New energy equipment capacity configuration method and system based on graph neural network

Abstract

The invention relates to the technical field of smart power grids, and discloses a new energy equipment capacity configuration method and system based on a graph neural network, and the method comprises the steps: constructing a corresponding directed weighted graph structure based on the topological data of a target new energy equipment group; updating the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition structure; performing parameter adjustment on the first multi-modal graph neural network model based on the time-varying composition structure to obtain a second multi-modal graph neural network model; performing multi-modal feature fusion on the time variation composition structure based on a second multi-modal graph neural network model to obtain a corresponding embedded vector set; and on the basis of the embedded vector set, a capacity planning model is constructed by taking the matching of the generating capacity of the target new energy equipment group with the power demand as a target, and the capacity planning model is solved to obtain a distributed capacity configuration strategy of the target new energy equipment group. The reliability of capacity planning can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a new energy equipment capacity configuration method and system based on graph neural network. Background Art

[0002] As the proportion of renewable energy power generation equipment in the power grid continues to rise, the rational planning of its capacity is of great significance to ensure the stable and efficient operation of the power grid. At present, the technology of capacity planning of renewable energy power generation equipment using graph neural network models has been applied to a certain extent. The construction of its initial graph structure mostly depends on static data such as the geographical location, type, and power generation capacity of renewable energy power generation equipment, as well as the connection information of power grid nodes. However, the power grid environment is not fixed. The real-time collection of dynamic information such as equipment operation data and meteorological data by sensors makes the attributes of nodes and edges in the graph change frequently.

[0003] Existing technologies have some shortcomings when dealing with high-frequency dynamic changes. On the one hand, it is difficult to take into account both real-time and accuracy at the same time. When processing newly added or changed nodes and edges, information lag or error accumulation is prone to occur. For example, when meteorological conditions suddenly change and cause the power generation capacity of a new energy power generation equipment to fluctuate greatly, the model cannot quickly and accurately adjust the node attributes and re-evaluate its interactive impact on surrounding nodes. On the other hand, new energy power generation equipment is diverse in type and highly heterogeneous in layout. The interactive relationships between different types of equipment are intricate, and unified modeling is difficult. This also exacerbates the difficulty of the model in dealing with dynamic changes, resulting in deviations between capacity planning results and actual needs, affecting the efficient utilization and stable operation of new energy power generation equipment in the power grid. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a new energy equipment capacity configuration method and system based on graph neural network.

[0005] In a first aspect, an embodiment of the present invention provides a method for configuring the capacity of new energy equipment based on a graph neural network, comprising: Constructing a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; The directed weighted graph structure is updated based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition graph structure; Modeling the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjusting parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; Based on the second multimodal graph neural network model, multimodal feature fusion is performed on the time-varying graph structure to obtain a corresponding embedding vector set; Based on the embedded vector set, a capacity planning model is constructed with the goal of matching the power generation of the target new energy device group with the power demand, and the capacity planning model is solved to obtain a distributed capacity configuration strategy for the target new energy device group.

[0006] Preferably, the constructing of a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group includes: Acquire topological data of the target new energy device group, and determine a corresponding physical connection topological graph based on the topological data, wherein the topological data includes connection relationships and energy flow directions between nodes in the target new energy device group; Obtain node static attribute data of the target new energy device group, and weight the physical connection topology graph based on the node static attribute data to obtain a corresponding directed weighted graph structure, wherein the node static attribute data includes the geographical location and power generation performance data of each node in the target new energy device group.

[0007] Preferably, the step of acquiring the node static attribute data of the target new energy equipment group and weighting the physical connection topology graph based on the node static attribute data to obtain a corresponding directed weighted graph structure includes: Calculating the distance between nodes in the target new energy equipment group based on the geographical location of each node; Calculating the energy transmission intensity between nodes in the target new energy equipment group based on the distance between the nodes and the power generation performance data of each node; The corresponding directed edges in the physical connection topology graph are weighted based on the energy transmission intensity between the nodes to obtain a directed weighted graph structure.

