CN115000969A - Hybrid power flow controller planning configuration method considering wind power integration - Google Patents

Hybrid power flow controller planning configuration method considering wind power integration Download PDF

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CN115000969A
CN115000969A CN202210873554.7A CN202210873554A CN115000969A CN 115000969 A CN115000969 A CN 115000969A CN 202210873554 A CN202210873554 A CN 202210873554A CN 115000969 A CN115000969 A CN 115000969A
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袁博
吴熙
张章
关辰皓
杨鹏
王涛
王颖
史善哲
李倩
王瑞
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State Grid Corp of China SGCC
Southeast University
State Grid Hebei Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
Southeast University
State Grid Hebei Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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Abstract

The invention discloses a planning configuration method of a hybrid power flow controller considering wind power integration, and belongs to the field of power system stabilization and control. A planning and configuration method of a hybrid power flow controller considering wind power integration comprises the following steps: s1: acquiring wind power-load data, and dividing a wind output-load probability scene by using a K-means clustering algorithm; s2: establishing an HPFC planning configuration optimization model considering multiple scenes; s3: calculating target functions corresponding to different scenes; s4: weighting according to the occurrence probability of different scenes to obtain a total objective function, and recording an individual optimal objective function, a group optimal objective function, and HPFC (high performance filter) addresses and capacities corresponding to the individual optimal objective function and the group optimal objective function; s5: if the iteration number does not reach the set value, turning to S6, otherwise, turning to S7; s6: updating the parameters and the speed of the control variable, and turning to S3; s7: and outputting the optimal objective function and the HPFC planning configuration result.

Description

Hybrid power flow controller planning configuration method considering wind power integration
Technical Field
The invention relates to the field of power system stabilization and control, in particular to a hybrid power flow controller planning configuration method considering wind power integration.
Background
In order to achieve the goal of "carbon peak reaching and carbon neutralization", a new power system mainly based on new energy needs to be constructed. Currently, part of new energy resources such as wind power generation, photovoltaic and the like are applied in China. However, since the wind speed is easily affected by the environment, there is a great uncertainty and a great time sequence in the wind power output. Therefore, large-scale wind power integration will greatly affect the safety and stability of the power grid.
The Hybrid Power Flow Controller (HPFC) has the same Power Flow regulation function as the UPFC, and has the advantages of lower cost, less electromagnetic interference and switching loss, and strong practical application value. The safety and the stability of the power grid can be improved by optimizing the power flow distribution by accessing the HPFC in the wind power grid-connected system. Due to the high investment cost of HPFC equipment, a large amount of capital investment is wasted if the planning is not configured properly, and system stability and power transmission capacity may even be reduced. Therefore, the research on the planning configuration of the HPFC has important significance for effectively utilizing the capacity of the HPFC equipment, mining the application potential of the HPFC equipment and fully exerting the power flow control function of the HPFC equipment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a hybrid power flow controller planning configuration method considering wind power integration.
The purpose of the invention can be realized by the following technical scheme:
a planning and configuration method of a hybrid power flow controller considering wind power integration comprises the following steps:
step S1: acquiring wind power-load data, and dividing a wind output-load probability scene by using a K-means clustering algorithm;
step S2: establishing an HPFC planning configuration optimization model considering multiple scenes;
step S3: calculating target functions corresponding to different scenes;
step S4: weighting according to the occurrence probability of different scenes to obtain a total objective function, and recording an individual optimal objective function, a group optimal objective function, and HPFC (high performance filter) addresses and capacities corresponding to the individual optimal objective function and the group optimal objective function;
step S5: if the iteration number does not reach the set value, turning to the step S6, otherwise, turning to the step S7;
step S6: updating the parameters and the speed of the control variables of each objective function, and turning to the step S3;
step S7: and outputting the optimal objective function and the HPFC planning configuration result.
Optionally, the dividing the wind power-load probability scene by using a K-means clustering algorithm includes the following steps:
1) acquiring wind power-load sample data, and establishing a sample data matrix X.
Figure BDA0003758130580000021
Wherein X ═ { X ═ X 1 ,x 2 ,…,x n }；x j ＝{x j1 ,x j2 ,…,x jm }; m is the number of wind power-load nodes; n is the number of samples.
