CN117477541A - Urban power grid reactive voltage dynamic optimization control method based on zone control - Google Patents
Urban power grid reactive voltage dynamic optimization control method based on zone control Download PDFInfo
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Abstract
The invention discloses a dynamic optimization control method for reactive voltage of an urban power grid based on zone control, and relates to the field of reactive power control of the power grid. The method comprises the following steps: (1) data preparation. Load prediction is carried out based on historical operation data of the power grid, load segmentation is carried out according to a load change curve of one day in the future, and the load segmentation can be 24 segments or 48 segments; (2) grid partitioning. According to a power grid topological structure, a power grid node voltage-reactive sensitivity matrix is established, and a double-layer clustering method is established by combining the pearson coefficient and the Euclidean distance to perform power grid reactive optimization partition; (3) dynamic reactive day-ahead optimization. Establishing a dynamic reactive power optimization model based on load segmentation, wherein the active network loss, the voltage offset and the equipment action cost of the model are used as objective functions, discrete equipment action frequency constraint and the like are comprehensively considered, and an improved balance optimizer algorithm is utilized to carry out model solution so as to obtain a daily power grid reactive power optimization control scheme; and (4) real-time reactive voltage control. And monitoring the operation data of the power grid in real time, wherein the operation data comprises voltage and power flow, and carrying out reactive voltage optimization control correction according to the actual voltage, flow and load deviation conditions to obtain a reactive voltage control scheme of the power grid based on the real-time operation data. The method has the advantages that the power grid is partitioned and integrated into the dynamic reactive power optimization field, and the reactive power voltage control capability of the power grid is improved.
Description
Technical Field
A dynamic optimization control method for reactive voltage of an urban power grid based on zone control relates to the field of reactive power control of the power grid.
Technical Field
The reactive power optimization of the power grid is an important means for improving the safety stability and economy of the power system, and the reasonable configuration of reactive power can reduce the active loss of the power grid, reduce the reactive power dispatching cost, improve the system stability and improve the voltage quality. With national economy development, the scale of the urban power grid is continuously expanded, a large number of power transmission cables are connected into the power grid, and the occurrence frequency of reactive power excess conditions is increased due to the generated working capacitance. The urban power grid power system has the characteristics of high complexity, high control difficulty, strong regularity, large peak-valley difference and the like, so that the dynamic reactive power optimization of the urban power grid is more complex.
The dynamic reactive power optimization problem of the urban power grid is a multi-objective mixed integer nonlinear optimization problem of coupling on a time scale, dimensions and orders of magnitude among a plurality of objective functions are inconsistent, and weights are difficult to allocate. The existing algorithm has the problems of complex concept, low calculation efficiency, unreasonable multi-objective function weight distribution and the like, and is difficult to effectively improve the power supply quality of the system.
Disclosure of Invention
The invention provides a dynamic optimization control method for reactive voltage of an urban power grid based on zone control, which is characterized by comprising the following steps:
(1) Data preparation. Load prediction is carried out based on historical operation data of the power grid, load segmentation is carried out according to a load change curve of one day in the future, and the load segmentation can be 24 segments or 48 segments;
(2) And (5) partitioning the power grid. According to a power grid topological structure, a power grid node voltage-reactive sensitivity matrix is established, and a double-layer clustering method is established by combining the pearson coefficient and the Euclidean distance to perform power grid reactive optimization partition;
(3) Dynamic reactive day-ahead optimization. Establishing a dynamic reactive power optimization model based on load segmentation, wherein the active network loss, the voltage offset and the equipment action cost of the model are used as objective functions, discrete equipment action frequency constraint and the like are comprehensively considered, and an improved balance optimizer algorithm is utilized to carry out model solution so as to obtain a daily power grid reactive power optimization control scheme;
(4) And (5) real-time reactive voltage control. And monitoring the operation data of the power grid in real time, wherein the operation data comprises voltage and power flow, and carrying out reactive voltage optimization control correction according to the actual voltage, flow and load deviation conditions to obtain a reactive voltage control scheme of the power grid based on the real-time operation data.
Further, the future operation state of the power grid in the step (1) is predicted. Removing data of the fault condition of the power grid by taking the day as a unit, and combining daily active consumption in the past 30 days into a combined set Z= { W 1 ,W 2 ,...W 30 Predicting the change condition of the active consumption of the next incoming call network by a data fitting mode; considering that the urban power network has obvious high and low peaks, large load fluctuation and stable period, the active power consumption in different periods is affected by weather and other factors, and the active power consumption W of each day is divided into periods according to peak-valley electricity prices i Is divided into a high peak section, a normal section and a low peak section (w i,1 ,w i,2 ,w i,3 I=1, ··, 30), and respectively carrying out the prediction method for predicting the change condition of the active consumption through data fitting.
Further, the reactive-voltage sensitivity matrix in the step (2) is solved. The method comprises the steps of setting N load nodes to be partitioned in a power grid to form a set N, setting M reactive power sources and nodes with reactive power regulation capability to form a set M. The construction of the reactive-voltage sensitivity matrix comprises the following steps:
(1) Calculating to obtain daily average power of each node of the power grid by using historical power data of the power grid, and performing reactive power optimization by using a balance optimizer algorithm to obtain compensation values (q j J=1, 2,..m), and the voltage of the node in N (v i ,i＝1,2,...n)。
(2) For the j-th reactive node belonging to M, setting the rest nodes in M as PQ nodes, and adding correction quantity delta q to the reactive compensation value of the PQ nodes:
q′ j ＝q j +Δq (1)
wherein: q's' j And q j The reactive power compensation quantity after correction and before correction is obtained; Δq is the reactive compensation amount correction value.