[0008] Preferably, the updating of the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain the corresponding time-varying composition graph structure includes: Acquire real-time operation data of each node in the target new energy equipment group and real-time meteorological data of the area where the target new energy equipment group is located; Time-aligning the real-time operation data and the real-time meteorological data of each node to obtain dynamic attribute data of each node; The directed weighted graph structure is updated in real time based on the dynamic attribute data of each node to obtain a corresponding time-varying graph structure.

[0009] Preferably, the modeling of the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjusting parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model, includes: Performing feature extraction on the directed weighted graph structure to obtain node features of each node and edge features of each edge, wherein the node features include power generation performance data, and the edge features include energy transmission intensity; Training a preset graph convolutional network based on the node features of each node and the edge features of each edge to obtain a first multimodal graph neural network model; Extracting features from the time-varying graph structure to obtain real-time node features of each node and real-time edge features of each edge, wherein the real-time node features include real-time operation data and real-time meteorological data, and the real-time edge features include real-time energy transmission intensity; Based on the real-time node features of each node and the real-time edge features of each edge, the parameters of the first multimodal graph neural network model are adjusted to obtain a second multimodal graph neural network model.

[0010] Preferably, the step of adjusting parameters of the first multimodal graph neural network model based on the real-time node feature of each node and the real-time edge feature of each edge to obtain a second multimodal graph neural network model comprises: Based on the real-time node features of each node and the real-time edge features of each edge, an incremental learning mechanism is used to adjust the parameters of the first multimodal graph neural network model to obtain a second multimodal graph neural network model.

[0011] Preferably, the multimodal feature fusion of the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set includes: Extracting data from the time-varying composition graph structure to obtain multimodal data of each node, wherein the multimodal data includes real-time operation data and real-time meteorological data; The real-time operation data and the real-time meteorological data of each node are input into the second multimodal graph neural network model for modality alignment and semantic mapping to obtain a corresponding embedding vector set.

[0012] Preferably, based on the embedded vector set, a capacity planning model is constructed with the goal of matching the power generation of the target new energy device group with the power demand, and the capacity planning model is solved to obtain a distributed capacity configuration strategy for the target new energy device group, including: Performing data extraction on the embedded vector set to obtain the power generation efficiency of each node in the target new energy equipment group; Based on the power generation efficiency of each node, construct an objective function with the goal of minimizing the deviation between the power generation and the power demand of the target new energy equipment group, and characterize the objective function as the capacity planning model; The capacity planning model is solved by a solver based on preset constraints to obtain a capacity configuration strategy for each node in the target new energy equipment group, wherein the preset constraints include a non-negative capacity constraint and a maximum capacity constraint.

[0013] Preferably, the capacity planning model is characterized by the following formula: in, represents the power generation efficiency of the ith node, represents the planned capacity of the i-th node, Indicates the electricity demand, Indicates the number of nodes.

[0014] In a second aspect, an embodiment of the present invention provides a new energy equipment capacity configuration system based on a graph neural network, comprising: A static structure determination module, used to construct a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; A dynamic structure determination module, used for updating the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition graph structure; A parameter adjustment module, used to model the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjust the parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; A multimodal fusion module, used for performing multimodal feature fusion on the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set; The capacity configuration strategy determination module is used to construct a capacity planning model based on the embedded vector set with the goal of matching the power generation of the target new energy equipment group with the power demand, and solve the capacity planning model to obtain the distributed capacity configuration strategy of the target new energy equipment group.

