Distributed photovoltaic polymerization method considering time-space two-dimensional characteristics

Abstract

The invention provides a distributed photovoltaic aggregation method considering time-space two-dimensional characteristics, and belongs to the field of power grids. The method comprises the following steps: acquiring operation data of a power grid system, and determining an electrical distance feature vector of each node and an output feature vector of each node according to the operation data; the electrical distance feature vector is used for representing the distributed photovoltaic electrical distance of the node; the output feature vectors are used for representing distributed photovoltaic output characteristics of the nodes; determining a comprehensive feature vector of each node according to the electrical distance feature vector of each node and the output feature vector of each node; clustering the comprehensive feature vector of each node to obtain a clustering center, wherein each clustering center forms a target aggregation model; the target aggregation model is used for determining the installation position and capacity of the energy storage device. According to the method, the target aggregation model is determined by combining the electrical distance feature vector and the output feature vector, the model is more reasonable, the configuration of the energy storage device is more reasonable, and the stability of a power grid system is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a distributed photovoltaic aggregation method taking into account dual-dimensional characteristics of time and space. Background Art

[0002] With the advancement of energy transition, clean energy sources such as photovoltaics and wind power have garnered widespread attention. Photovoltaic power generation has rapidly grown due to its widespread distribution and ease of development. However, due to the intermittent and unstable nature of photovoltaic power generation, its large-scale application can introduce significant power disturbances, posing challenges to system power balance. Energy storage devices can be used to smooth peak loads and fill valleys, significantly impacting system power balance.

[0003] In existing technology, distributed photovoltaic systems are typically modeled using aggregation, and the integration of energy storage devices is guided by this aggregation model. However, existing static aggregation models are typically applied to wind turbines. Due to the significant differences between wind turbines and photovoltaic systems, static aggregation models are not suitable for photovoltaic systems. This can lead to irrational energy storage device configuration and affect the stability of the power grid system. Summary of the Invention

[0004] The embodiment of the present invention provides a distributed photovoltaic aggregation method that takes into account the dual-dimensional characteristics of time and space, so as to solve the problem in the prior art that the aggregation model is unreasonable and affects the stability of the power grid system.

[0005] In a first aspect, an embodiment of the present invention provides a distributed photovoltaic aggregation method that takes into account the dual-dimensional characteristics of time and space, including:

[0006] Obtaining the operating data of the power grid system and determining the electrical distance characteristic vector and output characteristic vector of each node based on the operating data; wherein the electrical distance characteristic vector is used to characterize the distributed photovoltaic electrical distance of the node; and the output characteristic vector is used to characterize the distributed photovoltaic output characteristics of the node;

[0007] Determine the comprehensive characteristic vector of each node according to the electrical distance characteristic vector of each node and the output characteristic vector of each node;

[0008] Clustering the comprehensive feature vectors of each node to obtain at least one cluster center, and each cluster center forms a target aggregation model;

[0009] According to the target aggregation model, the installation location and capacity of the energy storage device are determined.

[0010] Optionally, a comprehensive characteristic vector of each node is determined based on the electrical distance characteristic vector of each node and the output characteristic vector of each node, including:

[0011] For any node, the first weight and second weight of the node are determined based on the electrical distance characteristic vector of the node and the output characteristic vector of the node; the electrical distance characteristic vector of the node is multiplied by the first weight to obtain a first intermediate vector; the output characteristic vector of the node is multiplied by the second weight to obtain a second intermediate vector; the first intermediate vector and the second intermediate vector are spliced together to obtain a comprehensive characteristic vector of the node.

[0012] Optionally, determining the first weight and the second weight of the node according to the electrical distance characteristic vector of the node and the output characteristic vector of the node includes:

[0013] Find the maximum value among the elements of the electrical distance characteristic vector of the node as the first maximum value of the node;

[0014] Find the maximum value among the elements of the output eigenvector of the node as the second maximum value of the node;

[0015] A first weight of the node and a second weight of the node are determined according to the first maximum value and the second maximum value.