The reactive compensation values of the other reactive sources remain unchanged. Obtaining the voltage (v) of each node in N through load flow calculation i,j I=1, 2, n), and v i The difference is made to obtain a voltage difference:
Δv i,j ＝v i,j -v i (2)
wherein: v i,j The voltage of the load node i is increased under the condition that delta q is added to the compensation value of the reactive power source j; v i Representing an initial voltage value of the load node i; deltav i,j And adding delta q to the compensation value of the reactive power source j for the load node i, and then adding the node voltage change value.
Let reactive-voltage sensitivity be S i,j The reactive-voltage sensitivity matrix S is constructed, defined as:
wherein: s is S i,j Representing load nodesi voltage sensitivity to reactive power source j; deltav i,j Adding delta q to the reactive power source j compensation value of the load node i, and then adding a node voltage change value; Δq is the reactive compensation variation.
Further, the dual-layer clustering based on the voltage sensitivity matrix in the step (2) includes the following steps:
(1) And reading voltage sensitivity matrix data, wherein each row represents the voltage sensitivity of the load node to be classified to each reactive power source, and simultaneously reflects the influence capability of the reactive power source to each node, the voltage sensitivity is used as an object cluster, and the reactive power source nodes are not positioned at the edge of the partition. Therefore, the reactive power source nodes do not participate in clustering, and the partitions are planned according to the principle of nearby after the load nodes are clustered.
(2) The first layer of clusters comprises Kmeans clusters based on similarity measurement of numerical distance and Kmeans clusters based on similarity measurement of morphological characteristics. The numerical distance is European distance d m,x Morphology features adopt Pelson coefficient rho m,x ：
Wherein: s is S i,j Representing the voltage sensitivity of the load node i to the reactive power source j; s is S x,i Representing the voltage sensitivity of the reactive source i in the center point x;represent S i Is the average value of (2);Represent S x Is the average value of (2); j is the total number of reactive sources.
For n load nodes to be partitioned, randomly selecting k samples as a center point, where k is not greater thanIs the largest integer of (2); calculating the similarity between each sample and the center point, and clustering d based on Euclidean distance by taking the highest similarity as a clustering basis m,x Small similarity high, p in pearson coefficient-based clusters m,x The large similarity is high; judging whether the clustering result is consistent with the last time, if so, outputting the clustering result, otherwise, recalculating the similarity and classifying by taking the member mean value in the cluster as a central value until the clustering result is unchanged, and obtaining a clustering result p.
The clustering result is biased each time due to the randomness of the initial center point selection. According to the invention, randomness is fully reflected by carrying out multiple clustering, and 15 times of kmeans clustering based on Euclidean distance and 15 times of kmeans clustering based on Pelson coefficient are carried out.
Calculating the similarity between each sample in the cluster and the center point, and d in the cluster based on Euclidean distance m,x Small similarity high, p in pearson coefficient-based clusters m,x The large similarity is high, and the sum of the similarity of all samples is the similarity index of the clustering result:
d all ＝∑d i (7)
ρ all ＝∑ρ i (8)
wherein: d, d all Is a similarity index based on Euclidean distance clustering results; ρ all Is a similarity index based on the pearson coefficient clustering result; d, d i The Euclidean distance between the sample i and the center point of the sample i; ρ i Is the pearson coefficient for sample i and its center point.
5 clustering results with lower similarity are removed by the two clustering modes, and the rest clustering results form a clustering result set P= (P) 1 ,p 2 ,...p 20 )。
(2) And (5) second-layer clustering, and selecting base clustering. The basic clustering is used as the basis of subsequent classification, so that the advantages of two clustering indexes are fully combined, the clustering quality is improved, k samples serving as the basic clustering have enough differences, and have enough similarity with other samples, and the similarity index d (p i ,p j ) And similarity index D (p i ,P)：
Wherein:representing the cluster p i Data S of (B) a A dataset of the cluster;Representing the cluster p j Data S of (B) a A dataset of the cluster;Representing the cluster p i Data S of (B) a Belonging clusters and cluster p j Data S of (B) a The number of data of the cluster intersection to which the cluster belongs; |p| represents the data amount of the collection P; d (p) i ,p j ) Represents p i And p is as follows j Similarity between the values of 0,1]The larger the value, the more the cluster p i And p is as follows j The higher the similarity is, the square can effectively reduce the value of the similarity when the difference is larger; d (p) i P) represents the cluster P i Similarity with clusters contained in cluster set P, value range [0,1 ]]The larger the number, the higher the similarity.
With D (p) i P) selecting one sample P for index selection i As a first basis cluster, p i Should be sufficiently similar to the rest of the sample, D (p i P) the largest one as the first base cluster. For the rest of the base clusters, the similarity index D (p) is further defined by requiring enough similarity with the rest of the samples and enough difference with the selected base clusters i ,P,P′)：
Wherein: p is a cluster set comprising unselected clusters and selected clusters; p' is a collection of clusters that have been selected as base clusters.
With D (p) i Samples are selected as basic clusters by using the maximum of P and P' as indexes, and D (P) is recalculated after each selection i P, P') and select new samples until there are k base clusters.
(3) Second-layer clustering, construction data S i And S is equal to j And (5) an inter-similarity matrix A. Based on S i And S is equal to j Similarity under classification mode of different base clusters, definition similarity is a ij ：
Wherein:representation data S i Clustering p 'with basis' η A data set which belongs to a cluster in a classification mode; x-shaped articles η (S i ,S j ) Reaction data S i And S is equal to j Clustering p 'on basis' η Whether belonging to the same cluster in the classification of (a); a, a ij Reaction data S i And S is equal to j Clustering p 'on basis' η Similarity in classification, value range [0,1]The larger the number is, the higher the similarity is, and the presence of square can effectively cut down the value of the similarity when the difference is large.
