New energy model aggregation method and system based on network path planning

Abstract

The invention discloses a new energy model aggregation method and system based on network path planning, and the method comprises the steps: obtaining whole network data, and determining a to-be-aggregated source node set and a target node set which is accessed to an equivalent power supply after aggregation based on the whole network data; constructing a power grid weighted graph; based on the power grid weighted graph, calculating an optimal target node with the minimum total path cost corresponding to each source node in the source node set, and establishing a mapping relation between each source node and the corresponding optimal target node; and constructing a post-aggregation simulation model based on the mapping relation. According to the invention, the optimal electrical path to the backbone network frame bus is searched for each dispersed new energy power supply bus through the shortest path algorithm, and new energy output on a large number of dispersed source nodes is aggregated to a plurality of backbone network frame nodes with a relatively small number according to the path relation. Therefore, the aggregation of the new energy power supply nodes and the simplification efficiency of the whole power grid model are high, and the consistency is good.

Description

Technical Field

[0001] This invention relates to the field of digital simulation and calculation technology for power systems, and more specifically, to a method and system for aggregating new energy models based on network path planning. Background Technology

[0002] With the increasing proportion of renewable energy generation, wind power, photovoltaic, and other power plants are being widely and dispersedly connected to the grid. To assess the impact of this large-scale renewable energy integration on the stability characteristics of the power grid, electromechanical transient simulations of the power system require the creation of dynamic models containing detailed control models for each renewable energy plant in simulation software (such as BPA, PSASP, PSSE, etc.). This results in a massive number of simulation network nodes and control units, often exceeding the unit processing capacity limits of commercial simulation software, making it difficult to implement detailed modeling of all renewable energy nodes directly.

[0003] Currently, the industry typically uses equivalent methods to reduce model size, such as Ward's equivalent and REI equivalent methods. These traditional methods ignore some dynamic characteristics of the peripheral system and focus primarily on the detailed dynamic characteristics of the internal system. However, this approach is not suitable for studying the overall stability of large-scale interconnected power grids with numerous distributed renewable energy sources. Another common approach is to perform coarse approximation aggregation based on geographical location and other characteristics. However, this approach may ignore certain actual electrical connections and fail to accurately reflect some dynamic response characteristics determined by the network structure.

[0004] Therefore, a new energy model aggregation method based on network path planning is needed. Summary of the Invention

[0005] This invention proposes a new energy model aggregation method and system based on network path planning to solve the problems of excessive software capacity and low computational efficiency caused by excessively large node size in transient stability simulation.

[0006] The present invention aims to solve the problems existing in the prior art, and the specific objectives include: 1. solving the problem of the scale of the detailed simulation model of the power grid exceeding the limit due to the large number of new energy nodes; 2. providing an automated model aggregation method to overcome the limitations of simple geographical aggregation; 3. ensuring that the simplified model after aggregation can retain the key electrical dynamic characteristics of the original power grid backbone and new energy units to the greatest extent while limiting the number of model units.

[0007] To address the aforementioned problems, according to one aspect of the present invention, a new energy model aggregation method based on network path planning is provided, the method comprising:

[0008] Acquire full network data, and based on the full network data, determine the set of source nodes to be aggregated and the set of target nodes to be connected to the equivalent power source after aggregation;

[0009] Construct a weighted graph of the power grid;

[0010] Based on the power grid weighted graph, calculate the optimal target node with the minimum total path cost for each source node in the source node set, and establish a mapping relationship between each source node and its corresponding optimal target node.

[0011] An aggregated simulation model is constructed based on the mapping relationship.

[0012] Preferably, determining the set of source nodes to be aggregated and the set of target nodes to be connected to an equivalent power source after aggregation based on the entire network data includes:

[0013] The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data;

[0014] Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation;

[0015] Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

[0016] Preferably, the construction of the power grid weighted map includes:

[0017] The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

[0018] Preferably, the weight of the edge is determined based on the impedance magnitude |Z| or reactance value X of the branch.

[0019] Preferably, the calculation of the optimal target node with the minimum total path cost for each source node in the source node set, based on the power grid weighted graph, includes:

[0020] For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

[0021] Preferably, the construction of the aggregated simulation model based on the mapping relationship includes:

[0022] For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch.

[0023] Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed.

[0024] The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

[0025] According to another aspect of the present invention, a new energy model aggregation system based on network path planning is provided, the system comprising:

[0026] The data acquisition unit is used to acquire data from the entire network and determine, based on the data, the set of source nodes to be aggregated and the set of target nodes to be connected to the equivalent power source after aggregation.

[0027] The power grid weighted graph construction unit is used to construct a power grid weighted graph.

