Power grid partitioning method under high-proportion new energy access background

Abstract

The invention provides a power grid partitioning method under a high-proportion new energy access background, and the method comprises the following steps: S1, giving an electrical distance index from the two perspectives of line power flow and line impedance, and constructing an electrical distance matrix; s2, a correction coefficient is defined based on the new energy output typical scene set, the electrical distance matrix is corrected, and a full-dimensional electrical distance matrix is obtained; and S3, based on the electrical distance matrix, performing power grid partitioning by adopting a clustering algorithm. The method is of great significance in improving the capacity of the power grid to deal with the uncertainty of new energy output and the new energy consumption level.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a power grid partitioning method under the background of high-proportion new energy access. Background Art

[0002] With the development of the national economy, the social electricity demand has increased year by year. The voltage level of the power grid has gradually increased under the background that the original grid structure cannot meet the load transmission requirements. However, in the early days of the high-voltage grid, its structure was not perfect. In order to ensure the reliability of power supply, it was necessary to run in parallel with the low-voltage grid, and the electromagnetic ring network was thus created. With the improvement of the grid structure, the shortcomings of the electromagnetic ring network have become increasingly apparent. Therefore, untying the electromagnetic ring network and realizing the hierarchical and zoned operation of the power grid is the development trend of the future power grid.

[0003] At present, most of the research work on power grid zoning methods at home and abroad mainly focuses on two approaches: artificial experience and intelligent algorithms. On the one hand, the artificial experience method usually involves dispatchers disconnecting certain lines based on factors such as geographical location, regional division, and operating experience to complete the grid zoning. The zoning process relies too much on the subjective experience and technical level of the operators, ignoring the overall grid structure and operating conditions of the power grid, which may have an adverse impact on the power supply security of the power grid; on the other hand, the zoning method based on intelligent algorithms applies the community discovery algorithm in complex network theory to the study of power grid zoning, overcoming the shortcomings of the open-loop zoning scheme generation method, which is highly subjective, and provides a theoretical basis for zoning. Affected by the randomness, volatility, and intermittency of the output of new energy such as wind power and photovoltaics, the power system flow presents new characteristics of frequent changes and large fluctuations. Therefore, the traditional zoning scheme that ignores the high proportion of new energy access is no longer applicable, and cannot effectively deal with the uncertainty of new energy output and further improve the level of new energy consumption. Summary of the invention

[0004] The present invention proposes a grid partitioning method under the background of high proportion of new energy access, so as to solve the problem that the existing scheme is not applicable to the grid partitioning under the background of high proportion of new energy access.

[0005] In order to solve the above technical problems, the present invention provides a method for grid partitioning in the context of high-proportion new energy access, comprising the following steps:

[0006] Step S1: Provide electrical distance indicators from the perspectives of line flow and line impedance, and construct an electrical distance matrix;

[0007] Step S2: defining a correction coefficient based on a set of typical scenarios of new energy output, and correcting the electrical distance matrix to obtain a full-dimensional electrical distance matrix;

[0008] Step S3: Based on the electrical distance matrix, a clustering algorithm is used to partition the power grid.

[0009] Preferably, the electrical distance matrix d ij The expression is:

[0010]

[0011] Where, d ij is the electrical distance between nodes i and j; z ij is the impedance of the line between nodes i and j; P ij is the active power flowing through the line between nodes i and j; P ij,max is the maximum active power that can be transmitted between i and j; γ ij is the power flow coefficient of line ij; α is the weight.

[0012] Preferably, step S2 comprises the following steps:

[0013] Step S211: Establish a power matrix under a typical scenario set of new energy output: P (k) is the power matrix of the system line in the kth scenario; P i (k) is the power of the ith line in the kth scenario; m is the total number of lines;

[0014] Step S212: constructing a correction coefficient based on the scene probability;

[0015] Step S213: using the correction coefficient to correct the electrical distance matrix to construct a full-dimensional electrical distance matrix.

[0016] Preferably, in step S212, the correction coefficient The expression is:

[0017]

[0018] In the formula, is the correction coefficient for the kth scenario; r represents the number of typical scenarios; p(k) represents the probability of the kth scenario.

[0019] Preferably, step S213 includes:

[0020] Step S2131: using the correction coefficient to correct the adjacent nodes in the electrical distance matrix:

[0021]

[0022] Step S2132: for two non-adjacent nodes, find the shortest path between the two nodes based on the Dijkstra algorithm, and then accumulate the electrical distances of the connection paths to obtain the electrical distance between any two nodes;

[0023] Step S2133: construct a full-dimensional electrical distance matrix S through the electrical distances of adjacent nodes in step S231 and the electrical distances of non-adjacent nodes in step S232.