[0016] Optionally, determining the first weight of the node and the second weight of the node according to the first maximum value and the second maximum value includes:

[0017] Determine the first weight of the node and the second weight of the node in combination with the first formula according to the first maximum value and the second maximum value;

[0018] The first formula is:

[0019]

[0020] Among them, w xi is the first weight of the i-th node, w yi is the second weight of the i-th node; a i is the first maximum value of the i-th node, b i is the second maximum value of the i-th node; i = 1,…, n, where n is the total number of nodes.

[0021] Optionally, determining the electrical distance characteristic vector of each node and the output characteristic vector of each node based on the operating data includes:

[0022] Determine the voltage-reactive sensitivity matrix according to the disturbed node voltage matrix and the node injected reactive matrix, and determine the electrical distance eigenvector of each node according to the voltage-reactive sensitivity matrix;

[0023] For any node, the output characteristic vector of the node is obtained according to the output curve of the distributed photovoltaic system of the node.

[0024] Optionally, determining a voltage-reactive-power sensitivity matrix based on the disturbed node voltage matrix and the node-injected reactive power matrix, and determining an electrical distance characteristic vector of each node based on the voltage-reactive-power sensitivity matrix, includes:

[0025] According to the disturbed node voltage matrix and the node injected reactive matrix, the voltage reactive sensitivity matrix is determined in combination with the second formula;

[0026] According to the voltage-reactive sensitivity matrix, the electrical distance characteristic vector of each node is determined in combination with the third formula;

[0027] The second formula includes:

[0028] ΔQ=S QV ΔV

[0029] Among them, ΔQ is the node injection reactive matrix, S QV is the voltage reactive sensitivity matrix, ΔV is the disturbed node voltage matrix;

[0030] The third formula includes:

[0031]

[0032] x i ′=[x i1 'x i2 ′…x in ′]

[0033]

[0034] x i =[x i1 x i2 ...x in ]

[0035] Among them, x ij ′ is the sensitivity of the reactive power change of node i to the voltage of node j, j = 1,…,n, n is the total amount of nodes; x i is the electrical distance characteristic vector of node i.

[0036] Optionally, the output characteristic vector of the node is obtained according to the output curve of the distributed photovoltaic system of the node, including:

[0037] According to the distributed photovoltaic output curve of the node, the output characteristic vector of the node is obtained in combination with the fourth formula;

[0038] The fourth formula includes:

[0039] y i =[y i1 y i2 ...y im ]=[Pi,T＝1 P i,T＝2 ...P i,T＝m ]

[0040]

[0041] Among them, y i is the output eigenvector of node i, P i,T＝k is the average output of distributed photovoltaic power generation at node i in the kth time period; T0 is the duration of each time period; p is the output of distributed photovoltaic power generation at node i at each moment in the kth time period; k = 1,…, m, where m is the total number of time periods.

[0042] Optionally, determine the installation location and capacity of the energy storage device, including:

[0043] For any cluster center, the node closest to the cluster center in the cluster corresponding to the cluster center is used as the installation location of the energy storage device corresponding to the cluster center; the output characteristic vector of the cluster center is determined based on the comprehensive characteristic vector of the cluster center, and the capacity of the energy storage device corresponding to the cluster center is determined based on the output characteristic vector of the cluster center.

[0044] Optionally, the K-means++ algorithm is used to cluster the comprehensive feature vectors of each node.

[0045] Optionally, the Euclidean distance is used to determine the distance between the sample point and the cluster center.