[0015] Compared with the prior art, the method and system for configuring the capacity of new energy equipment based on graph neural network in the embodiment of the present invention have the following beneficial effects: a directed weighted graph structure is constructed according to the topological data of the target new energy equipment group, which lays a structured foundation for subsequent analysis and can intuitively present the connection and weight relationship between devices; the real-time operation data updates the directed weighted graph structure to generate a time-varying graph structure, which can effectively capture the dynamic changes of the operation status of the equipment group over time; the second multimodal graph neural network model is obtained by adjusting parameters based on the time-varying graph structure, which improves the model's fitting and prediction capabilities for dynamic data, and the embedded vector set obtained by the model through multimodal feature fusion of the time-varying graph structure integrates the key information of the equipment group, providing strong data support for capacity planning; a capacity planning model is constructed with the goal of matching power generation with power demand, and a distributed capacity configuration strategy is obtained by solving it, which can effectively improve the matching degree between the power generation of the new energy equipment group and the power demand, thereby enhancing the stability and reliability of the entire energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for configuring the capacity of new energy equipment based on a graph neural network according to an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a new energy equipment capacity configuration system based on a graph neural network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In the description of the present invention, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0019] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0020] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a new energy equipment capacity configuration method based on a graph neural network, comprising the steps of: S1. Construct a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; Specifically, step S1 includes: 1) Obtain the topological data of the target new energy equipment group, and determine the corresponding physical connection topological diagram based on the topological data; Specifically, the topological data includes the connection relationship and energy flow direction between the nodes in the target new energy device group. The topological data can be obtained from the topological diagram, connection document or monitoring system of the target new energy device group. It should be noted that each device in the target new energy device group can be regarded as a node, and the "node" described in the present invention can be understood as a new energy device. Furthermore, the physical connection topological diagram corresponding to the target new energy device group is determined based on the topological data.

[0022] 2) Obtain the node static attribute data of the target new energy equipment group, and weight the physical connection topology graph based on the node static attribute data to obtain the corresponding directed weighted graph structure.

[0023] Specifically, the node static attribute data includes the geographical location and power generation performance data of each node in the target new energy equipment group.

[0024] Further, step 2) includes: i) calculating the distance between nodes in the target new energy equipment group based on the geographical location of each node; ii) Calculate the energy transmission intensity between nodes in the target new energy equipment group based on the distance between nodes and the power generation performance data of each node; The energy transmission intensity is related to the power generation performance data and is inversely proportional to the distance between nodes. Specifically, the energy transmission intensity is proportional to the power generation power of the node and inversely proportional to the square of the distance between the nodes. Based on this correspondence, the energy transmission intensity between nodes in the target new energy equipment group can be determined.

[0025] iii) Based on the energy transmission intensity between nodes, the corresponding directed edges in the physical connection topology graph are weighted to obtain a directed weighted graph structure.

[0026] The energy transmission intensity between nodes is represented as the weight of the corresponding directed edge. If the energy transmission intensity between two nodes is large, the weight of the edge is large; conversely, if the energy transmission intensity between two nodes is small, the weight of the edge is small.

[0027] S2. updating the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain the corresponding time-varying composition graph structure; Specifically, step S2 includes: 1) Obtain the real-time operation data of each node in the target new energy equipment group and the real-time meteorological data of the area where the target new energy equipment group is located; Real-time operating data includes the output power and operating speed of the node, and real-time meteorological data includes light intensity, wind speed, temperature and humidity.

[0028] 2) Time-align the real-time operation data and real-time meteorological data of each node to obtain the dynamic attribute data of each node; It can be understood that the dynamic attribute data of each node includes operation data and meteorological data at different time points.

[0029] 3) Based on the dynamic attribute data of each node, the directed weighted graph structure is updated in real time to obtain the corresponding time-varying graph structure.

[0030] Based on the dynamic attribute data of each node, each node and each edge in the directed weighted graph is updated respectively to obtain the corresponding time-varying graph structure. It can be understood that when the attributes of the nodes change, the energy transmission intensity between the nodes, that is, the weight of the edge, will also change accordingly.

[0031] S3, modeling the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjusting parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; Specifically, step S3 includes: 1) Extract features from the directed weighted graph structure to obtain the node features of each node and the edge features of each edge; Node features include power generation performance data, and edge features include energy transmission intensity.

[0032] 2) Based on the node features of each node and the edge features of each edge, the preset graph convolutional network is trained to obtain the first multimodal graph neural network model; Specifically, the training process includes: i) Data partitioning The extracted node features of each node and edge features of each edge are divided into a training set, a validation set, and a test set. Specifically, in this embodiment, the division is performed in a ratio of 70%, 15%, and 15%.

[0033] ii) Choosing a Graph Neural Network The preset graph convolutional network updates the feature representation of nodes by aggregating the adjacent information of nodes, and is more suitable for processing directed weighted graph structures.

