Hierarchical architecture optimization method, device and equipment of virtual power plant and medium

Abstract

The application relates to the technical field of virtual power plant architecture optimization, and discloses a layered architecture optimization method, device, equipment and medium for a virtual power plant, which comprises the following steps: constructing an initial control architecture of the virtual power plant, wherein a plurality of third nodes on a third level are used to represent first-type users and second-type users; identifying key hubs based on hub topology connection relationships and economic value data of second nodes to determine hub nodes used to represent key aggregator platforms; identifying system decoupling to determine decoupling nodes, and taking a plurality of third nodes connected with the decoupling nodes as autonomous nodes used to represent the first-type users; under the condition that the hub nodes and the autonomous nodes remain unchanged, optimizing to-be-adjusted nodes to obtain a target control architecture of the virtual power plant; the to-be-adjusted nodes are other nodes in the initial control architecture except the hub nodes and the autonomous nodes, and the reliability and stability of the virtual power plant during user resource scheduling are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant architecture optimization, and particularly relates to a layered architecture optimization method, device and equipment of a virtual power plant and a medium. BACKGROUND

[0002] In a new power system, a virtual power plant promotes optimized allocation of power resources, maintains normal power supply and use, meets the demand of the whole society for power, and provides reliable power guarantee for social and economic development and people's life. In related technologies, there is a virtual power plant, that is, an aggregator organizes resources and responds mainly by adjusting load. A virtual power plant pilot controls user resources, distributed energy and energy storage devices in real time through a dispatching operation control platform, and the network architecture is relatively simple, and the individualized use of user resources is not reflected enough. SUMMARY

[0003] The present application provides a layered architecture optimization method, device and equipment of a virtual power plant, which overcomes the technical problem that the individualized use of user resources by a virtual power plant is not reflected enough, and realizes the reliability and stability of a virtual power plant when scheduling user resources.

[0004] In order to achieve the above purpose, the main technical scheme adopted by the present application includes:

[0005] In a first aspect, the present application provides a layered architecture optimization method of a virtual power plant, comprising:

[0006] An initial control architecture of a virtual power plant is constructed, the initial control architecture comprising a first level, a second level and a third level; a first node on the first level is used to represent a regional regulation and control management platform, a plurality of second nodes on the second level are used to represent aggregator platforms, and a plurality of third nodes on the third level are used to represent first type users and second type users; wherein the volume of an aggregator platform to which the first type users belong is greater than the volume of an aggregator platform to which the second type users belong;

[0007] Based on a hub topology connection relationship and economic value data of the second nodes, key hubs are identified from the second nodes to determine a hub node representing a key aggregator platform from the plurality of second nodes;

[0008] Based on the hub topology connection relationship, system decoupling is identified from the second nodes to determine a decoupling node from the plurality of second nodes, and a plurality of third nodes connected to the decoupling node are used as autonomous nodes representing the first type users;

[0009] Optimizing the to-be-adjusted nodes while maintaining the hub nodes and the autonomous nodes unchanged to obtain a target control architecture of the virtual power plant; wherein the to-be-adjusted nodes are other nodes in the initial control architecture except the hub nodes and the autonomous nodes.

[0010] The application constructs and optimizes the control architecture, taking the second level as the core, constructs a regional regulation and management platform, a virtual power plant (aggregator), and a multi-layer interactive architecture of adjustable user resources. On the one hand, the application identifies key hubs for the second nodes to determine hub nodes, identifies system decoupling for the second nodes to determine autonomous nodes, and optimizes the second nodes and the third nodes in the first stage, and on the other hand, the application optimizes the to-be-adjusted nodes in the second stage while maintaining the hub nodes and the autonomous nodes unchanged. Through the two-stage optimization, appropriate user resources are selected for the aggregator platform. The problem that the virtual power plant does not sufficiently reflect the individualized utilization of user resources in the related art is overcome, and the reliability and stability of the virtual power plant when scheduling user resources are realized. The system cost, system safety, and regulation requirements of the virtual power plant are considered, and reasonable node links are optimized, so that the flexible use and safe and efficient connection of adjustable resources are effectively realized.

[0011] Optionally, the key hub identification for the second nodes based on the hub topology connection relationship and the economic value data of the second nodes comprises:

[0012] calculating a comprehensive hub score of the second nodes based on the hub topology connection relationship and the economic value data of the second nodes;

[0013] determining a node with the comprehensive hub score within a preset comprehensive hub score range as a hub node for representing a key aggregator platform.