(4) And (5) second-layer clustering, and hierarchical aggregation clustering. And regarding each row of the matrix A as a sample, and performing hierarchical aggregation clustering by taking Euclidean distance as an index. Each sample is regarded as one cluster, euclidean distances among different clusters are calculated to merge adjacent clusters until the number of the clusters reaches k.
Further, the dynamic reactive power optimization model in the step (2).
And (3) load flow constraint:
wherein: p (P) Gi And P Li Representing the active power of the motor and the load; q (Q) Gi And Q is equal to Li Representing reactive power of the motor and the load; q (Q) Ci Representing reactive power of the reactive source; u (U) i And U j Representing the voltage magnitudes of nodes i and j; θ ij Representing the phase angle difference of nodes i and j.
Voltage constraint:
U i min ≤U i ≤U i max (17)
wherein: u (U) i min And U i max Representing the lower limit and the upper limit of the voltage constraint of the node i; u (U) i Representing the voltage magnitude at node i.
Reactive power source output constraint:
wherein: q (Q) MCR And Q is equal to L Reactive power provided by MCR and reactance, respectively; q (Q) MCR min And Q is equal to MCR max Providing reactive upper and lower limits for MCR; k (k) L The reactance switching quantity is discrete value, k L min And k is equal to L max The upper limit and the lower limit of the switching quantity are adopted.
And (3) limiting the switching speed of the reactor group:
|k L (t)-k L (t+1)|≤Δk max (19)
wherein: k (k) L (t) and k L The (t+1) represents the switching quantity of the reactors in the t period and the t+1 period, and the switching quantity in the unit period is limited because the switching of the reactors can cause power grid fluctuation; Δk max Representing the maximum value of the number of switching in a unit time period.
Substation transformation ratio is limited:
wherein: t is the transformer transformation ratio; k (k) T A variable tap position; delta T min The variable quantity of the gear ratio between different gears is obtained; k (k) T min And k is equal to T max The lower limit and the upper limit of the tap gear are respectively defined.
Reactive power all-day dispatch and tap shift limits:
wherein: q (Q) max And k is equal to max Respectively representing the schedulable amounts of the MCR and the reactor in one day; t (T) max The frequency of shifting the transformer tap in one day is represented; ΔQ max (t)、Δk max (T) and DeltaT max And (t) respectively representing the maximum dispatching values of the MCR, the reactor group and the transformer substation in the t period, and determining according to the total power difference of the power grid in the front period and the rear period in the predicted power, wherein the definition is as follows:
the objective function comprises minimum active loss, minimum voltage deviation, minimum reactive power dispatching cost and minimum transformer dispatching cost.
Active loss P loss Minimum:
wherein: n is the number of load nodes, m is the number of reactive power sources, and n+m represents the total number of nodes of the power grid; g ij Representing the conductance between node i and node j; representing the phase difference θ between node i and node j ij 。
Voltage deviation U d Minimum:
wherein: u (U) i Is the voltage of the node i; u' is the target voltage of the node i; n+m is the total number of nodes.
Reactive compensation switching quantity Q all Minimum:
wherein: q (Q) MCRi (t) represents the reactive compensation amount of the MCR at time t; k (k) Li (t) represents the switching number of the reactor at the moment t; ΔQ Lmin The reactive compensation difference value of the primary switching of the reactor is represented; m and n are the total number of MCR and the total number of reactors, respectively.
Tap position change T of transformer substation all Minimum:
wherein:T all the sum of the tap shift numbers of the transformer; k (k) Ti (t) is tap position of the transformer i at t moment; y is the total number of transformers.
Consider that the reactor group and the transformer have minimum switching quantity delta Q L min With minimum transformation ratio delta T min Is limited to min Q all Discretizing the reactive compensation value of the reactor group:
wherein: k' L And k is equal to L The switching numbers after discretization and before discretization of the reactor are respectively; k (k) Li For a certain switching number of the reactor, the switching number is larger than k L Is the smallest integer of (a); k' T And k is equal to T The gear positions after and before the discrete of the transformer tap are respectively; k (k) Ti For a certain gear, is greater than k T Is the smallest integer of (a); η (eta) Q The discrete range was defined and taken to be 0.5.
The method processes the problem that the dimensions and orders of magnitude are different between multiple objective functions by normalizing and establishing satisfaction functions:
wherein: f (f) i F is the normalized objective function 1 Is P loss Normalized to f 2 Is U (U) d Normalized to f 3 Is Q all Normalized to f 4 Is T all Normalizing; f is the original objective function value; f (f) max Is an unoptimized function value; f (f) min To consider only the objective function value under the corresponding objective. Satisfaction function value range [0,1 ]]Smaller values indicate higher satisfaction.
Adding weight coefficients to satisfaction functions of different targets, and comprehensively defining an objective function F:
wherein: weight coefficientDetermined by analytic hierarchy process.
Definition f i The ratio of the importance of eachConstructing a judgment matrix A:
normalizing A by column, summing by row, and normalizing again:
obtaining weight coefficient
Further, the improved balance optimizer algorithm in step (3), wherein the important parameters include:
five elite leadership balancing pool:
C eq,pool ＝{C eq(1) ,C eq(2) ,C eq(3) ,C eq(4) ,C eq(ave) } (39)
wherein: c (C) eq(1) To C eq(4) For the optimal four solutions in the iteration, C eq(ave) Is the mean of these four solutions.
The local and global searching capability of the algorithm is comprehensively considered, and the F coefficient is improved:
wherein: a, a 1 Is constant 2; sign is a sign taking function; r and lambda are [0,1 ]]Random values in between; iter and Maxiter represent the current iteration number and the maximum iteration number.
