A power grid partitioning method under high proportion of new energy access

Abstract

This invention provides a power grid partitioning method under the background of high proportion of renewable energy access, including the following steps: Step S1: Based on the perspectives of line power flow and line impedance, an electrical distance index is given, and an electrical distance matrix is ​​constructed; Step S2: Based on a typical renewable energy output scenario set, a correction coefficient is defined to correct the electrical distance matrix, resulting in a full-dimensional electrical distance matrix; Step S3: Based on the electrical distance matrix, a clustering algorithm is used to partition the power grid. This method is of great significance for improving the power grid's ability to cope with the uncertainty of renewable energy output and the level of renewable energy absorption.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and specifically to a power grid zoning method under the background of high proportion of new energy access. Background Technology

[0002] With the development of the national economy, the demand for electricity has increased year by year, and the voltage level of the power grid has gradually increased as the original grid structure could no longer meet the load transmission requirements. However, in the early stages of the high-voltage grid, its structure was not perfect. To ensure power supply reliability, it needed to operate in parallel with the low-voltage grid, thus creating the electromagnetic loop network. As the grid structure has improved, the shortcomings of the electromagnetic loop network have become increasingly apparent. Therefore, breaking the electromagnetic loop network and realizing the layered and zoned operation of the power grid is the future development trend of the power grid.

[0003] Currently, most research on power grid partitioning methods, both domestically and internationally, focuses on two main approaches: manual experience and intelligent algorithms. On the one hand, manual experience-based methods typically involve dispatchers disconnecting certain lines based on factors such as geographical location, regional division, and operational experience to partition the power grid. This partitioning process relies heavily on the subjective experience and technical skills of individual operators, neglecting the overall grid structure and operational status, which may negatively impact the power supply security of the grid. On the other hand, partitioning methods based on intelligent algorithms apply community detection algorithms from complex network theory to power grid partitioning research, overcoming the subjectivity inherent in open-loop partitioning scheme generation methods and providing a theoretical basis for partitioning. Due to the randomness, volatility, and intermittency of renewable energy output such as wind and solar power, power system flow exhibits new characteristics of frequent and significant fluctuations. Therefore, traditional partitioning schemes that ignore high-proportion renewable energy integration are no longer applicable, failing to effectively address the uncertainty of renewable energy output and further improve the level of renewable energy absorption. Summary of the Invention

[0004] This invention proposes a grid partitioning method under the background of high proportion of new energy access, so as to solve the problem that existing solutions cannot be applied to the grid partitioning problem under the condition of high proportion of new energy access.

[0005] To address the aforementioned technical problems, this invention provides a grid zoning method under the background of high proportion of renewable energy access, comprising the following steps:

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

[0007] Step S2: Based on the typical scenario set of new energy power output, define correction coefficients to correct the electrical distance matrix and obtain the 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] In the formula, d ij z is the electrical distance between nodes i and j; ij P is the impedance of the line between nodes i and j; ij It is the active power flowing through the line between nodes i and j; P ij,max It is the maximum active power that can be transferred between i and j; γ ij α is the power flow coefficient of line ij; α is the weight.

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

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

[0014] Step S212: Construct correction coefficients based on scenario probabilities;

[0015] Step S213: Correct the electrical distance matrix using the correction coefficient 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 k-th scenario; r represents the number of typical scenarios; p(k) represents the probability of the k-th scenario.

[0019] Preferably, step S213 includes:

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

[0021]

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

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

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

[0025] Step S31: Generate 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 membership matrix A of the AP clustering algorithm and set reference values;

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

[0028] Step S34: Change the reference value in step S32, and repeat steps S32 to S33 until the number of cluster centers output is the expected value, thus obtaining 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 t-th iteration; R t ′ +1 (i,j) represents the attraction value after the t-th iteration; A t (i,j) represents the degree of belonging between node i and node j in the t-th iteration; A t ′ +1 (i,j) represents the degree of belonging after the t-th iteration; λ is the damping coefficient, ranging from 0 to 1.

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

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

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

[0038] Step S223: Select scenes that satisfy the conditions of the Kantorovich distance formula, reduce them, and change the scene probability;

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

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

[0041]

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

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

[0044]

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

[0046] The beneficial effects of this invention include at least the following: This invention proposes a grid zoning method under the background of high proportion of renewable energy integration. It improves the calculation method of electrical distance index from the perspectives of line power flow and line impedance, and corrects the electrical distance matrix based on a typical set of renewable energy output scenarios, making the zoning scheme applicable to zoning problems of grids with a high proportion of renewable energy. This method is of great significance for improving the grid's ability to cope with the uncertainty of renewable energy output and the level of renewable energy absorption. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

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

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

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

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

[0052] Figure 6 This is a scenario of reduced wind and solar power output according to an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, this embodiment of the invention provides a grid zoning method under the background of high proportion of new energy access, including the following steps:

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

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

[0058]

[0059] In the above formula, z ij P is the impedance of the line between nodes i and j; ij P is the active power flowing through the line between nodes i and j. ij,max It is the maximum active power that can be transferred between i and j; γ ij It is the power flow coefficient of line ij; d ij α 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 power output, define correction coefficients to correct the electrical distance matrix and obtain the full-dimensional electrical distance matrix.