[0014] Optionally, the system decoupling identification for the second nodes based on the hub topology connection relationship comprises:

[0015] judging the second nodes based on the hub topology connection relationship, and determining a second node as a decoupling node in the plurality of second nodes if the number of user resources connected by the second node is greater than or equal to a preset decoupling resource number.

[0016] Optionally, the optimization of the to-be-adjusted nodes comprises:

[0017] setting a comprehensive target value of the initial control architecture based on safety indexes, economic indexes, and comprehensive indexes of each node in the initial control architecture;

[0018] The to-be-adjusted nodes are optimized until the comprehensive target value meets a preset target value.

[0019] Optionally, the setting of the comprehensive target value of the initial control architecture based on the security index, the economic index and the comprehensive index of each node in the initial control architecture comprises: the comprehensive target value is calculated by the following formula:

[0020]

[0021] In the formula, E is the comprehensive target value of the initial control architecture, W1 is a security index weighting coefficient, S i is the security index of each node i, W2 is an economic index weighting coefficient, P i is the economic index of each node i, W3 is a comprehensive index weighting coefficient, O i is the comprehensive index of each node i.

[0022] Optionally, the optimization of the to-be-adjusted nodes comprises: adjusting the positions of the to-be-adjusted nodes in the initial control architecture and the connection relationships between the to-be-adjusted nodes.

[0023] Optionally, the construction of the initial control architecture of the virtual power plant comprises: clustering based on the obtained user resource data, and constructing the initial control architecture of the virtual power plant based on the clustering result, the clustering result comprising two or more of distributed energy, electric vehicles, energy storage devices, commercial loads and industrial loads.

[0024] Further, the users and aggregators of the virtual power plant are networked based on clustering and comprehensive comparison of the obtained user resource data, the users of the virtual power plant are clustered according to five typical resource characteristics, the market requirements of user-side demand are taken as input conditions, each type of load aggregator is taken as a basis, the initial control architecture of the virtual power plant is constructed based on the clustering result, a hierarchical tree-like decentralized collaborative control and communication structure is established as the initial control architecture, and a basis is provided for subsequent optimization of the target control architecture.

[0025] In a second aspect, an embodiment of the present application provides a layered architecture optimization device of a virtual power plant, comprising a construction module, a hub module, an autonomous module and an optimization module.

[0026] The construction module is configured to construct an initial control architecture of a virtual power plant, the initial control architecture comprising a first level, a second level and a third level; a first node on the first level is configured to represent a regional regulation and control management platform, a plurality of second nodes on the second level are configured to represent aggregator platforms, and a plurality of third nodes on the third level are configured to represent first-type users and second-type users; wherein the volume of an aggregator platform to which the first-type users belong is greater than the volume of an aggregator platform to which the second-type users belong.

[0027] The hub module is used to identify key hubs for the second node based on the hub topology connection relationship and the economic value data of the second node, so as to determine the hub node used to characterize the key aggregator platform among the plurality of second nodes.

[0028] The autonomous module is used to perform system decoupling identification on the second node based on the hub topology connection relationship, so as to determine the decoupling node among the plurality of second nodes, and to use the plurality of third nodes connected to the decoupling node as autonomous nodes for characterizing the first type of user;

[0029] The optimization module is used to optimize the nodes to be adjusted while keeping the hub node and the autonomous node unchanged, so as to obtain the target control architecture of the virtual power plant; wherein, the nodes to be adjusted are other nodes in the initial control architecture other than the hub node and the autonomous node.

[0030] Thirdly, embodiments of this application provide a computer device, including:

[0031] The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the aforementioned hierarchical architecture optimization method for a virtual power plant.

[0032] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the above-described hierarchical architecture optimization method for a virtual power plant. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a hierarchical architecture optimization method for a virtual power plant according to an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of the control architecture of a virtual power plant according to an embodiment of this application;

[0036] Figure 3 This is a step diagram of the hierarchical architecture optimization method for virtual power plants in this application embodiment;

[0037] Figure 4 This is a step diagram of the second method for optimizing the hierarchical architecture of a virtual power plant in this application embodiment;

[0038] Figure 5 This is a schematic diagram of a hierarchical architecture optimization device for a virtual power plant according to an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] The key concepts and technologies involved in this application are explained below:

[0041] A virtual power plant refers to a virtual consortium constructed using advanced communication, measurement, and control technologies and software systems. It enables the clustering and optimized control of resources such as distributed power sources, energy storage, controllable loads, and electric vehicles, and participates as a whole in grid dispatching and electricity market transactions. Its internal resources are spatially distributed rather than a centralized physical entity, hence the term "virtual"; however, it functions collaboratively as a whole, achieving the same functions as a traditional power plant, hence the term "power plant."