Mass generation rate G:
G＝G cp F(C eq -λC) (41)
wherein: r is (r) 1 And r 2 w is [0,1 ]]Random values in between; GP is constant 0.5.
According to the key parameters, the final iteration formula is as follows:
wherein: c (C) eq Is a member of the five elite equilibrium pool.
Further, the monitoring stage in the step (4) includes the following steps:
(1) In the real-time reactive voltage control stage, the voltage of each node is monitored in real time, and whether the situation of overlarge voltage deviation exists or not is judged every 20 minutes, so that the target voltage u i And the actual voltage u' i The relationship should be:
wherein: u's' i The actual voltage of the node i; u (u) i Target voltage for node i; epsilon min And epsilon max The lower and upper limits of the relative deviation, respectively.
If the voltage deviation of a certain node in the period i is overlarge, judging the partition to which the node belongs, and acquiring the actual power condition (p 'of the node in the period i' i ,q′ i ) And under the condition of controlling only the regional reactive power source, carrying out reactive power optimization calculation under the actual power condition. And simultaneously, the later prediction data is adjusted, and the predicted power of all nodes in the voltage deviation overlarge area is corrected:
(2) And (3) recalculating the reactive power configuration scheme after the period i, and subtracting the scheduling limit consumed in the voltage out-of-limit condition from the subsequent reactive power scheduling limit.
Drawings
Fig. 1: euclidean distance-based kmeans clustering flow chart
Fig. 2: hierarchical aggregation class flow chart
Fig. 3: improved balance optimizer algorithm flow chart
Fig. 4: integrated flow chart of control system
Detailed description of the preferred embodiments
The invention will be further illustrated, but is not limited, by the examples given. A dynamic optimization control method for reactive voltage of an urban power grid based on zone control comprises the following steps:
(1) Data preparation. And carrying out load prediction based on historical operation data of the power grid, and carrying out load segmentation according to a load change curve of one day in the future, wherein the load segmentation is 24 segments.
(2) And (5) partitioning the power grid. The power grid is internally provided with N load nodes to be partitioned to form a set N, and the power grid is internally provided with M reactive power sources and nodes with reactive power regulation capability to form a set M. Calculating to obtain daily average power of each node of the power grid by using historical power data of the power grid, and performing reactive power optimization by improving a balance optimizer algorithm to obtain compensation values (q j J=1, 2,..m), and the voltage of the node in N (v i ,i＝1,2,...n)。
For the j-th reactive node belonging to M, setting the rest nodes in M as PQ nodes, and adding correction quantity delta q to the reactive compensation value of the PQ nodes:
q′ j ＝q j +Δq (1)
wherein: q's' j And q j The reactive power compensation quantity after correction and before correction is obtained; Δq is the reactive compensation amount correction value.
The reactive compensation values of the other reactive sources remain unchanged. Obtaining the voltage (v) of each node in N through load flow calculation i,j I=1, 2, n), and v i The difference is made to obtain a voltage difference:
Δv i,j ＝v i,j -v i (2)
wherein: v i,j The voltage of the load node i is increased under the condition that delta q is added to the compensation value of the reactive power source j; v i Representing an initial voltage value of the load node i; deltav i,j And adding delta q to the compensation value of the reactive power source j for the load node i, and then adding the node voltage change value.
The reactive power-voltage sensitivity is S i,j Reactive-voltage sensitivity matrix S:
wherein: s is S i,j Representing the voltage sensitivity of the load node i to the reactive power source j; deltav i,j Adding delta q to the reactive power source j compensation value of the load node i, and then adding a node voltage change value; Δq is the reactive compensation variation.
A first layer clustering is performed. Based on Euclidean distance d with each row in reactive-voltage sensitivity matrix S as one sample m,x Is based on the pearson coefficient ρ m,x 15 times each, the Euclidean distance-based kmeans clustering flow is shown in FIG. 1, and the Pelson coefficient ρ is based m,x The kmeans clustering procedure of (c) is consistent. Euclidean distance d m,x Pearson coefficient ρ m,x Is defined as follows:
wherein: s is S i,j Representing the voltage sensitivity of the load node i to the reactive power source j; s is S x,i Representing the voltage sensitivity of the reactive source i in the center point x;represent S i Is the average value of (2);Represent S x Is the average value of (2); j is the total number of reactive sources.
Calculating similarity indexes of the 30 clustering results based on Euclidean distance d m,x The kmeans cluster similarity index of (d) all Based on the pearson coefficient ρ m,x Kmeans cluster similarity index of (2) all The definition is as follows:
wherein: d, d all Is a similarity index based on Euclidean distance clustering results; ρ all Is a similarity index based on the pearson coefficient clustering result; d, d i The Euclidean distance between the sample i and the center point of the sample i; ρ i Is the pearson coefficient for sample i and its center point.
5 clustering results with lower similarity are removed by the two clustering modes, and the rest clustering results form a clustering result set P= (P) 1 ,p 2 ,...p 20 )。
And performing second-layer clustering. Defining similarity index d (p) i ,p j ) And similarity index D (p i ,P)：
Wherein:representing the cluster p i Data S of (B) a A dataset of the cluster;Representing the cluster p j Data S of (B) a A dataset of the cluster;Representing the cluster p i Data S of (B) a Belonging clusters and cluster p j Data S of (B) a The number of data of the cluster intersection to which the cluster belongs; |p| represents the data amount of the collection P; d (p) i ,p j ) Represents p i And p is as follows j Similarity between the values of 0,1]The larger the value, the more the cluster p i And p is as follows j The higher the similarity, the more squares can be presentEffectively reducing the similarity value when the difference is large; d (p) i P) represents the cluster P i Similarity with clusters contained in cluster set P, value range [0,1 ]]The larger the number, the higher the similarity.