2) Randomly selecting K samples as initial clustering centers u i 。u i ＝{u i1 ,u i2 ,…,u im },i＝1,2,…,K。
3) The clustering quality depends on the similarity of data sequences in the same subset and the difference of data among different subsets. In order to characterize the similarity and difference between data sequences, Euclidean distance is adopted to characterize the distance between a sample and a cluster center, and the calculation formula is as follows:
Figure BDA0003758130580000022
the euclidean distance of each sample to each cluster center is compared and summed to a subset with the nearest cluster center.
4) The cluster center is recalculated according to the following formula:
Figure BDA0003758130580000031
wherein x is j Is a subset S i A set of m-dimensional sample sequences of, N i To divide into subsets S i The number of medium m-dimensional samples.
The cluster center of a subset is the newly generated data sequence after averaging the data within the subset, but not necessarily the data sequence actually contained in data object X.
5) And using a standard measure function value E as an iteration convergence criterion, wherein the calculation formula is as follows:
Figure BDA0003758130580000032
6) selection D CH(+) The index is taken as an index of effectiveness of clustering, D CH(+) The definition of the index is:
Figure BDA0003758130580000033
wherein, T K The method is the sum of squares of the inter-class separation difference and mainly reflects the inter-class separation; p is K The method is an intra-class dispersion square sum and mainly reflects intra-class compactness; k is the number of clusters.
By calculating D for each value of K CH(+) Indexes are obtained, and comparison is carried out to find out the optimal clustering number.
7) Probability of occurrence of each scene λ p The following formula was used for the calculation.
Figure BDA0003758130580000034
Wherein, N p Is the number of samples contained in each scene.
Optionally, the planning configuration optimization model of the HPFC includes:
objective function and constraint
The optimization calculation of the HPFC deployment planning is carried out from two aspects of reducing investment cost and solving the problem of system load flow out-of-limit, the objective function of the optimization model comprises HPFC configuration cost, system network loss, system static safety margin and N-1 thermal stability margin,
the objective function comprises:
HPFC configuration costs;
system network loss;
a system static safety margin;
system N-1 thermal stability margin;
the constraint conditions comprise: power system operating constraints and HPFC self constraints.
Optionally, the HPFC configuration cost:
cost H ＝C UPFC ×S UPFC +C ST ×S ST (7)
Figure BDA0003758130580000041
wherein, C UPFC And C ST Capacity price factor for UPFC and ST; a is 1 ～a 6 Is a constant in the price coefficient function; s UPFC And S ST Capacity of UPFC and ST;
taking the maximum capacity used in all operational scenarios as the final capacity:
Figure BDA0003758130580000042
wherein the content of the first and second substances,
Figure BDA0003758130580000043
the current phasor is the injection voltage phasor and the current phasor of the UPFC series side converter under the scene k;
Figure BDA0003758130580000044
the current phasor is the injection voltage phasor and the current phasor flowing through the UPFC parallel side converter under the scene k;
Figure BDA0003758130580000045
the current phasor is the injection voltage phasor and the current phasor of the ST series side converter under a scene k;
optionally, the network loss of the system is mainly the active loss caused by the power flow passing through the line, that is:
Figure BDA0003758130580000046
wherein Nl is the total number of branches of the system, P mij And P mji The active power flows from the head end of the line to the tail end of the line and from the tail end of the line to the head end of the line respectively for the mth branch.
The following can be obtained by simplification:
Figure BDA0003758130580000051
wherein G is m Denotes the conductance on the mth branch, V mi 、θ mi 、V mj 、θ mj The voltage amplitude and the phase angle of the head end node and the tail end node of the mth branch are respectively.
Optionally, defining the ratio of the line through-flow power to the line power flow limit as the line load rate M m Therefore, the running state of the line is reflected.
Figure BDA0003758130580000052
Wherein S is m Is the apparent power, S, flowing through branch m m0 Is the apparent power limit value of branch m.
The static safety margin of the system under the normal condition can be obtained through the line load rate:
Figure BDA0003758130580000053
optionally, the system N-1 thermal stability index when the N-1 fault occurs is:
Figure BDA0003758130580000054
wherein, N c Is an expected failure set; n is c The total number of predicted fault lines;
Figure BDA0003758130580000055
representing the load rate of branch m after the fault.
When the line is overloaded in the calculation process, a penalty value needs to be added to the line safety index.