[0046] The embodiment of the present invention provides a distributed photovoltaic aggregation method that takes into account dual-dimensional characteristics of time and space. The above method includes: obtaining the operating data of the power grid system, and determining the electrical distance characteristic vector of each node and the output characteristic vector of each node based on the operating data; wherein the electrical distance characteristic vector is used to characterize the distributed photovoltaic electrical distance of the node; the output characteristic vector is used to characterize the distributed photovoltaic output characteristics of the node; according to the electrical distance characteristic vector of each node and the output characteristic vector of each node, the comprehensive characteristic vector of each node is determined; the comprehensive characteristic vector of each node is clustered to obtain at least one cluster center, and each cluster center forms a target aggregation model; according to the target aggregation model, the installation location and capacity of the energy storage device are determined. The embodiment of the present invention considers the characteristics of two dimensions, combines the electrical distance characteristic vector and the output characteristic vector to determine the target aggregation model. The aggregation model conforms to the application scenario of the photovoltaic unit and is more in line with the actual application needs. The energy storage configuration scheme obtained when used to guide the energy storage configuration is also more reasonable, which can effectively balance the power disturbance of the system and improve the stability of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 This is a flow chart for implementing a distributed photovoltaic aggregation method that takes into account the dual-dimensional characteristics of time and space, provided by an embodiment of the present invention;

[0049] Figure 2 is a topological diagram of a distributed photovoltaic system provided by an embodiment of the present invention;

[0050] Figure 3 is a photovoltaic output diagram at a certain node provided by an embodiment of the present invention;

[0051] Figure 4 Schematic diagram of clustering results provided by an embodiment of the present invention;

[0052] Figure 5 It is a schematic diagram of clustering results in the prior art;

[0053] Figure 6 This is a comparison diagram of the output of the cluster group and node 4 in the prior art;

[0054] Figure 7 It is a structural schematic diagram of a distributed photovoltaic aggregation device taking into account the dual-dimensional characteristics of time and space provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0057] Figure 1 The embodiment of the present invention provides a distributed photovoltaic aggregation method that takes into account the dual-dimensional characteristics of time and space. Figure 1 The embodiment of the present invention provides a distributed photovoltaic aggregation method that takes into account the dual-dimensional characteristics of time and space, which is detailed as follows:

[0058] The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space includes:

[0059] S101: Acquire operating data of the power grid system and determine the electrical distance characteristic vector and output characteristic vector of each node based on the operating data; wherein the electrical distance characteristic vector is used to characterize the distributed photovoltaic electrical distance of the node; and the output characteristic vector is used to characterize the distributed photovoltaic output characteristics of the node.

[0060] In order to reflect the degree of electrical coupling between nodes in the system, the embodiment of the present invention defines the concept of electrical distance to form an electrical distance feature vector of the first dimension.

[0061] At the same time, due to the intermittent and unstable nature of distributed photovoltaic power generation, its output characteristics need to be characterized. The embodiment of the present invention obtains the output curve of each distributed photovoltaic through the operating data of the distributed photovoltaic system to form a second-dimensional output feature vector.

[0062] In a possible implementation, S101 may include:

[0063] S1011: determining a voltage-reactive power sensitivity matrix based on the disturbed node voltage matrix and the node-injected reactive power matrix, and determining an electrical distance eigenvector of each node based on the voltage-reactive power sensitivity matrix;

[0064] In power grid systems, voltage-reactive power sensitivity is often used to calculate electrical distance to facilitate cluster control. Therefore, in the embodiments of the present invention, a voltage-reactive power sensitivity matrix is determined based on the disturbed node voltage matrix and the node-injected reactive power matrix. This matrix is then used to determine the electrical distance eigenvector for each node.

[0065] Specifically, S1011 may include:

[0066] 1. Determine the voltage-reactive sensitivity matrix based on the disturbed node voltage matrix and the node injected reactive matrix in combination with the second formula;

[0067] 2. Determine the electrical distance eigenvector of each node based on the voltage-reactive sensitivity matrix and the third formula;

[0068] The second formula includes:

[0069] ΔQ=S QV ΔV

[0070] Among them, ΔQ is the node injection reactive matrix, S QV is the voltage reactive sensitivity matrix, ΔV is the disturbed node voltage matrix;

[0071] The modified equation for Newton's method power flow calculation is:

[0072]

[0073] in, is the Jacobian matrix.

[0074] S QV This is actually the L portion of the Jacobian matrix in power flow calculations. Because it is not divided by the node voltage, the sensitivity matrix is asymmetric. Therefore, embodiments of the present invention can modify the voltage-reactive power sensitivity matrix.