Select D (p) i P) largest sample P i As the first base cluster. Further defining similarity index D (p i ,P,P′)：
Wherein: p is a cluster set comprising unselected clusters and selected clusters; p' is a collection of clusters that have been selected as base clusters.
Continuing to select D (p) i The largest sample of P, P' is used as the base cluster, and D (P) is recalculated after each selection i P, P') and select new samples until there are k base clusters.
Construction data S i And S is equal to j And (5) an inter-similarity matrix A. Based on S i And S is equal to j Similarity under classification mode of different base clusters, definition similarity is a ij ：
Wherein:representation data S i Clustering p 'with basis' η A data set which belongs to a cluster in a classification mode; x-shaped articles η (S i ,S j ) Reaction dataS i And S is equal to j Clustering p 'on basis' η Whether belonging to the same cluster in the classification of (a); a, a ij Reaction data S i And S is equal to j Clustering p 'on basis' η Similarity in classification, value range [0,1]The larger the number is, the higher the similarity is, and the presence of square can effectively cut down the value of the similarity when the difference is large.
And (3) carrying out hierarchical aggregation clustering on the matrix A by taking each row as one sample, wherein the flow is shown in figure 2. Clustering is carried out by taking Euclidean distance as an index. Each sample is regarded as one cluster, euclidean distances among different clusters are calculated to merge adjacent clusters until the number of the clusters reaches k. And obtaining a clustering result of each load node of the power grid.
Determining the weight coefficient corresponding to the multiple objective functions, f 1 Is an active network loss P loss ，f 2 For voltage deviation U d ，f 3 For reactive power dispatching cost Q all ，f 4 For the transformer action cost T all . Definition f i The ratio of the importance of eachConstructing a judgment matrix A:
normalizing A by column, summing by row, and normalizing again:
obtaining weight coefficient
And (5) building a dynamic reactive power optimization model of the power grid.
Wherein: f (f) i F is the normalized objective function 1 Is P loss Normalized to f 2 Is U (U) d Normalized to f 3 Is Q all Normalized to f 4 Is T all Normalizing; f is the original objective function value; f (f) max Is an unoptimized function value; f (f) min To consider only the objective function value under the corresponding objective. Satisfaction function value range [0,1 ]]Smaller values indicate higher satisfaction; weight coefficientDetermined by analytic hierarchy process.
Constraint conditions:
U i min ≤U i ≤U i max (25)
|k L (t)-k L (t+1)|≤Δk max (27)
wherein: u (U) imin And U imax Representing the lower limit and the upper limit of the voltage constraint of the node i; u (U) i Representing the voltage magnitude of node i; q (Q) MCR And Q is equal to L Reactive power provided by MCR and reactance, respectively; q (Q) MCRmin And Q is equal to MCRmax Providing reactive upper and lower limits for MCR; k (k) L The reactance switching quantity is discrete value, k Lmin And k is equal to Lmax The upper limit and the lower limit of the switching quantity are set; k (k) L (t) and k L (t+1) represents the number of reactor switching between the t period and the t+1 period, becauseThe reactor switching can cause power grid fluctuation, and the switching quantity in a unit time period is limited; Δk max Representing the maximum value of the switching quantity in a unit time period; t is the transformer transformation ratio; k (k) T A variable tap position; delta T min The variable quantity of the gear ratio between different gears is obtained; k (k) Tmin And k is equal to Tmax The lower limit and the upper limit of the tap gear are respectively; q (Q) max And k is equal to max Respectively representing the schedulable amounts of the MCR and the reactor in one day; t (T) max The frequency of shifting the transformer tap in one day is represented; ΔQ max (t)、Δk max (T) and DeltaT max And (t) respectively representing the maximum dispatching values of the MCR, the reactor group and the transformer substation in the t period.
In the dynamic reactive power day-ahead optimization stage, an improved balance optimizer algorithm is used for calculating a dynamic reactive power optimization model, and the flow is shown in figure 3. Important parameters of the algorithm include:
five elite leadership balancing pool:
C eq,pool ＝{C eq(1) ,C eq(2) ,C eq(3) ,C eq(4) ,C eq(ave) } (34)
wherein: c (C) eq(1) To C eq(4) For the optimal four solutions in the iteration, C eq(ave) Is the mean of these four solutions.
The local and global searching capability of the algorithm is comprehensively considered, and the F coefficient is improved:
wherein: a, a 1 Is constant 2, a 2 Is a constant of 1; sign is a sign taking function; r and lambda are [0,1 ]]Random values in between; iter and Maxiter represent the current iteration number and the maximum iteration number.
Mass generation rate G:
G＝G cp F(C eq -λC) (36)
wherein: r is (r) 1 And r 2 w is [0,1 ]]Random values in between; GP is constant 0.5.
According to the key parameters, the final iteration formula is as follows:
wherein: c (C) eq Is a random member of the five elite equilibrium pool.
And in the real-time reactive voltage control stage, the voltage of each node is monitored in real time, and whether the voltage deviation is overlarge is judged every 20 minutes.
Wherein: u's' i The actual voltage of the node i; u (u) i Target voltage for node i; epsilon min And epsilon max The lower and upper limits of the relative deviation, respectively.
