH_IMAGE033
is a vectorwAnd (4) transposition.
To make itGCarry as much information as possible, so letGAs scattered as possible, i.e. the variance is maximal, as follows:
Figure 877278DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 359206DEST_PATH_IMAGE035
is composed ofBThe covariance matrix of (a), since the data is normalized,the covariance matrix is therefore equal toBThe correlation coefficient matrix of (a) is,
Figure 680466DEST_PATH_IMAGE036
is thatBTransposing of the matrix.
The maximum entropy principle considers that in all solutions of the ill-posed problem, one solution which meets the constraint condition but has the maximum entropy value is selected, which is the only unbiased selection that we can make, and any other answer means that we add artificial information. While the goal of generalized maximum entropy is to simultaneously maximize shannon entropy for all random variables in the system. If the weights of the evaluation indexes are regarded as different random variables, then the simultaneous maximization is requiredmThe Shannon entropy of each weight is based on the probability of possible values of random variables, so that the Shannon entropy is weighted
Figure 976406DEST_PATH_IMAGE037
Wherein the content of the first and second substances,
Figure 978997DEST_PATH_IMAGE038
is a variable of
Figure 564699DEST_PATH_IMAGE039
Is a priori weighted value
Figure 604330DEST_PATH_IMAGE040
The probability of the correspondence is such that,lis the number of the prior weights,
Figure 845956DEST_PATH_IMAGE041
and satisfy
Figure 199708DEST_PATH_IMAGE042
According to the principle of generalized maximum entropy, simultaneously maximizingmShannon entropy of individual weight variablesHThat is to say that,
Figure 639917DEST_PATH_IMAGE043
wherein the content of the first and second substances,pis the probability corresponding to the prior weight to be solved,
Figure 53712DEST_PATH_IMAGE044
is a transpose thereof.
Aiming at the dual-target planning problem, an evaluation index weight model is constructed by a weighting method,
Figure 517054DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 923764DEST_PATH_IMAGE046
to evaluate the objective function of the metric weight model,
Figure 234791DEST_PATH_IMAGE047
for balancing the factors, are preset according to actual requirements,Ris a correlation coefficient array of the index data array,wis the weight to be found.
The tree growth algorithm is a common optimization algorithm, and a certain number of tree populations which are initially randomly generated are sorted from high to low according to the fitness of a solution, and are divided into four groups with different functions, wherein the four groups are respectively defined as an optimal groupN 1Dispute light groupN 2Group of deletionsN 3And breeding groupN 4. The following problems exist in the conventional algorithm: 1.N 1in the iterative process, the trees do not communicate with each other, and each tree is independently searched in an isolated way, so that the blindness is realized; 2.N 2the moving direction and distance of the tree can not be ensured to approach in the solving process.
Therefore, the traditional tree growing algorithm is improved, and specifically, the tree growing algorithm comprises the following steps: the improved tree growth algorithm process is shown in fig. 3, and is the same as the traditional algorithm, and only on the basis of the traditional tree growth algorithm, the iterative process of the optimal group of trees and the moving formula of the group of trees are improved; in improved tree growing algorithmOne group of
Figure 740859DEST_PATH_IMAGE048
A group ofw j And regarding the objective function optimization solution of the evaluation index weight model as a tree in the forest, and regarding the objective function of the evaluation index weight model as the fitness of the tree.
The first group with the best fitness is the optimal group, which represents the group of trees with the highest growth, and can be more easily exposed to sunlight, and the performance of striving for more nutrients is used for local search nearby.
The improved iterative process is as follows:
s1) traversing the optimal group, and synthesizing one tree with other two different trees randomly selected to obtain a new generation of the tree; therefore, new trees can be generated more reasonably, and the frequent out-of-limit of pure random search is avoided;
the tree synthesis formula is as follows,
Figure 19394DEST_PATH_IMAGE049
wherein the content of the first and second substances,
Figure 714948DEST_PATH_IMAGE050
is the first in the optimal groupcGeneration by generationiA plurality of trees are arranged in the container,
Figure 67432DEST_PATH_IMAGE051
for the other two different trees that were randomly selected,Fin order to be a scaling factor, the scaling factor,
Figure 806718DEST_PATH_IMAGE052
is composed of
Figure 57702DEST_PATH_IMAGE053
To a corresponding secondc+1 generation of trees.
S2) randomly selecting part of new generation trees to replace the corresponding old generation trees to obtain a new optimal group.
S3) if the new optimal group is better than the old optimal group, replacing the old optimal group with the new optimal group.
The second group is a dispute group ordered from high to low, for the secondcOf a minor iterationN 2Trees in the population
Figure 806215DEST_PATH_IMAGE054
First of all fromN 1AndN 2two trees closest to the user (not including the tree itself) are found, and vectors of the two close trees (i.e., the two trees closest to the moved tree) are set as
Figure 13205DEST_PATH_IMAGE055
；
Improved motion formula:
Figure 939704DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 927252DEST_PATH_IMAGE057
as the parameter(s) is (are),R 0-1is a random number between 0 and 1, is subject to uniform distribution,
Figure 151560DEST_PATH_IMAGE058
is the increment of the vector of the transition,
Figure 285826DEST_PATH_IMAGE059
for trees in a moved group, i.e. movedc+1 generation of trees. Based on knowledge of similar triangles, new formulae are generated
Figure 632494DEST_PATH_IMAGE060
Is fixed at
Figure 592490DEST_PATH_IMAGE061
On a line of (2), which ensures along the vector
Figure 620489DEST_PATH_IMAGE062
Moving in a direction
Figure 864389DEST_PATH_IMAGE063
And can not cross and reliably approach the two selected trees.