[0061] Specifically, correction coefficients are defined based on a set of typical scenarios for new energy power output, as shown in equations (2) and (3):

[0062]

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

[0064]

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

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

[0067]

[0068] For any two non-adjacent nodes, the shortest path between them is found based on Dijkstra's algorithm. The electrical distances of the connecting paths are then summed to obtain the electrical distance between any two nodes. Finally, these sums are combined to obtain the full-dimensional electrical distance matrix.

[0069] Correcting the electrical distance matrix before expanding to the full-dimensional electrical distance matrix directly considers the impact of renewable energy output on electrical connections. This allows for a more flexible response to the highly volatile nature of renewable energy, making the correction process more targeted, without altering or diluting the characteristics of renewable energy output during the correction 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, in the embodiments of the present invention, the update formulas for the attraction matrix R and the membership matrix A of the AP clustering algorithm in the power grid partitioning model based on the AP clustering algorithm are shown in equations (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 t-th iteration; R t ′ +1 (i,j) represents the attraction value after the t-th iteration; A t (i,j) represents the degree of belonging between node i and node j in the t-th iteration; A t ′ +1 (i,j) represents the degree of belonging after the t-th iteration; λ is the damping coefficient, ranging from 0 to 1, used for the convergence of the algorithm.

[0077] Compared to other clustering algorithms, the AP clustering algorithm used in this embodiment does not require manual determination of cluster centers. Instead, it allocates the number of cluster centers based on the similarity between nodes, without relying on the setting of initial parameters, thus possessing objectivity. 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 attribution matrix R, and set the initial reference values;

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

[0081] 4) Obtain the cluster centers and assign the other nodes to each cluster;

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

[0083] 6) Obtain the final partitioning scheme.

[0084] Example 2

[0085] This embodiment uses the method of Embodiment 1 to analyze two schemes. The schemes include: no new energy source in the system and new energy source in the system. Figure 2 and Figure 3 The partitioning results are shown for the two schemes. The full-dimensional electrical distance matrix can be calculated based on steps S1 and S2. Table 1 shows a partial comparison of the full-dimensional electrical distance matrix values ​​for the two schemes.

[0086] Table 1

[0087]

[0088] The comparison of the partitioning results shows that the electrical distance matrix of the system changed significantly before and after the integration of new energy sources. Specifically, the electrical distance between some nodes may increase or decrease. This change stems from the volatility and uncertainty brought about by the integration of new energy sources. For example, in Table 1, D(3,18), D(27,17), and D(9,39) decreased after the integration of new energy sources, while D(25,26), D(23,24), and D(14,15) decreased. This indicates that the volatility and uncertainty of new energy sources alter the inherent characteristics of the system. Therefore, it is crucial to comprehensively consider the characteristics of new energy sources when partitioning the system; otherwise, the partitioning scheme may be difficult to adapt to actual operational needs.

[0089] From the partition results Figure 2 and Figure 3 It can be seen that the size of the power grid zones changed significantly before and after the integration of renewable energy. Before the integration, the system was divided into four zones, while after the integration, it was divided into only three. This change is mainly attributed to the increased complexity and uncertainty of grid operation due to the volatility of renewable energy, making the original zones insufficient to meet the balancing requirements of renewable energy scenarios. Specifically, when the zone size is too small, power fluctuations within the region are difficult to mitigate, reducing system stability. Therefore, after the integration of renewable energy, the zone size increased while the number of zones decreased, reflecting the adaptive optimization of the system during zone adjustments.

[0090] For Region 2, the original electrical distance between Node 9 and Node 39 was relatively large, resulting in a weak electrical connection; therefore, they were assigned to different zones. However, after the integration of new energy sources, the electrical distance between Node 9 and Node 39 decreased significantly, indicating that their electrical connection was strengthened. Consequently, Node 39 was reassigned to Region 2 in the zone after the integration of new energy sources, leading to a significant expansion of Region 2's size. This adjustment not only reflects the reshaping of electrical connections by new energy sources but also demonstrates that the zoning scheme can dynamically adapt to system changes when considering the impact of new energy sources.

[0091] For Region 1, after the integration of new energy sources, Region 1 was merged with Region 3 and incorporated some nodes originally belonging to Region 4. The main reason for this adjustment is the concentrated distribution of new energy power plants within Region 1, including two photovoltaic power plants and two wind farms. The output characteristics of these new energy power plants increase the complexity of power fluctuations within the region, thus requiring an expansion of the region's scale to balance power supply and demand and enhance internal stability.

[0092] For Region 3, the original Region 4 was reclassified into a new Region 3 after the integration of renewable energy, and the region size was reduced. The new Region 3 does not include renewable energy power plants, indicating that it bears lower volatility risk in the new zoning scheme and is suitable as a region with higher stability in the system.

[0093] Based on the simulation results above, the effectiveness of the grid zoning scheme generation method under the background of high proportion of new energy access in this invention is verified.