[0075] Define x ij is the influence, x ij The calculation formula is:

[0076]

[0077] x i =[x i1 x i2 ...x in ]

[0078] Based on the above analysis, the third formula can include:

[0079]

[0080] x i ′=[x i1 'x i2 ′…x in ′]

[0081]

[0082] x i =[x i1 x i2 ...x in ]

[0083] Among them, x ij ′ is the sensitivity of the reactive power change of node i to the voltage of node j, j = 1,…,n, n is the total amount of nodes; x i is the electrical distance characteristic vector of node i.

[0084] S1012: For any node, obtain the output characteristic vector of the node according to the output curve of the distributed photovoltaic system of the node.

[0085] The operation data of the power grid system includes the output curve of distributed photovoltaics at each node, which reflects the output characteristics of each node, thereby forming the output characteristic vector of each node.

[0086] The output curve of distributed photovoltaic is divided into segments according to T0, and the average output in each period is calculated. The output in each period forms the output characteristic vector of the node.

[0087] Specifically, in a possible implementation, S1012 may include:

[0088] 1. Based on the distributed photovoltaic output curve of the node, the output characteristic vector of the node is obtained by combining the fourth formula;

[0089] The fourth formula may include:

[0090] y i =[y i1 y i2 ...y im ]=[P i,T＝1 P i,T＝2 ...P i,T＝m ]

[0091]

[0092] Among them, y i is the output eigenvector of node i, P i,T＝k is the average output of distributed photovoltaic power generation at node i in the kth time period; T0 is the duration of each time period; p is the output of distributed photovoltaic power generation at node i at each moment in the kth time period; k = 1,…, m, where m is the total number of time periods.

[0093] Based on the above, the electrical distance eigenvector and the output eigenvector of each node are determined. Since the electrical distance eigenvector and the output eigenvector reflect node characteristics from two different dimensions, it is necessary to combine them to provide a more comprehensive description of the characteristics.

[0094] S102: Determine a comprehensive characteristic vector of each node based on the electrical distance characteristic vector of each node and the output characteristic vector of each node;

[0095] In the embodiment of the present invention, the feature vectors of two dimensions are combined to obtain a comprehensive feature vector, which can more accurately and comprehensively reflect the node characteristics.

[0096] For example, the two vectors may be weighted to form a comprehensive feature vector.

[0097] Specifically, in a possible implementation, S102 may include:

[0098] S1021: For any node, determine the first weight and the second weight of the node based on the electrical distance characteristic vector of the node and the output characteristic vector of the node; multiply the electrical distance characteristic vector of the node by the first weight to obtain a first intermediate vector; multiply the output characteristic vector of the node by the second weight to obtain a second intermediate vector; splice the first intermediate vector and the second intermediate vector to obtain a comprehensive characteristic vector of the node.

[0099] Based on the above implementation, the electrical distance characteristic vector of node i is x i =[x i1 x i2 ...x in ], the output eigenvector of node i is y i =[y i1 y i2 ...y im ], weights are assigned to the electrical distance eigenvector and the output eigenvector respectively, and the two vectors are weighted to obtain the comprehensive integrated eigenvector.

[0100] Comprehensive feature vector v i It can be expressed as:

[0101] vi=[w xi xi1...w xi xi n w yi yi1...w yi yi m ]

[0102] Among them, w xi is the first weight of the i-th node, w yi is the second weight of the i-th node;

[0103] In the above formula, the electrical distance eigenvector is placed first and the output eigenvector is placed second. Alternatively, the output eigenvector can be placed second and the electrical distance eigenvector can be placed third. The specific order of splicing is not limited here.

[0104] In a possible implementation, S1021 may include:

[0105] 1. Find the maximum value among the elements of the electrical distance eigenvector of the node as the first maximum value of the node;

[0106] 2. Find the maximum value among the elements of the output eigenvector of the node and use it as the second maximum value of the node;

[0107] 3. Determine the first weight of the node and the second weight of the node based on the first maximum value and the second maximum value.

[0108] In the embodiment of the present invention, the maximum value of the two vectors can be determined, and the first weight and the second weight can be determined based on the hierarchical analysis method.