If the voltage deviation of a certain node in the period i is overlarge, judging the partition to which the node belongs, and acquiring the actual power condition (p 'of the node in the period i' i ,q′ i ) And under the condition of controlling only the regional reactive power source, carrying out reactive power optimization calculation under the actual power condition. And simultaneously, the later prediction data is adjusted, and the predicted power of all nodes in the voltage deviation overlarge area is corrected:
and (3) recalculating the reactive power configuration scheme after the period i, and subtracting the scheduling limit consumed in the voltage out-of-limit condition from the subsequent reactive power scheduling limit. The overall flow of the control system is shown in fig. 4.
Claims (7)
1. The dynamic optimization control method for the reactive voltage of the urban power grid based on zone control is characterized by comprising the following steps of:
(1) Data preparation. Load prediction is carried out based on historical operation data of the power grid, load segmentation is carried out according to a load change curve of one day in the future, and the load segmentation can be 24 segments or 48 segments;
(2) And (5) partitioning the power grid. According to a power grid topological structure, a power grid node voltage-reactive sensitivity matrix is established, and a double-layer clustering method is established by combining the pearson coefficient and the Euclidean distance to perform power grid reactive optimization partition;
(3) Dynamic reactive day-ahead optimization. Establishing a dynamic reactive power optimization model based on load segmentation, wherein the active network loss, the voltage offset and the equipment action cost of the model are used as objective functions, discrete equipment action frequency constraint and the like are comprehensively considered, and an improved balance optimizer algorithm is utilized to carry out model solution so as to obtain a daily power grid reactive power optimization control scheme;
(4) And (5) real-time reactive voltage control. And monitoring the operation data of the power grid in real time, wherein the operation data comprises voltage and power flow, and carrying out reactive voltage optimization control correction according to the actual voltage, flow and load deviation conditions to obtain a reactive voltage control scheme of the power grid based on the real-time operation data.
2. The method for dynamic optimization control of reactive voltage of urban power network based on zone control according to claim 1, wherein the future operation state of the power network in step (1) is predicted. Removing data of the fault condition of the power grid by taking the day as a unit, and combining daily active consumption in the past 30 days into a combined set Z= { W 1 ,W 2 ,...W 30 Predicting the change condition of the active consumption of the next incoming call network by a data fitting mode; considering that the urban power network has obvious high and low peaks, large load fluctuation and stable period, the active power consumption in different periods is affected by weather and other factors, and the active power consumption W of each day is divided into periods according to peak-valley electricity prices i Is divided into a high peak section, a normal section and a low peak section (w i,1 ,w i,2 ,w i,3 I=1, ··, 30), and respectively carrying out the prediction method for predicting the change condition of the active consumption through data fitting.
3. The method for dynamic optimization control of reactive voltage of urban power network based on zone control according to claim 1, wherein the reactive-voltage sensitivity matrix in step (2) is solved. The method comprises the steps of setting N load nodes to be partitioned in a power grid to form a set N, setting M reactive power sources and nodes with reactive power regulation capability to form a set M. The construction of the reactive-voltage sensitivity matrix comprises the following steps:
(1) Calculating to obtain daily average power of each node of the power grid by using historical power data of the power grid, and performing reactive power optimization by improving a balance optimizer algorithm to obtain compensation values (q j J=1, 2,..m), and the voltage of the node in N (v i ,i＝1,2,...n)。
(2) For the j-th reactive node belonging to M, setting the rest nodes in M as PQ nodes, and adding correction quantity delta q to the reactive compensation value of the PQ nodes:
q′ j ＝q j +Δq (1)
wherein: q's' j And q j The reactive power compensation quantity after correction and before correction is obtained; Δq is the reactive compensation amount correction value.
The reactive compensation values of the other reactive sources remain unchanged. Obtaining the voltage (v) of each node in N through load flow calculation i,j I=1, 2, n), and v i The difference is made to obtain a voltage difference:
Δv i,j ＝v i,j -v i (2)
wherein: v i,j The voltage of the load node i is increased under the condition that delta q is added to the compensation value of the reactive power source j; v i Representing an initial voltage value of the load node i; deltav i,j And adding delta q to the compensation value of the reactive power source j for the load node i, and then adding the node voltage change value.
Let reactive-voltage sensitivity be S i,j The reactive-voltage sensitivity matrix S is constructed, defined as:
wherein: s is S i,j Representing the voltage sensitivity of the load node i to the reactive power source j; deltav i,j Adding delta q to the reactive power source j compensation value of the load node i, and then adding a node voltage change value; Δq is the reactive compensation variation.
4. The method for dynamic optimization control of reactive voltage of an urban power grid based on zone control according to claim 1, wherein the double-layer clustering based on the voltage sensitivity matrix in the step (2) comprises the following steps:
(1) And reading voltage sensitivity matrix data, wherein each row represents the voltage sensitivity of the load node to be classified to each reactive power source, and simultaneously reflects the influence capability of the reactive power source to each node, the voltage sensitivity is used as an object cluster, and the reactive power source nodes are not positioned at the edge of the partition. Therefore, the reactive power source nodes do not participate in clustering, and the partitions are planned according to the principle of nearby after the load nodes are clustered.
(2) The first layer of clusters comprises Kmeans clusters based on similarity measurement of numerical distance and Kmeans clusters based on similarity measurement of morphological characteristics. The numerical distance is European distance d m,x Morphology features adopt Pelson coefficient rho m,x ：
Wherein: s is S i,j Representing the voltage sensitivity of the load node i to the reactive power source j; s is S x,i Representing the voltage sensitivity of the reactive source i in the center point x;represent S i Are all of (1)A value;Represent S x Is the average value of (2); j is the total number of reactive sources.
For n load nodes to be partitioned, randomly selecting k samples as a center point, where k is not greater thanIs the largest integer of (2); calculating the similarity between each sample and the center point, and clustering d based on Euclidean distance by taking the highest similarity as a clustering basis m,x Small similarity high, p in pearson coefficient-based clusters m,x The large similarity is high; judging whether the clustering result is consistent with the last time, if so, outputting the clustering result, otherwise, recalculating the similarity and classifying by taking the member mean value in the cluster as a central value until the clustering result is unchanged, and obtaining a clustering result p.