[0028] The mapping relationship establishment unit is used to calculate the optimal target node with the minimum total path cost corresponding to each source node in the source node set based on the power grid weighted graph, and to establish a mapping relationship between each source node and the corresponding optimal target node.

[0029] An aggregation unit is used to construct an aggregated simulation model based on the mapping relationship.

[0030] Preferably, the data acquisition unit, based on the network-wide data, determines the set of source nodes to be aggregated and the set of target nodes to be connected to an equivalent power source after aggregation, including:

[0031] The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data;

[0032] Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation;

[0033] Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

[0034] Preferably, the power grid weighted graph construction unit constructs a power grid weighted graph by including:

[0035] The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

[0036] Preferably, the power grid weighted graph construction unit determines the weight of the edge based on the impedance magnitude |Z| or reactance value X of the branch.

[0037] Preferably, the mapping relationship establishment unit, based on the power grid weighted graph, calculates the optimal target node with the minimum total path cost corresponding to each source node in the source node set, including:

[0038] For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

[0039] Preferably, the aggregation unit, based on the mapping relationship, constructs an aggregated simulation model, including:

[0040] For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch.

[0041] Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed.

[0042] The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

[0043] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps of a new energy model aggregation method based on network path planning.

[0044] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0045] The aforementioned computer-readable storage medium; and

[0046] One or more processors for executing a program in the computer-readable storage medium.

[0047] This invention provides a new energy model aggregation method and system based on network path planning, comprising: acquiring full network data, and determining the set of source nodes to be aggregated and the set of target nodes connected to equivalent power sources after aggregation based on the full network data; constructing a power grid weighted graph; calculating the optimal target node with the minimum total path cost for each source node in the set of source nodes based on the power grid weighted graph, and establishing a mapping relationship between each source node and its corresponding optimal target node; and constructing a post-aggregation simulation model based on the mapping relationship. This invention abstracts the power grid as a graph network with "electrical distance" as the weight, and uses a shortest path algorithm to find the "optimal electrical path" to the main grid bus (target node) for each dispersed new energy power source bus (source node). Based on this path relationship, the new energy output from a large number of dispersed source nodes is aggregated to a relatively small number of main grid nodes, thereby achieving the aggregation of new energy power source nodes and simplification of the overall power grid model. The entire process can be automatically completed by a computer program, without relying on expert experience for manual equivalence, resulting in high efficiency, good consistency, and the core algorithm being independent of simulation software, allowing the use of various commercial software and demonstrating good versatility. Attached Figure Description

[0048] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0049] Figure 1 A flowchart of a new energy model aggregation method 100 based on network path planning according to an embodiment of the present invention;

[0050] Figure 2 This is a flowchart illustrating the overall process of a new energy model aggregation method based on network path planning according to an embodiment of the present invention.

[0051] Figure 3 This is an example diagram illustrating the search for the optimal path and optimal node according to an embodiment of the present invention;

[0052] Figure 4 This is a wind power distribution map before aggregation according to an embodiment of the present invention;

[0053] Figure 5 This is a map showing the agglomerated wind power distribution according to an embodiment of the present invention;

[0054] Figure 6 This is a schematic diagram of the structure of a new energy model aggregation system 600 based on network path planning according to an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0056] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0057] Figure 1 This is a flowchart of a new energy model aggregation method 100 based on network path planning according to an embodiment of the present invention. Figure 1 As shown, the new energy model aggregation method based on network path planning provided by this invention abstracts the power grid as a graph network with "electrical distance" as the weight. It uses a shortest path algorithm to find the "optimal electrical path" to the main grid bus (target node) for each dispersed new energy power source bus (source node). Based on this path relationship, the new energy output from a large number of dispersed source nodes is aggregated to a relatively small number of main grid nodes, thereby achieving the aggregation of new energy power source nodes and simplifying the overall power grid model. The entire process can be automatically completed by a computer program, without relying on expert experience for manual equivalence, resulting in high efficiency, good consistency, and the core algorithm being independent of simulation software, allowing the use of various commercial software and demonstrating good versatility. The new energy model aggregation method 100 based on network path planning provided by this invention starts from step 101. In step 101, the entire network data is acquired, and based on the entire network data, the set of source nodes to be aggregated and the set of target nodes connected to the equivalent power source after aggregation are determined.