[0109] Specifically, determine the first maximum value a of the i-th node i and the second maximum b i , the hierarchical judgment matrix is:

[0110]

[0111] The square root method is used to calculate the first weight and the second weight, and we get:

[0112]

[0113] Normalizing the two weights yields:

[0114]

[0115] Therefore, the comprehensive feature vector v i It can be expressed as:

[0116]

[0117] Based on the above, in one possible implementation, determining the first weight of the node and the second weight of the node according to the first maximum value and the second maximum value may include:

[0118] 1. Determine the first weight and the second weight of the node based on the first maximum value and the second maximum value in combination with the first formula;

[0119] The first formula is:

[0120]

[0121] Among them, w xi is the first weight of the i-th node, w yi is the second weight of the i-th node; a i is the first maximum value of the i-th node, b i is the second maximum value of the i-th node; i = 1,…, n, where n is the total number of nodes.

[0122] S103: Clustering the comprehensive feature vectors of each node to obtain at least one cluster center, and each cluster center forms a target aggregation model; wherein the target aggregation model is used to determine the installation location and capacity of the energy storage device.

[0123] Based on the above analysis, the comprehensive feature vector of each node is obtained, and the comprehensive feature vector reflects the characteristics of each node.

[0124] The comprehensive feature vectors of each node can be clustered, and each cluster center can be formed into a target aggregation model to perform unified analysis on nodes of the same type.

[0125] The target aggregation model can be expressed as:

[0126]

[0127] Among them, u iis the comprehensive feature vector of the i-th cluster center, and k is the number of cluster centers.

[0128] For example, the K-means++ algorithm can be used to cluster the comprehensive feature vectors of each node. The specific steps are as follows:

[0129] 1) Randomly select the comprehensive feature vector of a node as the cluster center;

[0130] 2) For the remaining unselected nodes, the square of their distance from the cluster center is calculated based on their comprehensive feature vectors, and used as the probability of them being selected as the next cluster center;

[0131] 3) Repeat the steps in 2) until k cluster centers are selected;

[0132] 4) Take the kth cluster center as the initial cluster center and perform the K-means allocation and update steps until the algorithm converges.

[0133] Among them, the Euclidean distance can be used to define the distance between the comprehensive feature vectors of two nodes:

[0134]

[0135] Because D ij =D ji , then the distance matrix D is a symmetric matrix.

[0136] Based on the above, in the embodiment of the present invention, the target aggregation model is determined according to the operating parameters of each node in the distributed photovoltaic system. It is determined based on the actual application scenario of the distributed photovoltaic system and is in line with reality. The target aggregation model is more in line with actual application needs and is more accurate. It can be used to guide the economic configuration of energy storage in the distributed photovoltaic system to achieve the site selection and capacity determination of the energy storage device and the on-site consumption of new energy power generation. It can be used to provide a strong theoretical basis for the design and planning of new energy power systems.

[0137] In one possible implementation, determining the installation location and capacity of the energy storage device specifically includes:

[0138] For any cluster center, the node closest to the cluster center in the cluster corresponding to the cluster center is used as the installation location of the energy storage device corresponding to the cluster center; the output characteristic vector of the cluster center is determined based on the comprehensive characteristic vector of the cluster center, and the capacity of the energy storage device corresponding to the cluster center is determined based on the output characteristic vector of the cluster center.

[0139] In this embodiment of the present invention, energy storage devices can be installed at the nodes closest to the cluster center, enabling sensitive voltage regulation and local energy consumption for each node within the cluster. Furthermore, the distributed photovoltaic output curve for each node in the cluster can be inversely analyzed based on the clustering results. Based on the similar photovoltaic output within the cluster, the minimum capacity of the energy storage device can be determined, achieving economical energy storage configuration.

[0140] The embodiment of the present invention considers the characteristics of two dimensions and combines the electrical distance characteristic vector and the output characteristic vector to determine the target aggregation model. This model conforms to the application scenarios of photovoltaic units and is more in line with actual application needs. The energy storage configuration scheme obtained when used to guide energy storage configuration is also more reasonable, which can effectively balance the power disturbance of the system and improve the stability of the power grid system.