[0034] iii) Model training The preset graph convolutional network is trained multiple times on the training set. The value of the loss function is calculated in each iteration, and the parameters of the preset graph convolutional network are updated using the optimizer. At the same time, verification is performed on the validation set to prevent overfitting. Finally, the performance of the preset graph convolutional network is evaluated on the test set to obtain the first multimodal graph neural network.

[0035] 3) Extract features from the time-varying graph structure to obtain real-time node features of each node and real-time edge features of each edge; Real-time node features include real-time operation data and real-time meteorological data, and real-time edge features include real-time energy transmission intensity.

[0036] 4) Based on the real-time node features of each node and the real-time edge features of each edge, the parameters of the first multimodal graph neural network model are adjusted to obtain a second multimodal graph neural network model.

[0037] Specifically, based on the real-time node features of each node and the real-time edge features of each edge, an incremental learning mechanism is used to adjust the parameters of the first multimodal graph neural network model to obtain a second multimodal graph neural network model. It should be noted that the adjusted parameters include the connection weights between the network layers in the first multimodal graph neural network model and the bias parameters in each network layer. The incremental learning mechanism gradually integrates the real-time node features of each node and the real-time edge features of each edge into the model to avoid the computational burden caused by one-time retraining. This approach improves the adaptability of the model to dynamic environments and reduces resource waste.

[0038] S4, performing multimodal feature fusion on the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set; Specifically, step S4 includes: 1) Extract data from the time-varying graph structure to obtain multimodal data for each node; Multimodal data includes real-time operational data and real-time meteorological data.

[0039] 2) The real-time operation data and real-time meteorological data of each node are input into the second multimodal graph neural network model for modality alignment and semantic mapping to obtain the corresponding embedding vector set.

[0040] The second multimodal graph neural network model is used to cross-fuse the multimodal data of each node. By performing modal alignment and semantic mapping on the multimodal data of each node, the node features of different modalities are mapped to a unified semantic embedding space, i.e., an embedding vector set, thereby effectively capturing the interaction modes and coordination laws of heterogeneous new energy devices at different scales.

[0041] S5. Based on the embedded vector set, a capacity planning model is constructed with the goal of matching the power generation of the target new energy equipment group with the power demand, and the capacity planning model is solved to obtain the distributed capacity configuration strategy of the target new energy equipment group.

[0042] Specifically, step S5 includes: 1) Extract data from the embedded vector set to obtain the power generation efficiency of each node in the target new energy equipment group; The embedded vector set includes multimodal feature information of the target new energy equipment group, which can be used to assist capacity planning by extracting the power generation efficiency.

[0043] 2) Based on the power generation efficiency of each node, an objective function is constructed with the goal of minimizing the deviation between the power generation and power demand of the target new energy equipment group, and the objective function is represented as a capacity planning model; Specifically, the model building process includes: i) Define variables The decision variables are the planned capacity of each node in the target new energy equipment group, and the parameters include the power generation efficiency, power demand and maximum allowable construction capacity of each node.

[0044] ii) Constructing the objective function The goal is to minimize the deviation between the power generation and power demand of the target new energy equipment group. Specifically, the following formula is used to characterize the capacity planning model: in, represents the power generation efficiency of the ith node, represents the planned capacity of the i-th node, Indicates the electricity demand, Indicates the number of nodes.

[0045] iii) Determine the constraints The preset constraints include a capacity non-negative constraint and a maximum capacity constraint. Specifically, the capacity non-negative constraint is that the planned capacity of each node cannot be a negative number, and the maximum capacity constraint is that the planned capacity of each node cannot exceed its maximum allowed construction capacity.

[0046] 3) Based on the preset constraints, the solver is used to solve the capacity planning model to obtain the capacity configuration strategy for each node in the target new energy equipment group.

[0047] Since solving the above capacity planning model is a linear programming problem, this embodiment uses the Gurobi solver to solve the capacity planning model to obtain the capacity configuration strategy of each node in the target new energy equipment group.