[0058] Preferably, determining the set of source nodes to be aggregated and the set of target nodes to be connected to an equivalent power source after aggregation based on the entire network data includes:

[0059] The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data;

[0060] Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation;

[0061] Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

[0062] In this invention, the source node set and the target node set are first determined. Specifically, the entire network data is input, and the low-voltage busbars (e.g., 34.5kV) of the new energy power supply busbars are defined as the source node set S to be aggregated. source The main grid nodes at high and medium voltage levels (such as 500kV, 220kV, and 110kV buses) are defined as the target node set S to be connected to the equivalent power source after final aggregation. target .

[0063] In step 102, a power grid weighted graph is constructed.

[0064] Preferably, the construction of the power grid weighted map includes:

[0065] The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

[0066] Preferably, the weight of the edge is determined based on the impedance magnitude |Z| or reactance value X of the branch.

[0067] In this invention, the physical power grid is abstracted as a weighted undirected graph G = (V, E, W). Here, V represents the set of all bus nodes; E represents the set of branches (lines, transformers); and W is the weight w(i,j) assigned to each edge e(i,j), which represents the electrical distance. Preferably, the weights are assigned using the impedance magnitude |Z| or reactance value X of the branch.

[0068] In step 103, based on the power grid weighted graph, the optimal target node with the minimum total path cost corresponding to each source node in the source node set is calculated, and a mapping relationship between each source node and its corresponding optimal target node is established.

[0069] Preferably, the calculation of the optimal target node with the minimum total path cost for each source node in the source node set, based on the power grid weighted graph, includes:

[0070] For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

[0071] Combination Figure 2 As shown, in this invention, the process of finding the optimal aggregation path and target node includes: for each source node s i ∈S source Perform the following operations in sequence:

[0072] 1. Shortest path calculation: using s i Starting from a point, use a shortest path algorithm (such as Dijkstra's algorithm) to calculate the path t to all target nodes. j ∈S target The shortest electrical path (i.e., the path with the minimum sum of edge weights);

[0073] 2. Optimal Target Node Selection: From all the calculation results in step 1, the target node with the shortest electrical path is selected as the optimal target node, denoted as t. k ;

[0074] 3. Establishing the mapping relationship: Establish the mapping relationship from the source node s i to the optimal target node t k mapping relationship That is, f(s) i )=t k .

[0075] like Figure 3 The diagram illustrates an example of finding the optimal target node for a wind power node W1. H1, H2, and H3 are all target nodes that meet the specified conditions. After applying a weighted shortest path algorithm (e.g., Dijkstra's algorithm), the red path points to the optimal target node closest to W1.

[0076] In step 104, an aggregated simulation model is constructed based on the mapping relationship.

[0077] Preferably, the construction of the aggregated simulation model based on the mapping relationship includes:

[0078] For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch.

[0079] Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed.

[0080] The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

[0081] Combination Figure 2As shown, in this invention, based on the established mapping relationship f from the source node to the optimal target node, the following steps are performed to complete the transfer and aggregation of new energy output, including:

[0082] 1. Create a new aggregated power node: Determine all optimal target nodes that have mapping relationships, i.e., a set. Subsequently, for each t k ∈S target Create a new power node g near it. j The power node is directly connected to the optimal target node t via a newly built grid-connected branch. k superior;

[0083] 2. Transfer and Aggregation: Transfer the original source node s i ∈S source The output power is transferred and aggregated to its mapped optimal target node t. k The corresponding newly built power point g j Up. Create a new power point g j The output is the sum of these outputs: After the transfer is complete, remove the original node s. i The effort put in; among them, For the original node s i The effort put in;

[0084] 3. Arrange parameters for newly built grid-connected branch lines: based on the parameters of each newly built power source point g. j Aggregate output capacity The parameters of the corresponding grid-connected branch are arranged according to the power output.

[0085] Through the above process, the construction of the aggregated simulation model can be completed. The power outputs of the original distributed renewable energy nodes have been removed and replaced by newly built power nodes derived from a smaller number of target nodes. After configuring detailed dynamic models for the new power nodes, transient stability calculations can be performed in the simulation software.

[0086] like Figure 4 The image shows a power grid before aggregation, containing a large number of dispersed renewable energy nodes. For example... Figure 5 As shown, this is a simplified power grid after aggregation, where the number of new energy power nodes has been aggregated into dozens of 500kV backbone network nodes. The area of ​​the red circles represents wind power output, and the sum of the red areas before and after aggregation is equal. This diagram does not include complete power network nodes and branches; only the relevant wind power nodes, backbone network, and aggregation paths from the final result are retained.

[0087] The new energy model aggregation method based on network path planning proposed in this invention has the following beneficial effects:

[0088] 1. Efficiently solves the problem of large-scale power grid simulation: It can aggregate thousands of new energy power nodes into dozens or hundreds of backbone network nodes, overcome the bottleneck of the upper limit of the number of dynamic model units in commercial simulation software, and realize efficient simulation of large-scale power grids with detailed new energy models.