The clustering result is biased each time due to the randomness of the initial center point selection. According to the invention, randomness is fully reflected by carrying out multiple clustering, and 15 times of kmeans clustering based on Euclidean distance and 15 times of kmeans clustering based on Pelson coefficient are carried out.
Calculating the similarity between each sample in the cluster and the center point, and d in the cluster based on Euclidean distance m,x Small similarity high, p in pearson coefficient-based clusters m,x The large similarity is high, and the sum of the similarity of all samples is the similarity index of the clustering result:
d all ＝∑d i (7)
ρ all ＝∑ρ i (8)
wherein: d, d all Is a similarity index based on Euclidean distance clustering results; ρ all Is a similarity index based on the pearson coefficient clustering result; d, d i The Euclidean distance between the sample i and the center point of the sample i; ρ i Is the pearson coefficient for sample i and its center point.
5 clustering results with lower similarity are removed by the two clustering modes, and the rest clustering results form a clustering result set P= (P) 1 ,p 2 ,...p 20 )。
(2) And (5) second-layer clustering, and selecting base clustering. The basic clustering is used as the basis of subsequent classification, so that the advantages of two clustering indexes are fully combined, the clustering quality is improved, k samples serving as the basic clustering have enough differences, and have enough similarity with other samples, and the similarity index d (p i ,p j ) And similarity index D (p i ,P)：
Wherein:representing the cluster p i Data S of (B) a A dataset of the cluster;Representing the cluster p j Data S of (B) a A dataset of the cluster;Representing the cluster p i Data S of (B) a Belonging clusters and cluster p j Data S of (B) a The number of data of the cluster intersection to which the cluster belongs; |p| represents the data amount of the collection P; d (p) i ,p j ) Represents p i And p is as follows j Similarity between the values of 0,1]The larger the value, the more the cluster p i And p is as follows j The higher the similarity is, the square can effectively reduce the value of the similarity when the difference is larger; d (p) i P) represents the cluster P i Similarity with clusters contained in cluster set P, value range [0 ]，1]The larger the number, the higher the similarity.
With D (p) i P) selecting one sample P for index selection i As a first basis cluster, p i Should be sufficiently similar to the rest of the sample, D (p i P) the largest one as the first base cluster. For the rest of the base clusters, the similarity index D (p) is further defined by requiring enough similarity with the rest of the samples and enough difference with the selected base clusters i ,P,P′)：
Wherein: p is a cluster set comprising unselected clusters and selected clusters; p' is a collection of clusters that have been selected as base clusters.
With D (p) i Samples are selected as basic clusters by using the maximum of P and P' as indexes, and D (P) is recalculated after each selection i P, P') and select new samples until there are k base clusters.
(3) Second-layer clustering, construction data S i And S is equal to j And (5) an inter-similarity matrix A. Based on S i And S is equal to j Similarity under classification mode of different base clusters, definition similarity is a ij ：
Wherein:representation data S i Clustering p 'with basis' η A data set which belongs to a cluster in a classification mode; x-shaped articles η (S i ,S j ) Reaction data S i And S is equal to j Clustering p 'on basis' η Whether belonging to the same cluster in the classification of (a); a, a ij Reaction data S i And S is equal to j Clustering p 'on basis' η Similarity in classification, value range [0,1]The larger the number is, the higher the similarity is, and the presence of square can effectively cut down the value of the similarity when the difference is large.
(4) And (5) second-layer clustering, and hierarchical aggregation clustering. And regarding each row of the matrix A as a sample, and performing hierarchical aggregation clustering by taking Euclidean distance as an index. Each sample is regarded as one cluster, euclidean distances among different clusters are calculated to merge adjacent clusters until the number of the clusters reaches k.
5. The method for dynamic optimization control of reactive voltage of urban power network based on zone control according to claim 1, wherein the dynamic reactive power optimization model in step (2) is described.
And (3) load flow constraint:
wherein: p (P) Gi And P Li Representing the active power of the motor and the load; q (Q) Gi And Q is equal to Li Representing reactive power of the motor and the load; q (Q) Ci Representing reactive power of the reactive source; u (U) i And U j Representing the voltage magnitudes of nodes i and j; θ ij Representing the phase angle difference of nodes i and j.
Voltage constraint:
U imin ≤U i ≤U imax (17)
wherein: u (U) imin And U imax Representing the lower limit and the upper limit of the voltage constraint of the node i; u (U) i Representing the voltage magnitude at node i.
Reactive power source output constraint:
wherein: q (Q) MCR And Q is equal to L Reactive power provided by MCR and reactance, respectively; q (Q) MCR min And Q is equal to MCR max Providing reactive upper and lower limits for MCR; k (k) L The reactance switching quantity is discrete value, k Lmin And k is equal to Lmax The upper limit and the lower limit of the switching quantity are adopted.
And (3) limiting the switching speed of the reactor group:
|k L (t)-k L (t+1)|≤Δk max (19)
wherein: k (k) L (t) and k L The (t+1) represents the switching quantity of the reactors in the t period and the t+1 period, and the switching quantity in the unit period is limited because the switching of the reactors can cause power grid fluctuation; Δk max Representing the maximum value of the number of switching in a unit time period.
Substation transformation ratio is limited:
wherein: t is the transformer transformation ratio; k (k) T A variable tap position; delta T min The variable quantity of the gear ratio between different gears is obtained; k (k) Tmin And k is equal to Tmax The lower limit and the upper limit of the tap gear are respectively defined.