[0141] The above method is described in detail below with reference to specific embodiments.

[0142] refer to Figure 2 , which shows a topology diagram of a distributed photovoltaic system containing 33 nodes, and different time-fluctuating outputs are added at all nodes to simulate the distributed photovoltaic output.

[0143] Since the 33 nodes are all located in the distribution network of the same city, the photovoltaic output on the same day is roughly the same. Figure 3 The PV output diagram at a certain node is shown. Figure 3 Starting from 0:00, the photovoltaic output is 0; from 7:00, the output gradually increases until it reaches a peak around 13:00; then the output begins to gradually decrease until it drops to 0 at 20:00, in order to simulate the fluctuation of photovoltaic output during the day.

[0144] Using the method provided by the embodiment of the present invention, the above 33 nodes are clustered according to the comprehensive feature vector and divided into 4 groups. Figure 4 , thereby achieving unified control of the system. Figure 5 The results of clustering using the method in the prior art are shown. Figure 4 and Figure 5 It can be seen that the classification of node 4 is different.

[0145] Figure 6 A comparison chart shows the average photovoltaic output of Cluster Groups III and IV in the prior art clustering method, compared to the photovoltaic output of node 4. Since the photovoltaic output characteristics of node 4 are more consistent with the average output characteristics of Cluster Group III, it is more reasonable to classify node 4 as Cluster Group III. This shows that the clustering in this embodiment of the present invention is more accurate and more in line with practical application requirements. The determined target aggregation model is also more reasonable, and the resulting coordination solution is also more reasonable.

[0146] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0147] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0148] Figure 7 A schematic diagram of the structure of a distributed photovoltaic aggregation device taking into account the dual-dimensional characteristics of time and space provided by an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0149] like Figure 7 As shown, the distributed photovoltaic aggregation device considering the dual-dimensional characteristics of time and space includes:

[0150] The first vector determination module 21 is configured to obtain operating data of the power grid system and determine an electrical distance characteristic vector and an output characteristic vector of each node based on the operating data; the electrical distance characteristic vector is used to characterize the distributed photovoltaic electrical distance of the node; and the output characteristic vector is used to characterize the distributed photovoltaic output characteristics of the node;

[0151] The second vector determination module 22 is used to determine the comprehensive characteristic vector of each node based on the electrical distance characteristic vector of each node and the output characteristic vector of each node;

[0152] The clustering module 23 is used to cluster the comprehensive feature vectors of each node to obtain at least one cluster center, and each cluster center forms a target aggregation model; wherein the target aggregation model is used to determine the installation location and capacity of the energy storage device.

[0153] In one possibility, the second vector determination module 22 may include:

[0154] The weighted splicing unit is used to determine, for any node, the first weight and the second weight of the node based on the electrical distance characteristic vector of the node and the output characteristic vector of the node; multiply the electrical distance characteristic vector of the node by the first weight to obtain a first intermediate vector; multiply the output characteristic vector of the node by the second weight to obtain a second intermediate vector; and splice the first intermediate vector and the second intermediate vector to obtain a comprehensive characteristic vector of the node.

[0155] In a possible implementation, the weighted splicing unit may include:

[0156] A first extreme value determination subunit is used to find the maximum value among the elements of the electrical distance characteristic vector of the node as the first maximum value of the node;

[0157] A second extreme value determination subunit is used to find the maximum value among the elements of the output characteristic vector of the node as the second maximum value of the node;

[0158] The weight determination subunit is used to determine the first weight of the node and the second weight of the node according to the first maximum value and the second maximum value.

[0159] In a possible implementation, the weight determination subunit may be specifically configured to: determine the first weight of the node and the second weight of the node according to the first maximum value and the second maximum value in combination with the first formula;

[0160] The first formula can be:

[0161]

[0162] Among them, w xi is the first weight of the i-th node, w yi is the second weight of the i-th node; a i is the first maximum value of the i-th node, b i is the second maximum value of the i-th node; i = 1,…, n, where n is the total number of nodes.