[0094] Example 3

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

[0096] This embodiment uses the improved IEEE 39-node system as the research object, and the new energy access point and topology are as follows: Figure 4 As shown. First, an autoregressive moving average model is used to generate wind and solar power output sampling scenarios, as shown in equation (9). 200 wind power and 200 solar power output scenarios are generated, as shown in equation (9). Figure 5 As shown.

[0097]

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

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

[0100]

[0101] In the above formula, C i It is a set of scenes that have been reduced, Ci ' is the set of deleted scenes; d(sv,sv') is the set of scenes s. v and s v Euclidean distance η(s) v ,s v ′) is scene s v and s v The product of probabilities of ′; p sv and p s′v These are scene s v and s v ′ in C i and C i The probability in ′.

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

[0103] 1) Use C i N represents i A set of scenarios to be reduced, using C i ′ represents N i Given a set of ' deleted scenes, initialize the probabilities of each scene in the two scene sets.

[0104] 2) Each time, select a scenario s that satisfies the conditions of the Kantorovich distance formula. v and s 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 is

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

[0107] Reduced wind and solar power scenarios, such as Figure 6 As shown in Tables 2 and 3, the probabilities of wind power and solar power output in various scenarios are presented.

[0108] Table 1: Probability of Wind Power Output in Typical Scenarios

[0109] Scene probability Scene probability Scene probability Scene 1 0.005 Scene 5 0.090 Scene 9 0.005 Scene 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. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; only preferred embodiments of the present invention are illustrated. The descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. As long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0114] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A grid zoning method under the background of high proportion of renewable energy access, characterized in that: Includes the following steps: Step S1: From the perspectives of line power flow and line impedance, provide electrical distance indicators and construct an electrical distance matrix; Step S2: Based on the typical scenario set of new energy power output, define correction coefficients to correct the electrical distance matrix and obtain the full-dimensional electrical distance matrix; Step S3: Based on the electrical distance matrix, a clustering algorithm is used to partition the power grid; The electrical distance matrix d ij The expression is: In the formula, d ij z is the electrical distance between nodes i and j; ij P is the impedance of the line between nodes i and j; ij It is the active power flowing through the line between nodes i and j; P ij,max It is the maximum active power that can be transferred between i and j; γ ij α is the power flow coefficient of line ij; α is the weight. Step S2 includes the following steps: Step S211: Establish the power matrix under the typical scenario set of new energy power output: P (k) This is the power matrix of the system circuit in the k-th scenario; P i (k) is the power of the i-th line in the k-th scenario; m is the total number of lines; Step S212: Construct correction coefficients based on scenario probabilities; Step S213: Correct the electrical distance matrix using the correction coefficients to construct a full-dimensional electrical distance matrix; In step S212, the correction coefficient The expression is: In the formula, Let be the correction coefficient for the k-th scenario; r represents the number of typical scenarios; p(k) represents the probability of the k-th scenario. Step S213 includes: Step S2131: Correct the adjacent nodes in the electrical distance matrix using the correction coefficient: Step S2132: For two non-adjacent nodes, find the shortest path between the two nodes based on Dijkstra's algorithm, and then accumulate the electrical distances of the connecting paths to obtain the electrical distance between any two nodes. Step S2133: Construct a full-dimensional electrical distance matrix S using the electrical distances of adjacent nodes in step S231 and the electrical distances of non-adjacent nodes in step S232.

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

3. The grid zoning method under the background of high proportion of new energy access according to claim 2, characterized in that: The expression to be 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 t-th iteration; R′ t+1 (i,j) represents the attraction value after the t-th iteration; A t (i,j) represents the degree of belonging between node i and node j in the t-th iteration; A′ t+1 (i,j) represents the affiliation value after the t-th iteration; λ is the damping coefficient, which ranges from 0 to 1.

4. The grid zoning method under the background of high proportion of new energy access according to claim 1, characterized in that: The typical scenario set of new energy power output in step S2 is constructed using the following method: Step S221: Generate a new set of energy source output scenarios using an autoregressive moving average model; Step S222: Initialize the probabilities of the scene set; Step S223: Select scenes that satisfy the conditions of the Kantorovich distance formula, reduce them, and change the scene probability; Step S224: Repeat step S223 until the number of scenes in the scene set reaches the set number.

5. The grid zoning method under the background of high proportion of new energy access according to claim 4, characterized in that: The expression for step S221 is: In the formula, y t φ is the time series value at time t; i For autoregressive parameters; α t This is a normal white noise process with a mean of 0 and a variance of σ. 2 ;θ j represents the moving average parameters; a and b represent the model order.

6. The grid zoning method under the background of high proportion of new energy access according to claim 4, characterized in that: In step S223, the expression for the Kantorovich distance formula is: In the formula, C i Indicates the set of scenes to be reduced; C′ i This indicates that the scene set has been deleted; d(s) v ,s′ v ) for scene s v and s′ v Euclidean distance; η(s) v ,s′ v ) is scene s v and s′ v The product of probabilities; and These are scene s v and s′ v In C i and C′ i The probability of it.

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