Optionally, for the power system, the node power balance needs to be satisfied under normal operating conditions, namely:
Figure BDA0003758130580000056
wherein Nb is the total number of nodes of the system; p gi 、Q gi The active power and the reactive power of the generator are respectively; p di 、Q di The active and reactive loads of the load nodes are obtained; g ij 、B ij Elements in the node admittance matrix;
in addition, each variable needs to satisfy a corresponding inequality constraint. For the control variable u, there are:
Figure BDA0003758130580000061
wherein, P g For active power output of the generator, P gmax And P gmin The active limit value for keeping stable operation of each generator is specifically taken as a relevant parameter of the generator; v g Is the PV node voltage magnitude.
For the state variable x, there are:
Figure BDA0003758130580000062
wherein S is l Is the apparent power, S, of the line l lmax A power limit to maintain stable operation for the line; q g For reactive power output of the generator, Q gmax And Q gmin Specifically taking values of the upper limit and the lower limit of the reactive power output of each generator as relevant parameters of the generators; v b Being nodes other than PV nodesThe magnitude of the voltage.
Alternatively, for the capacity and series side output voltage magnitude of the HPFC there are:
Figure BDA0003758130580000063
wherein S is UPFCmax And S STmax Maximum capacity for set UPFC and ST; v semax And V crmax The corresponding UPFC and ST series side voltage magnitudes at maximum capacity.
The following condition needs to be satisfied for the power flow control target of the HPFC:
Figure BDA0003758130580000064
wherein n represents a line on which the HPFC is installed, P nmax 、P nmin 、Q nmax 、Q nmin The upper limit and the lower limit of the active power flow and the reactive power flow of the line n are respectively.
A computer readable storage medium storing instructions which, when executed, can implement the hybrid power flow controller planning and configuring method.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow diagram illustrating a method according to an embodiment of the present invention;
FIG. 2 is an equivalent circuit diagram of an HPFC according to an embodiment of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, in an embodiment of the present invention, a hybrid power flow controller planning and configuring method considering wind power integration is disclosed, which includes the following steps:
s1: acquiring wind power-load data, and dividing a wind output-load probability scene by using a K-means clustering algorithm;
s2: establishing an HPFC planning configuration optimization model considering multiple scenes;
s3: calculating target functions corresponding to different scenes;
s4: obtaining a total objective function according to the occurrence probability weighting of different scenes, and recording an individual optimal objective function, a group optimal objective function, and HPFC (high performance filter) addresses and capacities corresponding to the individual optimal objective function and the group optimal objective function;
s5: if the iteration number does not reach the set value, turning to S6, otherwise, turning to S7;
s6: updating the parameters and the speed of the control variable, and turning to S3;
s7: and outputting the optimal objective function and the HPFC planning configuration result.
The K-means algorithm in step S1 is a space clustering algorithm based on the idea of division, and the application range of the K-means algorithm is the widest among all clustering algorithms, and has the following advantages: the algorithm has clear thought, simple and clear structure, easy realization and fast iterative convergence, and can be effectively applied to big data analysis and processing.
The main principle of the K-means clustering algorithm is as follows: a large amount of sample data is divided into a small number of sets, the sets are also called clusters, and the characteristics of each cluster are represented by a cluster center. On the basis, the samples are classified by using the distance between the samples and the clustering center, and the clustering center is updated according to the samples in the clustering. And the similarity of the same clustering sample is enhanced through repeated iteration, so that the reduction of the sample data is realized. The calculation process is as follows:
1) acquiring wind power-load sample data, and establishing a sample data matrix X.
Figure BDA0003758130580000081
Wherein X is { X ═ X 1 ,x 2 ,…,x n }；x j ＝{x j1 ,x j2 ,…,x jm }; m is the number of wind power-load nodes; n is the number of samples.
2) Randomly selecting K samples as initial clustering centers u i ,u i ＝{u i1 ,u i2 ,…,u im },i＝1,2,…,K。
3) The quality of clustering depends mainly on the similarity of data sequences in the same subset in X and the difference of data among different subsets. In order to characterize the similarity and difference between data sequences, Euclidean distance is adopted to characterize the distance between a sample and a cluster center, and the calculation formula is as follows:
Figure BDA0003758130580000082
the euclidean distances of each sample to each cluster center are compared and grouped with the nearest cluster center as a subset.
4) The cluster center is recalculated according to the following formula:
Figure BDA0003758130580000083
wherein x is j Is a subset S i A set of m-dimensional sample sequences of, N i To divide into subsets S i The number of medium m-dimensional samples.