[0024] Preferably, step S3 comprises the following steps:

[0025] Step S31: generating a similarity matrix based on the full-dimensional electrical distance matrix S of the power network;

[0026] Step S32: Initialize the attraction matrix R and the attribution matrix A of the AP clustering algorithm, and set reference values;

[0027] Step S33: Update R, A and the similarity matrix to obtain the cluster center, and assign the nodes to each cluster;

[0028] Step S34: Change the reference value of step S32, repeat steps S32 to S33, until the number of cluster centers outputted reaches the expected value, and obtain the partitioning scheme.

[0029] Preferably, the expression updated in step S33 is:

[0030] R t+1 (i,j)=(1-λ)·R t ' +1 (i,j)+λ·R t (i,j);

[0031]

[0032] A t+1 (i,j)=(1-λ)·A t ' +1 (i,j)+λ·A t (i,j);

[0033]

[0034] In the formula, R t (i, j) represents the attraction between node i and node j in the tth iteration; R t ' +1 (i, j) represents the attraction value after the tth iteration; A t (i,j) represents the degree of belonging of node i and node j in the tth iteration; A t ' +1 (i, j) represents the belonging degree value after the tth iteration; λ is the damping coefficient, ranging from 0 to 1.

[0035] Preferably, the new energy output typical scenario set in step S2 is constructed by the following method:

[0036] Step S221: Generate a new source energy output scenario set using an autoregressive moving average model;

[0037] Step S222: Initialize the probability of the scene set;

[0038] Step S223: Select scenes that meet the Kantorovich distance formula conditions for reduction, and change the scene probability;

[0039] Step S224: Repeat step S223 until the number of scenes in the scene set reaches a set number.

[0040] Preferably, the expression of step S221 is:

[0041]

[0042] In the formula, y t is the time series value at time t; φ i is the autoregressive parameter; α t is a normal white noise process with a mean of 0 and a variance of σ 2 θ j is the sliding average parameter; a and b represent the model order.

[0043] Preferably, in step S223, the expression of the Kantorovich distance formula is:

[0044]

[0045] In the formula, C i Indicates the scene set to be cut; C i ′ represents the deleted scene set; d(sv,sv′) is the scene s v and v ′Euclidean distance; η(s v ,s v ′) is the scene s v and v ′ probability product; p sv and p s′v The scenes are v and v ′ in C i and C i The probability in ′.

[0046] The beneficial effects of the present invention include at least: the present invention proposes a grid partitioning method under the background of high-proportion new energy access, improves the calculation method of electrical distance index from the two perspectives of line flow and line impedance, and corrects the electrical distance matrix based on the typical scenario set of new energy output, so that the partitioning scheme is applicable to the partitioning problem of a grid containing a high proportion of renewable energy. This method is of great significance for improving the ability of the grid to cope with the uncertainty of new energy output and the level of new energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the partitioning result under Solution 1 of the embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the partitioning result under Solution 2 of the embodiment of the present invention;

[0050] Figure 4 A schematic diagram of a system topology structure according to an embodiment of the present invention;

[0051] Figure 5 This is the wind power and photovoltaic output scenario before reduction in an embodiment of the present invention;

[0052] Figure 6 This is a scenario of wind power and photovoltaic output after reduction in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, an embodiment of the present invention provides a method for grid partitioning in the context of high-proportion new energy access, comprising the following steps:

[0056] Step S1: Provide electrical distance indicators from the perspectives of line flow and line impedance, and construct an electrical distance matrix.

[0057] Specifically, this embodiment provides an electrical distance index from two perspectives: line flow and line impedance. The electrical distance index simultaneously considers the impact of power grid line flow and impedance on power grid partitioning, as shown in formula (1).

[0058]

[0059] In the above formula, z ij is the impedance of the line between nodes i and j; P ij is the active power flowing through the line between nodes i and j, P ij,max is the maximum active power that can be transmitted between i and j; γ ij is the power flow coefficient of line ij; d ij is the electrical distance between nodes i and j; α is the weight.

[0060] Step S2: Based on the typical scenario set of new energy output, a correction coefficient is defined, and the electrical distance matrix is ​​corrected to obtain a full-dimensional electrical distance matrix.