[0089] 2. Preserves the true impact of the backbone network on stability characteristics: Aggregation is based on the "shortest electrical path" principle, ensuring that aggregated power sources are placed on the backbone network nodes with the closest electrical relationships. While significantly reducing the number of renewable energy power source nodes, it maximizes the preservation of the true impact of renewable energy power injection and backbone network characteristics on stability. The simulation results are more accurate than empirical aggregation methods based on geographical features and other conditions.

[0090] 3. Preserves detailed dynamic characteristics of new energy power generation equipment and control systems: Some new energy models contain thousands of control units, which may not be directly deployable across thousands of new energy access points on the entire network due to software processing limitations. Therefore, large-scale simulations necessitate the use of static simplified models. However, because this invention significantly reduces the number of new energy power nodes, detailed dynamic models can be deployed at each aggregation point. This maximizes the preservation of the dynamic response characteristics and protection control logic of the new energy model. The simulation results are more accurate than new energy modeling methods using static models or other simplified models.

[0091] 4. High automation and versatility: The entire process can be completed automatically by a computer program, eliminating the need for manual equivalence based on expert experience, resulting in high efficiency and good consistency. The core algorithm of this invention is independent of simulation software and can be used with various commercial software, demonstrating good versatility.

[0092] Taking a certain country's interconnected power grid as an example, this grid has approximately 1000 distributed 34.5kV wind power nodes. If each wind power model contains 1000 control units, the total number of control units for the wind power section will reach one million, exceeding the processing limit of the country's simulation software. The aggregation process based on the method of this invention includes:

[0093] S1: Define all 1000 34.5kV wind power busbars as S source Define all 500kV and 230kV busbars in the entire network as S. target .

[0094] S2: Construct a weighted graph model of the power grid, where the weights w(i,j) are taken as the impedance modulus |Z| of the branch lines.

[0095] S3: Perform shortest path calculation for each source node separately. For example, calculate the shortest path from wind farm A to all 500 / 230kV nodes. It is found that the total path impedance to 500kV substation B is the smallest. Therefore, it is determined to aggregate wind farm A to 500kV substation B.

[0096] S4: Assume that 30 wind farms will eventually be aggregated to a 500kV node B. Then, a new 34.5kV power node B' will be built near node B, and the total output of these 30 wind farms (assuming 1500MW) will be connected to node B'. Typical parameters will be assigned to the branches between B and B' according to the output.

[0097] S5: Ultimately, all 1000 wind power buses were aggregated onto 50 backbone network nodes. The number of wind power nodes decreased significantly, the wind power transient model was successfully deployed, and transient fault simulation calculations were successfully completed.

[0098] Figure 6 This is a schematic diagram of the structure of a new energy model aggregation system 600 based on network path planning according to an embodiment of the present invention. Figure 6 As shown, the new energy model aggregation system 600 based on network path planning provided by the embodiments of the present invention includes: a data acquisition unit 601, a power grid weighted graph construction unit 602, a mapping relationship establishment unit 603, and an aggregation unit 604.

[0099] Preferably, the data acquisition unit 601 is used to acquire data from the entire network and determine, based on the data from the entire network, the set of source nodes to be aggregated and the set of target nodes to be connected to an equivalent power source after aggregation.

[0100] Preferably, the data acquisition unit 601 determines, based on the network-wide data, the set of source nodes to be aggregated and the set of target nodes connected to an equivalent power source after aggregation, including:

[0101] The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data;

[0102] Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation;

[0103] Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

[0104] Preferably, the power grid weighted graph construction unit 602 is used to construct a power grid weighted graph.

[0105] Preferably, the power grid weighted graph construction unit 602 constructs a power grid weighted graph, including:

[0106] The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

[0107] Preferably, the power grid weighted graph construction unit 602 determines the weight of the edge based on the impedance modulus |Z| or reactance value X of the branch.

[0108] Preferably, the mapping relationship establishment unit 603 is used to calculate the optimal target node with the minimum total path cost corresponding to each source node in the source node set based on the power grid weighted graph, and to establish a mapping relationship between each source node and the corresponding optimal target node.

[0109] Preferably, the mapping relationship establishment unit 603, based on the power grid weighted graph, calculates the optimal target node with the minimum total path cost corresponding to each source node in the source node set, including:

[0110] For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

[0111] Preferably, the aggregation unit 604 is used to construct an aggregated simulation model based on the mapping relationship.

[0112] Preferably, the aggregation unit 604, based on the mapping relationship, constructs an aggregated simulation model, including:

[0113] For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch.