The cluster center of a subset is the newly generated data sequence after averaging the data within the subset, but not necessarily the data sequence actually contained in data object X.
5) And (3) taking the standard measure function value E as an iteration convergence criterion, wherein the calculation formula is as follows:
Figure BDA0003758130580000091
6) to screen out the best-performing clustering results, D is selected CH(+) Index as clusteringThe effectiveness index of (1). D CH(+) The definition of the index is:
Figure BDA0003758130580000092
wherein, T K The method is the sum of squares of the inter-class separation difference and mainly reflects the inter-class separation; p is K The method is an intra-class dispersion square sum and mainly reflects intra-class compactness; k is the number of clusters.
With increasing value of K, P K Tends to decrease, T K And gradually increases. D CH(+) The larger the index value is, the better the clustering effect of the K value is. By calculating D for each value of K CH(+) Indexes are obtained, and comparison is carried out to find out the optimal clustering number.
7) Probability of occurrence λ of each scene p The following formula was used for the calculation.
Figure BDA0003758130580000093
Wherein N is p Is the number of samples contained in each scene.
The planning configuration optimization model of the HPFC in step S2 is as follows:
1) controlled variable
Generally, the control variables of the power system are mainly: active power P of generator g Node voltage V of the generator g (i.e., the PV node voltage). For HPFC, the control variable has an addressing location L; constant voltage value V of parallel side shu (ii) a Active and reactive power flow target value P of controlled line ref 、Q ref 。
Transformer transformation ratio T k Setting the segmentation step length to be 0.02; a pre-installation position set is arranged at the HPFC address selection position L; and all other control variables are not provided with segment step length and randomly take values within the range.
2) Objective function
The optimization calculation of the HPFC expansion planning configuration is carried out from two aspects of reducing investment cost and solving the problem of system load flow out-of-limit, the objective functions of the optimization model comprise HPFC configuration cost, system network loss, system static safety margin and N-1 thermal stability margin, and the specific calculation mode of each objective function is as follows:
hpfc configuration costs.
The HPFC configuration cost employs a polynomial model, as follows:
cost H ＝C UPFC ×S UPFC +C ST ×S ST (7)
Figure BDA0003758130580000101
wherein, C UPFC And C ST Capacity price factor for UPFC and ST; a is 1 ～a 6 Is a constant in the price coefficient function; s UPFC And S ST Capacity of UPFC and ST;
taking the maximum capacity used in all operational scenarios as the final capacity:
Figure BDA0003758130580000102
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003758130580000103
the current phasor is the injection voltage phasor and the current phasor flowing through the UPFC series side converter under the scene k;
Figure BDA0003758130580000104
the current phasor is the injection voltage phasor and the current phasor flowing through the UPFC parallel side converter under the scene k;
Figure BDA0003758130580000105
the current phasor is the injection voltage phasor and the current phasor of the ST series side converter under a scene k; b. and (5) loss of a system network.
The network loss of the system is mainly active loss caused by the fact that power flow passes through a line, namely:
Figure BDA0003758130580000106
wherein Nl is the total number of branches of the system, P mij And P mji The active power flows from the head end of the line to the tail end of the line and from the tail end of the line to the head end of the line respectively for the mth branch.
The following can be obtained by simplification:
Figure BDA0003758130580000107
wherein G is m Denotes the conductance on the mth branch, V mi 、θ mi 、V mj 、θ mj The voltage amplitude and the phase angle of the head end node and the tail end node of the mth branch are respectively.
c. System static safety margins.
Defining the ratio of the line current to the line current limit value as the line load rate M m Therefore, the running state of the line is reflected.
Figure BDA0003758130580000111
Wherein S is m Is the apparent power, S, flowing through branch m m0 Is the apparent power limit value of branch m.
The static safety margin of the system under the normal condition can be obtained through the line load rate:
Figure BDA0003758130580000112
d. system N-1 thermal stability margin.
When evaluating the safety of the system, the N-1 fault analysis is needed to be carried out on the system. When one line breaks down, the power flow distribution of the system is greatly changed, so that the power flow of each line is influenced, the overload phenomenon of partial lines can be caused, and the safe operation of the power system is damaged. The thermal stability index of the system N-1 when the N-1 fault occurs is as follows:
Figure BDA0003758130580000113
wherein N is c Is an expected failure set; n is c The total number of predicted fault lines;
Figure BDA0003758130580000114
representing the load rate of branch m after the fault.