[0163] In a possible implementation, the first vector determination module 21 may include:

[0164] A first sub-vector forming unit is used to determine a voltage-reactive power sensitivity matrix according to the disturbed node voltage matrix and the node injection reactive power matrix, and to determine an electrical distance eigenvector of each node according to the voltage-reactive power sensitivity matrix;

[0165] The second sub-vector forming unit is used to obtain, for any node, an output characteristic vector of the node according to the output curve of the distributed photovoltaic system of the node.

[0166] In a possible implementation, the first sub-vector forming unit may be specifically configured to:

[0167] 1. Determine the voltage-reactive sensitivity matrix based on the disturbed node voltage matrix and the node injected reactive matrix in combination with the second formula;

[0168] 2. Determine the electrical distance eigenvector of each node based on the voltage-reactive sensitivity matrix and the third formula;

[0169] The second formula may include:

[0170] ΔQ=S QV ΔV

[0171] Among them, ΔQ is the node injection reactive matrix, S QVis the voltage reactive sensitivity matrix, ΔV is the disturbed node voltage matrix;

[0172] The third formula may include:

[0173]

[0174] x i ′=[x i1 'x i2 ′…x in ′]

[0175]

[0176] x i =[x i1 x i2 ...x in ]

[0177] Among them, x ij ′ is the sensitivity of the reactive power change of node i to the voltage of node j, j = 1,…,n, n is the total amount of nodes; x i is the electrical distance characteristic vector of node i.

[0178] In a possible implementation, the second sub-vector forming unit may be specifically configured to: obtain the output characteristic vector of the node according to the output curve of the distributed photovoltaic system of the node in combination with the fourth formula;

[0179] The fourth formula may include:

[0180] y i =[y i1 y i2 ...y im ]=[P i,T＝1 P i,T＝2 ...P i,T＝m ]

[0181]

[0182] Among them, y i is the output eigenvector of node i, P i,T＝k is the average output of distributed photovoltaic power generation at node i in the kth time period; T0 is the duration of each time period; p is the output of distributed photovoltaic power generation at node i at each moment in the kth time period; k = 1,…, m, where m is the total number of time periods.

[0183] In one possible implementation, determining the installation location and capacity of the energy storage device may include: for any cluster center, using the node in the cluster corresponding to the cluster center that is closest to the cluster center as the installation location of the energy storage device corresponding to the cluster center; determining the output characteristic vector of the cluster center based on the comprehensive characteristic vector of the cluster center, and determining the capacity of the energy storage device corresponding to the cluster center based on the output characteristic vector of the cluster center.

[0184] In a possible implementation, a K-means++ algorithm may be used to cluster the comprehensive feature vectors of each node.

[0185] In a possible implementation, the Euclidean distance may be used to determine the distance between the sample point and the cluster center.

[0186] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0187] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0188] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned distributed photovoltaic aggregation method embodiment taking into account the dual-dimensional characteristics of time and space. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0189] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A distributed photovoltaic aggregation method taking into account the dual-dimensional characteristics of time and space, characterized in that: include: Acquiring operational data of the power grid system, and determining an electrical distance characteristic vector and an output characteristic vector of each node based on the operational data; wherein the electrical distance characteristic vector is used to characterize the distributed photovoltaic electrical distance of the node; and the output characteristic vector is used to characterize the distributed photovoltaic output characteristics of the node; Determine the comprehensive characteristic vector of each node according to the electrical distance characteristic vector of each node and the output characteristic vector of each node; The comprehensive feature vectors of each node are clustered to obtain at least one cluster center, and each cluster center forms a target aggregation model; wherein the target aggregation model is used to determine the installation location and capacity of the energy storage device.

2. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 1 is characterized in that: Determining the comprehensive characteristic vector of each node based on the electrical distance characteristic vector of each node and the output characteristic vector of each node includes: For any node, the first weight and second weight of the node are determined based on the electrical distance characteristic vector of the node and the output characteristic vector of the node; the electrical distance characteristic vector of the node is multiplied by the first weight to obtain a first intermediate vector; the output characteristic vector of the node is multiplied by the second weight to obtain a second intermediate vector; the first intermediate vector and the second intermediate vector are spliced together to obtain a comprehensive characteristic vector of the node.

3. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 2 is characterized in that: The determining the first weight and the second weight of the node according to the electrical distance characteristic vector of the node and the output characteristic vector of the node includes: Find the maximum value among the elements of the electrical distance characteristic vector of the node as the first maximum value of the node; Find the maximum value among the elements of the output eigenvector of the node as the second maximum value of the node; A first weight of the node and a second weight of the node are determined according to the first maximum value and the second maximum value.

4. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 3 is characterized in that: The determining, according to the first maximum value and the second maximum value, a first weight of the node and a second weight of the node includes: Determine a first weight of the node and a second weight of the node according to the first maximum value and the second maximum value in combination with a first formula; The first formula is: Among them, w xi is the first weight of the i-th node, w yi is the second weight of the i-th node; a i is the first maximum value of the i-th node, b i is the second maximum value of the i-th node; i = 1,…, n, where n is the total number of nodes.

5. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to any one of claims 1 to 4, characterized in that: The determining of the electrical distance characteristic vector of each node and the output characteristic vector of each node according to the operating data includes: Determine a voltage-reactive sensitivity matrix based on the disturbed node voltage matrix and the node-injected reactive matrix, and determine an electrical distance eigenvector of each node based on the voltage-reactive sensitivity matrix; For any node, the output characteristic vector of the node is obtained according to the output curve of the distributed photovoltaic system of the node.

6. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 5 is characterized in that: The step of determining a voltage-reactive sensitivity matrix based on the disturbed node voltage matrix and the node-injected reactive matrix, and determining an electrical distance characteristic vector of each node based on the voltage-reactive sensitivity matrix, includes: Determine the voltage-reactive sensitivity matrix according to the disturbed node voltage matrix and the node injected reactive matrix in combination with a second formula; Determine the electrical distance eigenvector of each node based on the voltage-reactive sensitivity matrix and the third formula; The second formula includes: ΔQ=S QV ΔV Wherein, ΔQ is the reactive matrix injected into the node, S QV is the voltage reactive sensitivity matrix, ΔV is the node voltage matrix of the disturbance; The third formula includes: x i ′=[x i1 ′x i2 ′…x in ′] x i =[x i1 x i2 ...x in ] Among them, x ij ′ is the sensitivity of the reactive power change of node i to the voltage of node j, j = 1,…,n, n is the total amount of nodes; x i is the electrical distance characteristic vector of node i.

7. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 5, characterized in that: The step of obtaining the output characteristic vector of the node according to the output curve of the distributed photovoltaic system of the node includes: According to the distributed photovoltaic output curve of the node, the output characteristic vector of the node is obtained in combination with the fourth formula; The fourth formula includes: and i =[and i1 and i2 ...and im ]=[P i,T＝1 P i,T＝2 ...P i,T＝m ] Among them, y i is the output eigenvector of node i, P i,T＝k is the average output of distributed photovoltaic power generation at node i in the kth time period; T0 is the duration of each time period; p is the output of distributed photovoltaic power generation at node i at each moment in the kth time period; k = 1,…, m, where m is the total number of time periods.

8. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to any one of claims 1 to 4, characterized in that: Determining the installation location and capacity of the energy storage device includes: For any cluster center, the node closest to the cluster center in the cluster corresponding to the cluster center is used as the installation location of the energy storage device corresponding to the cluster center; the output characteristic vector of the cluster center is determined based on the comprehensive characteristic vector of the cluster center, and the capacity of the energy storage device corresponding to the cluster center is determined based on the output characteristic vector of the cluster center.

9. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to any one of claims 1 to 4, characterized in that: The K-means++ algorithm is used to cluster the comprehensive feature vectors of each node.

10. The distributed photovoltaic aggregation method considering the dual-dimensional characteristics of time and space according to claim 9, characterized in that: The Euclidean distance is used to determine the distance between the sample point and the cluster center.

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