[0042] An energy system is the entire process of transforming various energy resources into specific forms of energy services (effective energy) for production and daily life. It is a subsystem of the socio-economic system abstracted to study the laws governing energy conversion and use. It typically consists of a series of technological processes and equipment related to supply, conversion, storage, transmission and distribution, use, and environmental protection.

[0043] Electricity aggregators are service providers that aggregate customer-side electricity loads to achieve more efficient electricity operation and utilization. Their main functions include load forecasting, dispatching, and control. They use technology to aggregate dispersed, small-scale electricity loads, enabling energy and electricity trading through virtual power plants and assisting users in participating in the electricity market.

[0044] Distributed energy refers to a comprehensive energy utilization system distributed at the user end. Primary energy is supplemented by gaseous fuels or renewable energy, utilizing all available resources. Secondary energy is mainly distributed at the user end through combined heat and power (CHP) and cooling (CHP), connected to medium and low voltage distribution networks, supplemented by other central energy supply systems. This achieves cascaded energy utilization to directly meet the diverse needs of users, while providing support and supplementation through the central energy supply system.

[0045] Energy storage refers to the process of storing energy through a medium or device and releasing it when needed. Based on the energy storage method, energy storage can be divided into three categories: physical energy storage, chemical energy storage, and electromagnetic energy storage. Physical energy storage mainly includes pumped hydro storage, compressed air energy storage, and flywheel energy storage; chemical energy storage mainly includes lead-acid batteries, lithium-ion batteries, sodium-sulfur batteries, and flow batteries; and electromagnetic energy storage mainly includes supercapacitor energy storage and superconducting energy storage. Energy storage is widely distributed, allows for rapid and flexible regulation, and is also an important means of realizing virtual power plants.

[0046] User resources in a virtual power plant refer to the distributed energy resources participating in the virtual power plant, including distributed generation sources, controllable loads, microgrids, and electric vehicles. These resources can function as part of a power coordination and management system within the virtual power plant, enabling the aggregation and collaborative optimization of distributed energy resources to participate in the electricity market and grid operation as a specialized power plant. By rationally allocating and optimizing these user resources, the virtual power plant can alleviate grid pressure, provide local consumption capacity, and achieve more efficient energy utilization.

[0047] The communication structure of the virtual power plant communication network is based on existing power communication network resources, integrating existing demand response and load management systems. It collects high-frequency data from multiple sources and types, including transformer area data, distributed power source data, and electric vehicle charging station monitoring. Through clustering, different types of user resources are classified and combined, forming a clustered hierarchical management architecture to ensure secure and real-time transmission. It also allows for more flexible business models.

[0048] The business needs of virtual power plants extend beyond conventional power grids and distribution networks. In their interactive operation with the grid, they target different types of users, leading to the expansion of control-related services such as distributed power source control and demand response into medium- and low-voltage distribution networks. The business needs of virtual power plants are highly individualized and flexible, with participants aiming to reduce energy costs and improve energy efficiency.

[0049] Cyber-Physical Systems (CPS) are multi-dimensional, complex systems that integrate computing, networking, and the physical environment. Through the organic integration and deep collaboration of 3C (Computation, Communication, Control) technologies, CPS enables real-time sensing, dynamic control, and information services for large-scale engineering systems. The integrated design of computing, communication, and physical systems in CPS makes systems more reliable, efficient, and capable of real-time collaboration, and has significant and broad application prospects.

[0050] Complex networks typically consist of a large number of nodes and the connections between them. Complex network theory, among other things, is a mathematical and computational tool for studying network structure and dynamic behavior. This theory primarily focuses on the connections between nodes in various complex systems, the network topology, and the temporal evolution of these networks. It explores the general properties, structural characteristics, and dynamic behavior of these networks.

[0051] In new power systems, virtual power plants serve to optimize the allocation of power resources, maintain normal power supply and consumption, meet the electricity needs of the entire society, and provide reliable power security for socio-economic development and people's lives. Among related technologies, virtual power plants involve aggregators organizing resources and responding primarily with adjustable loads. Virtual power plant pilot projects use a dispatching and operation management platform to control user resources, distributed energy sources, and energy storage devices in real time. However, their grid structure is relatively simple, and they do not adequately reflect the personalized utilization of user resources.