When the line is overloaded in the calculation process, a penalty value needs to be added to the line safety index.
3) Constraint conditions
In the optimization process, the constraints of the power system operation and the HPFC itself must be satisfied to ensure the normal operation of the grid and the HPFC.
a. Power system operating constraints.
For the power system, the node power balance needs to be satisfied under normal operation conditions, namely:
Figure BDA0003758130580000121
wherein Nb is the total number of nodes of the system; p gi 、Q gi The active power and the reactive power of the generator are respectively; p di 、Q di The load nodes are active and reactive loads of the load nodes; g ij 、B ij Elements in the node admittance matrix;
in addition, each variable needs to satisfy a corresponding inequality constraint. For the control variable u, there are:
Figure BDA0003758130580000122
wherein, P g For active power output of the generator, P gmax And P gmin The active limit value for keeping stable operation of each generator is specifically taken as power generationRelevant parameters of the motor; v g Is the PV node voltage magnitude.
For the state variable x, there are:
Figure BDA0003758130580000123
wherein S is l Is the apparent power, S, of the line l lmax A power limit to maintain stable operation for the line; q g For reactive power output of the generator, Q gmax And Q gmin Specifically taking values of the upper limit and the lower limit of the reactive power output of each generator as relevant parameters of the generators; v b The voltage amplitudes of the nodes other than the PV node.
HPFC self-constraint
The capacity and series side output voltage amplitude for HPFC are:
Figure BDA0003758130580000124
wherein S is UPFCmax And S STmax Maximum capacity for set UPFC and ST; v semax And V crmax The corresponding UPFC and ST series side voltage magnitudes at maximum capacity.
The power flow control target for the HPFC needs to satisfy the following conditions:
Figure BDA0003758130580000131
wherein n represents a line on which the HPFC is installed, P nmax 、P nmin 、Q nmax 、Q nmin The upper limit and the lower limit of the active power flow and the reactive power flow of the line n are respectively.
Furthermore, the power balance of the nodes on the HPFC access line also needs to take into account the effect of the additional injected power.
Fig. 2 shows an equivalent circuit diagram of the HPFC. The master control circuit of the HPFC is a circuit ij, and m and n are additional virtual nodes; v se 、θ se Is UPAmplitude and phase angle of the output voltage at the FC series side; v sh 、θ sh The amplitude and phase angle of the output voltage at the parallel side of the UPFC; v cr 、θ cr The amplitude and phase angle of the output voltage for the ST series side; v vr 、θ vr The amplitude and phase angle of the output voltage for the ST parallel side; x se 、X sh Equivalent impedance of UPFC series and parallel coupling transformers; x cr 、X vr Equivalent impedance of the ST series-parallel coupling transformer;
Figure BDA0003758130580000132
the current phasor flowing through the series side and the parallel side of the UPFC;
Figure BDA0003758130580000133
is the current phasor flowing through the ST serial and parallel sides; v k 、θ k (k ═ i, j, m, n) is the corresponding node voltage amplitude and phase angle; g L 、b L 、B c Respectively the equivalent conductance and susceptance at the serial side of the line and the parallel susceptance of the line; p nj 、Q nj Is the active and reactive power flow of the controlled line.
As can be seen from fig. 2, according to the power injection model, the voltage source in the line can be equivalent to the injected power to the nodes i, m, n:
Figure BDA0003758130580000134
wherein, P ks 、Q ks And (k is i, m and n) respectively represent the active and reactive injection power of the HPFC equivalent to each node.
Neglecting self-losses, HPFC satisfies UPFC active conservation and ST power conservation:
Figure BDA0003758130580000141
the convergence criterion of the flow calculation iteration end of the system with the HPFC is as follows:
Figure BDA0003758130580000142
wherein, P ref And Q ref Setting a power flow control target value; p nj And Q nj Is the current of the controlled line; delta P and delta Q are iteration difference matrixes of node injection power P, Q in the load flow calculation process; epsilon 1 、ε 2 To calculate accuracy.
Some examples of the invention also relate to a computer-readable storage medium having instructions stored thereon. When the instructions are executed, the planning and configuration method of the hybrid power flow controller can be realized. More specifically, the instructions may be in a computer readable language. The computer may be a general purpose computing device or a special purpose computing device. In a specific implementation, the computer may be a desktop computer, a laptop computer, a network server, a Personal Digital Assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more integrated servers, data centers, and the like. For example, the storage medium may be, but is not limited to, a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a Digital Versatile Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)).