[0048] The embodiment of the present invention is a new energy equipment capacity configuration method based on a graph neural network. A directed weighted graph structure is constructed according to the topological data of a target new energy equipment group, which lays a structured foundation for subsequent analysis and can intuitively present the connection and weight relationship between devices. The real-time operation data updates the directed weighted graph structure to generate a time-varying graph structure, which can effectively capture the dynamic changes of the operation status of the equipment group over time. The second multimodal graph neural network model is obtained by adjusting parameters based on the time-varying graph structure, which improves the model's ability to fit and predict dynamic data, and the embedded vector set obtained by the model through multimodal feature fusion of the time-varying graph structure integrates the key information of the equipment group, providing strong data support for capacity planning. The capacity planning model is constructed with the goal of matching power generation with power demand, and the distributed capacity configuration strategy is obtained by solving it, which can effectively improve the matching degree between the power generation of the new energy equipment group and the power demand, thereby enhancing the stability and reliability of the entire energy system.

[0049] Based on the above-mentioned new energy equipment capacity configuration method based on graph neural network, Figure 2 As shown, an embodiment of the present invention provides a new energy equipment capacity configuration system based on a graph neural network, including: The static structure determination module 1 is used to construct a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; A dynamic structure determination module 2 is used to update the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition graph structure; A parameter adjustment module 3 is used to model the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjust the parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; A multimodal fusion module 4, configured to perform multimodal feature fusion on the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set; The capacity configuration strategy determination module 5 is used to construct a capacity planning model based on the embedded vector set with the goal of matching the power generation of the target new energy equipment group with the power demand, and solve the capacity planning model to obtain the distributed capacity configuration strategy of the target new energy equipment group.

[0050] It should be noted that each module in the above-mentioned new energy equipment capacity configuration system based on graph neural network can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a new energy equipment capacity configuration system based on graph neural network, please refer to the definition of a new energy equipment capacity configuration method based on graph neural network above. The two have the same functions and effects, which will not be repeated here.

[0051] In summary, the embodiment of the present invention is a new energy equipment capacity configuration method and system based on graph neural network, which constructs a directed weighted graph structure according to the topological data of the target new energy equipment group, lays a structured foundation for subsequent analysis, and can intuitively present the connection and weight relationship between devices; the real-time operation data updates the directed weighted graph structure to generate a time-varying graph structure, which can effectively capture the dynamic changes of the operation status of the equipment group over time; based on the time-varying graph structure, the parameters are adjusted to obtain the second multimodal graph neural network model, which improves the model's fitting and prediction capabilities for dynamic data, and the embedded vector set obtained by the model through multimodal feature fusion of the time-varying graph structure integrates the key information of the equipment group, providing strong data support for capacity planning; the capacity planning model is constructed with the goal of matching power generation with power demand, and the distributed capacity configuration strategy is obtained by solving, which can effectively improve the matching degree between the power generation and power demand of the new energy equipment group, thereby enhancing the stability and reliability of the entire energy system.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A new energy equipment capacity configuration method based on graph neural network, characterized in that: include: Construct a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; The directed weighted graph structure is updated based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition graph structure; Modeling the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjusting parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; Based on the second multimodal graph neural network model, multimodal feature fusion is performed on the time-varying graph structure to obtain a corresponding embedding vector set; Based on the embedded vector set, a capacity planning model is constructed with the goal of matching the power generation of the target new energy equipment group with the power demand, and the capacity planning model is solved to obtain a distributed capacity configuration strategy for the target new energy equipment group.

2. The new energy equipment capacity configuration method according to claim 1, characterized in that: The corresponding directed weighted graph structure is constructed based on the topological data of the target new energy equipment group, including: Acquire topological data of the target new energy device group, and determine a corresponding physical connection topological graph based on the topological data, wherein the topological data includes connection relationships and energy flow directions between nodes in the target new energy device group; Obtain node static attribute data of the target new energy device group, and weight the physical connection topology graph based on the node static attribute data to obtain a corresponding directed weighted graph structure, wherein the node static attribute data includes the geographical location and power generation performance data of each node in the target new energy device group.

3. The new energy equipment capacity configuration method according to claim 2, characterized in that: The acquiring of the node static attribute data of the target new energy equipment group, and weighting the physical connection topology graph based on the node static attribute data to obtain a corresponding directed weighted graph structure, includes: Calculating the distance between nodes in the target new energy equipment group based on the geographical location of each node; Calculating the energy transmission intensity between nodes in the target new energy equipment group based on the distance between the nodes and the power generation performance data of each node; The corresponding directed edges in the physical connection topology graph are weighted based on the energy transmission intensity between the nodes to obtain a directed weighted graph structure.