Reactive power all-day dispatch and tap shift limits:
wherein: q (Q) max And k is equal to max Respectively representing the schedulable amounts of the MCR and the reactor in one day; t (T) max The frequency of shifting the transformer tap in one day is represented; ΔQ max (t)、Δk max (T) and DeltaT max And (t) respectively representing the maximum dispatching values of the MCR, the reactor group and the transformer substation in the t period, and determining according to the total power difference of the power grid in the front period and the rear period in the predicted power, wherein the definition is as follows:
the objective function comprises minimum active loss, minimum voltage deviation, minimum reactive power dispatching cost and minimum transformer dispatching cost.
Active loss P loss Minimum:
wherein: n is the number of load nodes, m is the number of reactive power sources, and n+m represents the total number of nodes of the power grid; g ij Representing the conductance between node i and node j; representing the phase difference θ between node i and node j ij 。
Voltage deviation U d Minimum:
wherein: u (U) i Is the voltage of the node i; u' is the target voltage of the node i; n+m is the total number of nodes.
Reactive compensationCompensation quantity Q all Minimum:
wherein: q (Q) MCRi (t) represents the reactive compensation amount of the MCR at time t; k (k) Li (t) represents the switching number of the reactor at the moment t; ΔQ Lmin The reactive compensation difference value of the primary switching of the reactor is represented; m and n are the total number of MCR and the total number of reactors, respectively.
Tap position change T of transformer substation all Minimum:
wherein: t (T) all The sum of the tap shift numbers of the transformer; k (k) Ti (t) is tap position of the transformer i at t moment; y is the total number of transformers.
Consider that the reactor group and the transformer have minimum switching quantity delta Q Lmin With minimum transformation ratio delta T min Is limited to min Q all Discretizing the reactive compensation value of the reactor group:
wherein: k' L And k is equal to L The switching numbers after discretization and before discretization of the reactor are respectively; k (k) Li For a certain switching number of the reactor, the switching number is larger than k L Is the smallest integer of (a); k' T And k is equal to T The gear positions after and before the discrete of the transformer tap are respectively; k (k) Ti For a certain gear, is greater than k T Is the smallest integer of (a); η (eta) Q The discrete range was defined and taken to be 0.5.
The method processes the problem that the dimensions and orders of magnitude are different between multiple objective functions by normalizing and establishing satisfaction functions:
wherein: f (f) i F is the normalized objective function 1 Is P loss Normalized to f 2 Is U (U) d Normalized to f 3 Is Q all Normalized to f 4 Is T all Normalizing; f is the original objective function value; f (f) max Is an unoptimized function value; f (f) min To consider only the objective function value under the corresponding objective. Satisfaction function value range [0,1 ]]Smaller values indicate higher satisfaction.
Adding weight coefficients to satisfaction functions of different targets, and comprehensively defining an objective function F:
wherein: weight coefficientDetermined by analytic hierarchy process.
Definition f i The ratio of the importance of eachConstructing a judgment matrix A:
normalizing A by column, summing by row, and normalizing again:
obtaining weight coefficient
6. The method for dynamic optimization control of reactive voltage of a municipal power grid based on zone control according to claim 1, wherein the improved balance optimizer algorithm in step (3) comprises the following important parameters:
five elite leadership balancing pool:
C eq,pool ＝{C eq(1) ,C eq(2) ,C eq(3) ,C eq(4) ,C eq(ave) } (39)
wherein: c (C) eq(1) To C eq(4) For the optimal four solutions in the iteration, C eq(ave) Is the mean of these four solutions.
The local and global searching capability of the algorithm is comprehensively considered, and the F coefficient is improved:
wherein: a, a 1 Is constant 2; sign is a sign taking function; r and lambda are [0,1 ]]Random values in between; iter and Maxiter represent the current iteration number and the maximum iteration number.
Mass generation rate G:
G＝G cp F(C eq -λC) (41)
wherein: r is (r) 1 And r 2 w is [0,1 ]]Random values in between; GP is constant 0.5.
According to the key parameters, the final iteration formula is as follows:
wherein: c (C) eq Is a member of the five elite equilibrium pool.
7. The method for dynamic optimization control of reactive voltage of an urban power network based on zone control according to claim 1, wherein the real-time reactive voltage control stage in step (4) comprises the following steps:
(1) In the real-time reactive voltage control stage, the voltage of each node is monitored in real time, and whether the situation of overlarge voltage deviation exists or not is judged every 20 minutes, so that the target voltage u i And the actual voltage u' i The relationship should be:
wherein: u's' i The actual voltage of the node i; u (u) i Target voltage for node i; epsilon min And epsilon max The lower and upper limits of the relative deviation, respectively.
If at iThe Duan Mou node voltage deviation is overlarge, the partition to which the node belongs is judged, and the actual power condition (p 'of the node in the period is obtained' i ,q′ i ) And under the condition of controlling only the regional reactive power source, carrying out reactive power optimization calculation under the actual power condition. And simultaneously, the later prediction data is adjusted, and the predicted power of all nodes in the voltage deviation overlarge area is corrected:
(2) And (3) recalculating the reactive power configuration scheme after the period i, and subtracting the scheduling limit consumed in the voltage out-of-limit condition from the subsequent reactive power scheduling limit.
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| CN118367563A (en) * | 2024-06-19 | 2024-07-19 | 国网吉林省电力有限公司长春供电公司 | Low-voltage power cooperative control system in electric power distribution network |
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| CN118367563A (en) * | 2024-06-19 | 2024-07-19 | 国网吉林省电力有限公司长春供电公司 | Low-voltage power cooperative control system in electric power distribution network |
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