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (10)

1. A planning configuration method of a hybrid power flow controller considering wind power integration is characterized by comprising the following steps:
step S1: acquiring wind power-load data, and dividing a wind output-load probability scene by using a K-means clustering algorithm;
step S2: establishing an HPFC planning configuration optimization model considering multiple scenes;
step S3: calculating target functions corresponding to different scenes;
step S4: weighting according to the occurrence probability of different scenes to obtain a total objective function, and recording an individual optimal objective function, a group optimal objective function, and HPFC (high performance filter) addresses and capacities corresponding to the individual optimal objective function and the group optimal objective function;
step S5: if the iteration number does not reach the set value, turning to the step S6, otherwise, turning to the step S7;
step S6: updating each control variable, and proceeding to step S3;
step S7: and outputting the optimal objective function and the HPFC planning configuration result.
2. The planning and configuration method of the hybrid power flow controller considering wind power integration according to claim 1, wherein the method for dividing the wind output-load probability scene by using the K-means clustering algorithm comprises the following steps:
1) acquiring wind power-load sample data, and establishing a sample data matrix X.
Figure FDA0003758130570000011
Wherein X ═ { X ═ X 1 ,x 2 ,…,x n }；x j ＝{x j1 ,x j2 ,…,x jm }; m is the number of wind power-load nodes; n is the number of samples;
2) randomly selecting K samples as initial clustering centers u i ，u i ＝{u i1 ,u i2 ,…,u im },i＝1,2,…,K；
3) The Euclidean distance is adopted to represent the distance between the sample and the clustering center, and the calculation formula is as follows:
Figure FDA0003758130570000012
comparing the Euclidean distance between each sample and each clustering center, and grouping the Euclidean distance and the nearest clustering center into a subset;
4) recalculating the clustering centers:
Figure FDA0003758130570000021
wherein x is j Is a subset S i A set of m-dimensional sample sequences of, N i For dividing into subsets S i The number of medium m-dimensional samples;
5) and using a standard measure function value E as an iteration convergence criterion, wherein the calculation formula is as follows:
Figure FDA0003758130570000022
6) selection D CH(+) The index is taken as an index of effectiveness of clustering, D CH(+) The definition of the index is:
Figure FDA0003758130570000023
wherein, T K The method is the sum of squares of inter-class separation differences, and mainly reflects the inter-class separability; p K Is like internally separatedThe difference sum of squares, which mainly reflects the intra-class compactness; k is a clustering number;
by calculating D for each value of K CH(+) Indexes are obtained, and the indexes are compared to find out the optimal clustering number;
7) probability of occurrence λ of each scene p The following formula was used for calculation;
Figure FDA0003758130570000024
wherein N is p Is the number of samples contained in each scene.
3. The method as claimed in claim 1, wherein the planning configuration optimization model of the HPFC comprises: the target function and the constraint condition are used for developing, planning, configuring and optimizing the HPFC from two aspects of reducing investment cost and solving the problem of out-of-limit system power flow, the target function of the optimization model comprises HPFC configuration cost, system network loss, system static safety margin and N-1 thermal stability margin,
the objective function comprises: HPFC configuration costs; system network loss; a system static safety margin; and a system N-1 thermal stability margin;
the constraint conditions comprise: power system operating constraints and HPFC self constraints.
4. The hybrid power flow controller planning and configuring method considering wind power integration according to claim 3, wherein the HPFC configuration cost is as follows:
cost H ＝C UPFC ×S UPFC +C ST ×S ST (7)
Figure FDA0003758130570000031
wherein, C UPFC And C ST Capacity price factor for UPFC and ST；a 1 ～a 6 Is a constant in the price coefficient function; s UPFC And S ST Capacity of UPFC and ST;
taking the maximum capacity used in all operational scenarios as the final capacity:
Figure FDA0003758130570000032
wherein the content of the first and second substances,
Figure FDA0003758130570000033
the current phasor is the injection voltage phasor and the current phasor flowing through the UPFC series side converter under the scene k;
Figure FDA0003758130570000034
the current phasor is the injection voltage phasor and the current phasor flowing through the UPFC parallel side converter under the scene k;
Figure FDA0003758130570000035
the injection voltage phasor and the current phasor of the ST series-side converter in scene k are shown.