[0114] Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed.

[0115] The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

[0116] The new energy model aggregation system 600 based on network path planning in an embodiment of the present invention corresponds to the new energy model aggregation method 100 based on network path planning in another embodiment of the present invention, and will not be described again here.

[0117] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps of a new energy model aggregation method based on network path planning.

[0118] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0119] The aforementioned computer-readable storage medium; and

[0120] One or more processors for executing a program in the computer-readable storage medium.

[0121] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0122] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A new energy model aggregation method based on network path planning, characterized in that, The method includes: Acquire full network data, and based on the full network data, determine the set of source nodes to be aggregated and the set of target nodes to be connected to the equivalent power source after aggregation; Construct a weighted graph of the power grid; Based on the power grid weighted graph, calculate the optimal target node with the minimum total path cost for each source node in the source node set, and establish a mapping relationship between each source node and its corresponding optimal target node. An aggregated simulation model is constructed based on the mapping relationship.

2. The method according to claim 1, characterized in that, Based on the aforementioned network-wide data, the set of source nodes to be aggregated and the set of target nodes to be connected to equivalent power sources after aggregation are determined, including: The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data; Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation; Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

3. The method according to claim 1, characterized in that, The construction of the power grid weighted graph includes: The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

4. The method according to claim 3, characterized in that, The weight of an edge is determined based on the impedance magnitude |Z| or reactance value X of the branch.

5. The method according to claim 1, characterized in that, Based on the power grid weighted graph, calculate the optimal target node with the minimum total path cost for each source node in the source node set, including: For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

6. The method according to claim 1, characterized in that, Based on the mapping relationship, an aggregated simulation model is constructed, including: For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch. Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed. The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

7. A new energy model aggregation system based on network path planning, characterized in that, The system includes: The data acquisition unit is used to acquire data from the entire network and determine, based on the data, the set of source nodes to be aggregated and the set of target nodes to be connected to the equivalent power source after aggregation. The power grid weighted graph construction unit is used to construct a power grid weighted graph. The mapping relationship establishment unit is used to calculate the optimal target node with the minimum total path cost corresponding to each source node in the source node set based on the power grid weighted graph, and to establish a mapping relationship between each source node and the corresponding optimal target node. An aggregation unit is used to construct an aggregated simulation model based on the mapping relationship.

8. The system according to claim 6, characterized in that, The data acquisition unit determines, based on the network-wide data, the set of source nodes to be aggregated and the set of target nodes to be connected to an equivalent power source after aggregation, including: The set of source nodes to be aggregated is determined based on the low-voltage busbars of new energy power supply buses in the whole network data; Based on the backbone network nodes of high and medium voltage levels in the entire network data, determine the target set of nodes to be connected to the equivalent power source after final aggregation; Low voltage level refers to voltage less than 110kV, while high voltage level refers to voltage greater than or equal to 110kV.

9. The system according to claim 6, characterized in that, The power grid weighted graph construction unit constructs a power grid weighted graph, including: The power grid is abstracted as a weighted undirected graph G = (V, E, W); where V represents the set of all bus nodes; E is the edge, representing the set of branches; W is the set of weights w(i, j) assigned to each edge e(i, j), which represent the electrical distance, and i and j are nodes.

10. The system according to claim 9, characterized in that, The power grid weighted graph construction unit determines the weight of the edge based on the impedance magnitude |Z| or reactance value X of the branch.

11. The system according to claim 6, characterized in that, The mapping relationship establishment unit, based on the power grid weighted graph, calculates the optimal target node with the minimum total path cost for each source node in the source node set, including: For any source node s in the weighted graph of the power grid i ∈S source , with s i Starting from a given source node, use the shortest path algorithm to calculate the path t from any given source node to all target nodes. j ∈S target Find the shortest electrical path and select the target node with the smallest shortest electrical path as the optimal target node; where S source S is the set of source nodes; target For the target node set.

12. The system according to claim 6, characterized in that, The aggregation unit constructs an aggregated simulation model based on the mapping relationship, including: For each optimal target node, a corresponding new power node is created, and each optimal target node and the corresponding new power node are connected by a new grid-connected branch. Based on the mapping relationship, all the output power on all source nodes is transferred and aggregated to the newly created power node corresponding to the optimal target node of each source node, and the output power on the source node is removed after the transfer is completed. The aggregated output capacity of each newly built power node is determined, and the corresponding grid-connected branch parameters are determined based on the aggregated output capacity to construct a post-aggregation simulation model.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

14. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 13; as well as One or more processors for executing a program in the computer-readable storage medium.

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