[0061] Specifically, the correction coefficient is defined based on the typical scenario set of new energy output, as shown in equations (2) and (3):

[0062]

[0063] In the above formula, P (k) is the power matrix of the system line in the kth scenario; P i (k) is the power of the ith line in the kth scenario; m is the total number of lines.

[0064]

[0065] In the above formula, r is the number of typical scenes generated. is the correction coefficient for the kth scenario; p(k) represents the probability of the kth scenario.

[0066] In this embodiment, the electrical distance matrix of adjacent nodes is firstly corrected, as shown in formula (4).

[0067]

[0068] For any two non-adjacent nodes, the shortest path between the two nodes is found based on the Dijkstra algorithm, and then the electrical distances of the connection paths are accumulated to obtain the electrical distance between any two nodes, which are then merged to obtain the full-dimensional electrical distance matrix.

[0069] Modifying the electrical distance matrix before expanding it to the full-dimensional electrical distance matrix and directly considering the impact of new energy output on electrical connections is conducive to more flexible response to the highly volatile characteristics of new energy and makes the modification process more targeted without causing the new energy output characteristics to be changed or diluted during the modification process of the full-dimensional electrical distance matrix.

[0070] Step S3: Based on the electrical distance matrix, a clustering algorithm is used to partition the power grid.

[0071] Specifically, the update formulas of the attraction matrix R and the attribution matrix A of the AP clustering algorithm in the power grid partition model based on the AP clustering algorithm in the embodiment of the present invention are shown in formulas (5) to (8).

[0072] R t+1 (i,j)=(1-λ)·R t ' +1 (i,j)+λ·R t (i,j)(5)

[0073]

[0074] A t+1 (i,j)=(1-λ)·A t ' +1 (i,j)+λ·A t (i,j) (7)

[0075]

[0076] In the above formula, R t (i, k) represents the attraction between node i and node j in the tth iteration; R t ' +1 (i, j) represents the attraction value after the tth iteration; A t (i,j) represents the degree of belonging of node i and node j in the tth iteration; A t ' +1 (i, j) represents the belonging degree value after the tth iteration; λ is the damping coefficient, ranging from 0 to 1, which is used for the convergence of the algorithm.

[0077] Compared with other clustering algorithms, the AP clustering algorithm used in this embodiment does not require manual determination of cluster centers, but allocates the number of cluster centers based on the similarity between nodes, does not rely on the setting of initial parameters, and is objective. The power grid partitioning process based on the AP clustering algorithm is as follows:

[0078] 1) Generate a similarity matrix based on the full-dimensional electrical distance matrix S of the power network;

[0079] 2) Initialize the attraction matrix A and the belonging matrix R, and set the initial reference value;

[0080] 3) S, A and R are iteratively updated according to equations (5) to (8);

[0081] 4) Get the cluster center and assign other nodes to each cluster;

[0082] 5) Change the reference value and return to step 2) until the number of cluster centers outputted reaches the expected value;

[0083] 6) Get the final partitioning scheme.

[0084] Example 2

[0085] This embodiment adopts the method of embodiment 1 and analyzes two solutions. The solutions include: no new energy in the system and new energy in the system. Figure 2 and Figure 3 They are the partitioning results under the two schemes respectively. Based on step S1 and step S2, the full-dimensional electrical distance matrix can be calculated. Table 1 is a partial numerical comparison of the full-dimensional electrical distance matrix under the two schemes.

[0086] Table 1

[0087]

[0088] Comparing the partitioning results, it can be seen that the electrical distance matrix of the system has changed significantly before and after the access of new energy. Specifically, the electrical distance between some nodes may increase or decrease. This change is due to the volatility and uncertainty brought by the access of new energy. For example, D(3,18), D(27,17) and D(9,39) in Table 1 become smaller after the access of new energy to the system, while D(25,26), D(23,24) and D(14,15) become smaller. This shows that the volatility and uncertainty of new energy have changed the inherent characteristics of the system. It can be seen that it is very important to comprehensively consider the characteristics of new energy when partitioning, otherwise it may make the partitioning scheme difficult to adapt to the actual operation needs.

[0089] By partition result Figure 2 and Figure 3 It can be seen that the scale of the partitions has changed significantly before and after the access of new energy. Before the access of new energy, the system was divided into 4 partitions, but after the access of new energy, it was divided into only 3 partitions. This change is mainly attributed to the volatility characteristics of new energy, which increases the complexity and uncertainty of power grid operation, making it difficult for the original partitions to meet the balance requirements under the new energy scenario. Specifically, when the partition size is too small, the power fluctuations in the area are difficult to smooth out and the system stability is reduced. Therefore, after the access of new energy, the partition size has expanded, while the number of partitions has decreased, reflecting the adaptive optimization of the system in the partition adjustment.