5. The planning and configuration method of the hybrid power flow controller considering the wind power integration according to claim 3, wherein the objective function calculation method of the system network loss comprises the following steps:
the network loss of the system comprises active loss caused by power flow passing through a line, namely:
Figure FDA0003758130570000036
wherein Nl is the total branch number of the system; p mij And P mji The active power flows from the head end of the line to the tail end of the line and from the tail end of the line to the head end of the line respectively for the mth branch.
The following can be obtained by simplification:
Figure FDA0003758130570000041
wherein G is m Represents the conductance on the mth branch; v mi 、θ mi 、V mj 、θ mj The voltage amplitude and the phase angle of the head end node and the tail end node of the mth branch are respectively.
6. The planning and configuration method of the hybrid power flow controller considering wind power integration according to claim 3, wherein the objective function calculation method of the system static safety margin comprises the following steps:
defining the ratio of the line flowing through power flow to the line power flow limit value as a line load rate M m So as to reflect the running state of the line;
Figure FDA0003758130570000042
wherein S is m Is the apparent power flowing through branch m; s. the m0 Is the apparent power limit value of branch m.
The static safety margin of the system under the normal condition can be obtained through the line load rate:
Figure FDA0003758130570000043
7. the planning and configuration method of the hybrid power flow controller considering the wind power integration according to claim 3, wherein the calculation method of the objective function of the thermal stability margin of the system N-1 comprises the following steps:
the thermal stability index of the system N-1 when the N-1 fault occurs is as follows:
Figure FDA0003758130570000044
wherein N is c Is an expected failure set; n is c The total number of predicted fault lines;
Figure FDA0003758130570000045
representing the load rate of the branch m after the fault;
when the line is overloaded in the calculation process, a penalty value needs to be added to the line safety index.
8. The hybrid power flow controller planning and configuring method considering wind power integration according to claim 3, wherein the calculation method of the power system operation constraint comprises the following steps:
the power system needs to satisfy the node power balance, namely:
Figure FDA0003758130570000051
wherein Nb is the total number of nodes of the system; p gi 、Q gi The active power and the reactive power of the generator are respectively; p di 、Q di The load nodes are active and reactive loads of the load nodes; g ij 、B ij Elements in the node admittance matrix;
each variable needs to satisfy a corresponding inequality constraint; for the control variable u, there are:
Figure FDA0003758130570000052
wherein, P g For active power output of the generator, P gmax And P gmin The active limit value for keeping stable operation of each generator is specifically taken as a relevant parameter of the generator; v g Is the PV node voltage amplitude;
for the state variable x, there are:
Figure FDA0003758130570000053
wherein S is l Is the apparent power, S, of the line l lmax A power limit to maintain stable operation for the line; q g For reactive power output of the generator, Q gmax And Q gmin Specifically taking values of the upper limit and the lower limit of the reactive power output of each generator as relevant parameters of the generators; v b The voltage amplitudes of the nodes other than the PV node.
9. The planning and configuration method of the hybrid power flow controller considering the wind power integration according to claim 3, wherein the calculation method of the HPFC self-constraint comprises the following steps:
the capacity and series side output voltage amplitude for HPFC are:
Figure FDA0003758130570000054
wherein S is UPFCmax And S STmax Maximum capacity for set UPFC and ST; v semax And V crmax The corresponding UPFC and ST series side voltage magnitudes at maximum capacity.
The following condition needs to be satisfied for the power flow control target of the HPFC:
Figure FDA0003758130570000061
wherein n represents a line on which the HPFC is installed, P nmax 、P nmin 、Q nmax 、Q nmin The upper limit and the lower limit of the active power flow and the reactive power flow of the line n are respectively.
10. A computer readable storage medium storing instructions which, when executed, can implement the hybrid power flow controller planning and configuring method of any one of claims 1 to 9.
CN202210873554.7A 2022-07-21 2022-07-21 Hybrid power flow controller planning configuration method considering wind power integration Pending CN115000969A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116679627A (en) * 2023-05-04 2023-09-01 安徽机电职业技术学院 Coordinated control method for controlling multiple electrical devices

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116679627A (en) * 2023-05-04 2023-09-01 安徽机电职业技术学院 Coordinated control method for controlling multiple electrical devices

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