The third group is a deleted group, which represents the rejected weak small trees. In the algorithm, the trees of the group are deleted and new trees are randomly generated in equal numbers.
The fourth group is a breeding group, and the group representsN 1New populations derived from the optimal group. Newly generated trees are generated near the parent tree and inherit part of the position factors. Part of the parameters inherit the parameters of the mother tree, and the rest parameters randomly generate new parameters.
When the calculation of four groups in each iteration is completed, the fitness of the solution needs to be evaluated again, the groups need to be sorted, and then a new iteration calculation is carried out.
We can then get a globally optimal solution that satisfies the constraints, i.e., make
Figure 132690DEST_PATH_IMAGE064
Minimum set of prior probabilities
Figure 94830DEST_PATH_IMAGE065
. This group
Figure 473990DEST_PATH_IMAGE066
The method can meet the maximum entropy principle, so that the weight led out from the method has objectivity without doping subjective factors.
And 3, solving the weighted sum of the evaluation indexes of the alternative flexible power flow control equipment according to the weight of each evaluation index.
And 4, obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes, namely, the flexible power flow control equipment with the largest weighted sum is selected finally.
The method comprises the steps of determining evaluation indexes based on the requirements of power grid construction development and safe and stable operation, solving the weight of each evaluation index by adopting an improved tree growth algorithm, and scientifically and objectively obtaining the selected flexible power flow control equipment based on the weighted sum of the evaluation indexes.
An evaluation system for flexible power flow control equipment selection comprises,
an index determination module: determining evaluation indexes of all the alternative flexible power flow control devices;
a weight calculation module: solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight;
a weighted sum module: according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device;
a selection module: and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform an evaluation method selected by a flexible power flow control device.
A computing device comprising one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing an evaluation method selected by a flexible power flow control device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (9)

1. A method for evaluating selection of a flexible power flow control device is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
determining evaluation indexes of all the alternative flexible power flow control devices;
solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight;
the weight model of the evaluation index is,
Figure FDA0002497843910000011
Figure FDA0002497843910000012
wherein f (p) is an objective function of the evaluation index weight model, η is a balance factor, pjkAs the jth weight w of the variablejThe probability corresponding to the prior weight, R is a correlation coefficient array of the index data array, p is the probability corresponding to the prior weight to be solved, w is the weight to be solved, l is the number of the prior weights, and p 'and w' are the transposes of p and w respectively;
according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device;
and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes.
2. The method for evaluating selection of a flexible power flow control device according to claim 1, wherein: the evaluation indexes comprise a firmness index, an economic index, a cleanliness index and a full-period cost index; the robustness index comprises the passing rate of a power grid N-1 and the qualification rate of a power supply voltage, the economic index comprises a capacity-load ratio, a comprehensive line loss rate and average power failure time, the cleanliness index comprises the utilization rate of renewable energy and the electric quantity ratio of new energy, and the cost index of a full period comprises initial investment cost, operation cost, maintenance cost and maintenance cost.
3. The method for evaluating selection of a flexible power flow control device according to claim 1, wherein: the improved tree growth algorithm is based on the traditional tree growth algorithm, and improves the iteration process of the optimal group of trees and the movement formula of the group of trees; in the improved tree growing algorithm, a group of pjkA group wjAnd considering the objective function optimization solution of the evaluation index weight model as a tree in the forest, and considering the objective function of the evaluation index weight model as the treeThe fitness of (2).
4. The method for evaluating selection of a flexible power flow control device according to claim 3, wherein: the improved iterative process is that,
traversing the optimal group, and synthesizing one tree with other two different trees randomly selected to obtain a new generation of the tree;
randomly selecting part of new generation trees to replace corresponding old generation trees to obtain a new optimal group;
if the new optimal group is more optimal than the old optimal group, the old optimal group is replaced with the new optimal group.
5. The method for evaluating selection of a flexible power flow control device according to claim 4, wherein: the tree synthesis formula is as follows,
Figure FDA0002497843910000021
wherein the content of the first and second substances,
Figure FDA0002497843910000022
for the ith tree of the c-th generation in the optimal group,
Figure FDA0002497843910000023
for two other different trees, chosen at random, F is the scaling factor,
Figure FDA0002497843910000024
is composed of
Figure FDA0002497843910000025
The corresponding c +1 th generation of trees.
6. The method for evaluating selection of a flexible power flow control device according to claim 3, wherein: the improved formula for the movement is that,
Figure FDA0002497843910000026
Figure FDA0002497843910000027
wherein λ is a parameter, R0-1Is a random number between 0 and 1, is subject to uniform distribution,
Figure FDA0002497843910000028
for trees in the dispute group to be moved, x1、x2The two trees closest to the moved tree,
Figure FDA0002497843910000029
for the trees in the moved dispute group, y is the increment of the vector of the transition.
7. An evaluation system for flexible power flow control device selection, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
an index determination module: determining evaluation indexes of all the alternative flexible power flow control devices;
a weight calculation module: solving a preset evaluation index weight model by adopting an improved tree growth algorithm to obtain each evaluation index weight;
the weight model of the evaluation index is,
Figure FDA00024978439100000210
Figure FDA00024978439100000211
wherein f (p) is an objective function of the evaluation index weight model, η is a balance factor, pjkAs the jth weight w of the variablejR is a correlation coefficient array of the index data array, p is a probability corresponding to the prior weight to be solved, and w is the weight to be solvedThe weight, l is the number of the prior weight, and p 'and w' are the transposes of p and w respectively;
a weighted sum module: according to the weight of each evaluation index, solving the weighted sum of the evaluation indexes of each alternative flexible power flow control device;
a selection module: and obtaining the selected flexible power flow control equipment according to the weighted sum of the evaluation indexes.
8. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-6.
9. A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-6.
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