[0090] For region 2, the electrical distance between nodes 9 and 39 was originally large, and the electrical connection was weak, so they were assigned to different partitions. However, after the access to new energy, the electrical distance between nodes 9 and 39 was significantly reduced, indicating that the electrical connection between them was enhanced. Therefore, node 39 was included in region 2 in the partition after the access to new energy, resulting in a significant expansion of the scale of region 2. This adjustment not only reflects the reshaping of electrical connections by new energy, but also shows that the zoning scheme can dynamically adapt to system changes when considering the impact of new energy.

[0091] For Region 1, after access to new energy, Region 1 was merged with Region 3, and some nodes originally belonging to Region 4 were incorporated. The main reason for this adjustment is the concentrated distribution of new energy sites in Region 1, including 2 photovoltaic power stations and 2 wind farms. The output characteristics of new energy sites increase the complexity of power fluctuations in the area, so it is necessary to balance power supply and demand by expanding the regional scale and enhance the stability within the region.

[0092] For Region 3, the original Region 4 was re-divided into the new Region 3 after the access of new energy, and the regional scale was reduced. The new Region 3 does not include new energy stations, indicating that it bears a lower volatility risk in the new zoning scheme and is suitable as a more stable area in the system.

[0093] According to the above simulation calculation results, the effectiveness of the power grid partitioning scheme generation method under the background of high proportion of new energy access in the present invention is verified.

[0094] Example 3

[0095] This embodiment provides a method for constructing a set of typical scenarios for new energy output.

[0096] This embodiment takes the improved IEEE39 node system as the research object, and the new energy access points and topology structure are as follows: Figure 4 As shown. First, the autoregressive moving average model is used to generate the wind and solar output sampling scenarios, as shown in formula (9). 200 wind power and photovoltaic output scenarios are generated, as shown in Figure 5 shown.

[0097]

[0098] In the above formula, y t is the time series value at time t; φ i is the autoregressive parameter; α t is a normal white noise process with a mean of 0 and a variance of σ 2 θ j is the sliding average parameter.

[0099] Then, the scene set is reduced. That is, in the scene reduction part, the synchronous back-substitution method is used to further reduce the scene, and the final typical scene set represents the initial scene set. The key to implementing the synchronous back-substitution method is the selection of probability distance. The scene reduction is based on the Kantorovich distance, as shown in formula (10):

[0100]

[0101] In the above formula, C i is the set of cut scenes, Ci ′ is the deleted scene set; d(sv,sv′) is the scene s v and v ′Euclidean distance; η(s v ,s v ′) is the scene s v and v ′ probability product; p sv and p s′v The scenes are v and v ′ in C i and C i The probability in ′.

[0102] The specific steps for scene reduction are as follows:

[0103] 1) Using C i N i A collection of scenes to be cut, using C i ′ represents N i ′ sets of deleted scenes and initialize the probability of each scene in the two scene sets.

[0104] 2) Each time, select a scene s that satisfies the Kantorovich distance formula v and v ′, and s v ′From C i Remove from C and add to C i 'middle.

[0105] 3) Update C i and C i The number of scenes in ′, N i =N i -1, N i ′=N i ′-1; and change the scene s v The probability of

[0106] 4) Repeat the above steps until the number of scenes in the retained scene set reaches the set number.

[0107] The wind power and photovoltaic scenarios after reduction are as follows Figure 6 The probabilities of wind power and photovoltaic output scenarios are shown in Tables 2 and 3.

[0108] Table 1: Probability of typical wind power output scenarios

[0109] Scenario Probability Scenario Probability Scenario Probability Scenario 1 0.005 Scene 5 0.090 Scene 9 0.005 Scenario 2 0.110 Scene 6 0.070 Scene 10 0.140 Scene 3 0.005 Scene 7 0.085 Scene 11 0.275 Scene 4 0.110 Scene 8 0.005 Scene 12 0.100

[0110] Table 2: Probability of typical photovoltaic output scenarios

[0111]

[0112]

[0113] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0114] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for grid partitioning under the background of high proportion of new energy access, characterized by: The following steps are involved: Step S1: Provide electrical distance indicators from the perspectives of line flow and line impedance, and construct an electrical distance matrix; Step S2: defining a correction coefficient based on a set of typical scenarios of new energy output, and correcting the electrical distance matrix to obtain a full-dimensional electrical distance matrix; Step S3: Based on the electrical distance matrix, a clustering algorithm is used to partition the power grid.