4. The new energy equipment capacity configuration method according to claim 1, characterized in that: The updating of the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying graph structure includes: Acquire real-time operation data of each node in the target new energy equipment group and real-time meteorological data of the area where the target new energy equipment group is located; Time-aligning the real-time operation data and the real-time meteorological data of each node to obtain dynamic attribute data of each node; The directed weighted graph structure is updated in real time based on the dynamic attribute data of each node to obtain a corresponding time-varying graph structure.

5. The new energy equipment capacity configuration method according to claim 1, characterized in that: The step of modeling the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjusting parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model includes: Performing feature extraction on the directed weighted graph structure to obtain node features of each node and edge features of each edge, wherein the node features include power generation performance data, and the edge features include energy transmission intensity; Training a preset graph convolutional network based on the node features of each node and the edge features of each edge to obtain a first multimodal graph neural network model; Extracting features from the time-varying graph structure to obtain real-time node features of each node and real-time edge features of each edge, wherein the real-time node features include real-time operation data and real-time meteorological data, and the real-time edge features include real-time energy transmission intensity; Based on the real-time node features of each node and the real-time edge features of each edge, the parameters of the first multimodal graph neural network model are adjusted to obtain a second multimodal graph neural network model.

6. The new energy equipment capacity configuration method according to claim 5, characterized in that: The step of adjusting parameters of the first multimodal graph neural network model based on the real-time node feature of each node and the real-time edge feature of each edge to obtain a second multimodal graph neural network model includes: Based on the real-time node features of each node and the real-time edge features of each edge, an incremental learning mechanism is used to adjust the parameters of the first multimodal graph neural network model to obtain a second multimodal graph neural network model.

7. The new energy equipment capacity configuration method according to claim 1, characterized in that: The step of performing multimodal feature fusion on the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set includes: Extracting data from the time-varying composition graph structure to obtain multimodal data of each node, wherein the multimodal data includes real-time operation data and real-time meteorological data; The real-time operation data and the real-time meteorological data of each node are input into the second multimodal graph neural network model for modality alignment and semantic mapping to obtain a corresponding embedding vector set.

8. The new energy equipment capacity configuration method according to claim 1, characterized in that: The method of constructing a capacity planning model based on the embedded vector set with the power generation of the target new energy equipment group matching the power demand as the goal, and solving the capacity planning model to obtain a distributed capacity configuration strategy for the target new energy equipment group includes: Performing data extraction on the embedded vector set to obtain the power generation efficiency of each node in the target new energy equipment group; Based on the power generation efficiency of each node, construct an objective function with the goal of minimizing the deviation between the power generation and the power demand of the target new energy equipment group, and characterize the objective function as the capacity planning model; The capacity planning model is solved by a solver based on preset constraints to obtain a capacity configuration strategy for each node in the target new energy equipment group, wherein the preset constraints include a non-negative capacity constraint and a maximum capacity constraint.

9. The new energy equipment capacity configuration method according to claim 8, characterized in that: The capacity planning model is characterized by the following formula: in, represents the power generation efficiency of the i-th node, represents the planned capacity of the i-th node, Indicates the electricity demand, Indicates the number of nodes.

10. A new energy equipment capacity configuration system based on graph neural network, characterized in that: include: A static structure determination module, used to construct a corresponding directed weighted graph structure based on the topological data of the target new energy equipment group; A dynamic structure determination module, used to update the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition graph structure; A parameter adjustment module, used to model the directed weighted graph structure to obtain a first multimodal graph neural network model, and adjust the parameters of the first multimodal graph neural network model based on the time-varying graph structure to obtain a second multimodal graph neural network model; A multimodal fusion module, used for performing multimodal feature fusion on the time-varying graph structure based on the second multimodal graph neural network model to obtain a corresponding embedding vector set; The capacity configuration strategy determination module is used to construct a capacity planning model based on the embedded vector set with the goal of matching the power generation of the target new energy equipment group with the power demand, and solve the capacity planning model to obtain the distributed capacity configuration strategy of the target new energy equipment group.

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