2. A method for grid partitioning under the background of high proportion of new energy access according to claim 1, characterized in that: The electrical distance matrix d ij The expression is: Where, d ij is the electrical distance between nodes i and j; z ij is the impedance of the line between nodes i and j; P ij is the active power flowing through the line between nodes i and j; P ij,max is the maximum active power that can be transmitted between i and j; γ ij is the power flow coefficient of line ij; α is the weight.

3. The method for grid partitioning under the background of high proportion of new energy access according to claim 1 is characterized in that: Step S2 includes the following steps: Step S211: Establish a power matrix under a typical scenario set of new energy output: P (k) is the power matrix of the system line in the kth scenario; P i (k) is the power of the ith line in the kth scenario; m is the total number of lines; Step S212: constructing a correction coefficient based on the scene probability; Step S213: using the correction coefficient to correct the electrical distance matrix to construct a full-dimensional electrical distance matrix.

4. The method for grid partitioning in the context of high-proportion new energy access according to claim 3 is characterized in that: In step S212, the correction coefficient σ i (k) The expression is: In the formula, is the correction coefficient for the kth scenario; r represents the number of typical scenarios; p(k) represents the probability of the kth scenario.

5. The method for grid partitioning under the background of high proportion of new energy access according to claim 4 is characterized in that: Step S213 includes: Step S2131: using the correction coefficient to correct the adjacent nodes in the electrical distance matrix: Step S2132: for two non-adjacent nodes, find the shortest path between the two nodes based on the Dijkstra algorithm, and then accumulate the electrical distances of the connection paths to obtain the electrical distance between any two nodes; Step S2133: construct a full-dimensional electrical distance matrix S through the electrical distances of adjacent nodes in step S231 and the electrical distances of non-adjacent nodes in step S232.

6. The method for grid partitioning under the background of high proportion of new energy access according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: generating a similarity matrix based on the full-dimensional electrical distance matrix S of the power network; Step S32: Initialize the attraction matrix R and the attribution matrix A of the AP clustering algorithm, and set reference values; Step S33: Update R, A and the similarity matrix to obtain the cluster center, and assign the nodes to each cluster; Step S34: Change the reference value of step S32, repeat steps S32 to S33, until the number of cluster centers outputted reaches the expected value, and obtain the partitioning scheme.

7. A method for grid partitioning in the context of high proportion of new energy access according to claim 6, characterized in that: The expression updated in step S33 is: R t+1 (i,j)=(1-λ)·R t ′ +1 (i,j)+λ·R t (i,j); A t+1 (i,j)=(1-λ)·A t ′ +1 (i,j)+λ·A t (i,j); In the formula, R t (i, j) represents the attraction between node i and node j in the tth iteration; R t ' +1 (i, j) represents the attraction value after the tth iteration; A t (i, j) represents the degree of belonging of node i and node j in the tth iteration; A t ' +1 (i, j) represents the belonging degree value after the tth iteration; λ is the damping coefficient, ranging from 0 to 1.

8. The method for grid partitioning in the context of high-proportion new energy access according to claim 1 is characterized in that: In step S2, the typical scenario set of new energy output is constructed by the following method: Step S221: Generate a new source energy output scenario set using an autoregressive moving average model; Step S222: Initialize the probability of the scene set; Step S223: Select scenes that meet the Kantorovich distance formula conditions for reduction, and change the scene probability; Step S224: Repeat step S223 until the number of scenes in the scene set reaches a set number.

9. A method for grid partitioning in the context of high proportion of new energy access according to claim 8, characterized in that: The expression of step S221 is: In the formula, y t is the time series value at time t; φ i is the autoregressive parameter; α t is a normal white noise process with a mean of 0 and a variance of σ 2 θ j is the sliding average parameter; a and b represent the model order.

10. A method for grid partitioning in the context of high-proportion new energy access according to claim 8, characterized in that: In step S223, the expression of the Kantorovich distance formula is: In the formula, C i Indicates the scene set to be cut; C i ′ represents the deleted scene set; d(sv,sv′) is the scene s v and v ′Euclidean distance; η(s v ,s v ′) is the scene s v and v The probability product of ′; and The scenes are v and v ′ in C i and C i The probability in ′.

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