[0052] Taking the invitation-based model as an example, a virtual power plant invites user load resources, targeting demand-side response. It then integrates dispatchable loads based on this target, prioritizing demand-side response for scheduling optimization. The virtual power plant provides technical support for monitoring user resources, rapid data aggregation and transmission, and the interconnection and data management of massive intelligent terminals.

[0053] This application provides a method for optimizing the hierarchical architecture of a virtual power plant, referring to... Figure 1 , Figure 1 This is a flowchart of a hierarchical architecture optimization method for a virtual power plant according to an embodiment of this application, including:

[0054] S100. Construct the initial control architecture of the virtual power plant. The initial control architecture includes a first level, a second level, and a third level. The first node in the first level is used to represent the regional control and management platform. Multiple second nodes in the second level are used to represent the aggregator platform. Multiple third nodes in the third level are used to represent the first type of user and the second type of user. The aggregator platform to which the first type of user belongs has a larger scale than the aggregator platform to which the second type of user belongs.

[0055] The first type of user refers to the users on the third level connected by aggregators with fewer users. The second type refers to the users on the third level connected by aggregators with more users. After optimization, as an autonomous node, the size of the aggregator platform refers to the users on the third level connected by the aggregator.

[0056] The initial control architecture of a virtual power plant consists of three layers. The third layer is the adjustable user resource layer, which communicates with other layers in real time with operational information, automatically receives and responds to control commands and price incentives issued by other layers (management platform and aggregator platform), and implements this through user resource-side terminals. The second layer is the virtual power plant (aggregator) level, responsible for dynamically aggregating various adjustable resources, receiving control commands issued by the management platform online and decomposing them to various adjustable user resources within the virtual power plant, receiving price signals from the electricity market, and reporting relevant data information to the power grid control center and the power trading center. The first layer is the regional control center, which interacts with the virtual power plant and aggregators to make information decisions.

[0057] Furthermore, a direct hard connection for information communication between aggregators and user resources is established. Taking into account the information transmission method, as well as the data transmission and computing capabilities of aggregator devices and user resource smart terminals, the control architecture is designed to initially form the aggregator-user resource initial control architecture.

[0058] S200: Based on the hub topology connection relationship and the economic value data of the second node, key hub identification is performed on the second node to determine the hub node used to represent the key aggregator platform among multiple second nodes. Key hub identification is based on the hub topology connection relationship and the economic value data of the second node. The hub topology connection relationship is determined by whether it is a hub location; if it is, it is 1, and if it is not, it is 0. The economic value data of the second node is determined based on the user's own resource attributes. Then, a comprehensive scoring method is used to determine the nodes whose scores rank in the top 10% or top 15% of the total nodes and identify them as hub nodes.

[0059] S300. Based on the hub topology connection relationship, the second node is decoupled and identified in the system to determine the decoupled node among multiple second nodes, and multiple third nodes connected to the decoupled node are used as autonomous nodes to represent the first type of user.

[0060] Reference Figure 2 , Figure 2 This is a schematic diagram of the control architecture of a virtual power plant in an embodiment of this application. The system decoupling identification is to determine whether the number of user resources connected to the aggregator platform is greater than or equal to the preset number of decoupling resources. If it is greater than or equal to the preset number of decoupling resources, it is determined as a decoupling node, and the decoupling node is decoupled from the system. In this process, the stability of the virtual power plant control architecture is maintained.

[0061] S400. While keeping the hub node and autonomous node unchanged, optimize the node to be adjusted to obtain the target control architecture of the virtual power plant; wherein, the node to be adjusted is the other node in the initial control architecture besides the hub node and autonomous node.

[0062] In the optimized target control architecture, the first type of user node connects to the first node at the first level, and the second type of user node connects to multiple second nodes at the second level.

[0063] Optimization target values ​​for the nodes to be adjusted are set, taking into account both safety and economic indicators. Safety indicators consider the impact of connection speed, communication reliability, and system reliability between nodes, while economic indicators consider the economic cost and overall benefits of the virtual power plant control architecture.

[0064] Based on complex network theory, and according to the network connection method and topology, the system calculates the network's ability to transmit information and resist emergencies, such as connection speed and system reliability. By calculating parameters such as investment, topology and technical equipment requirements, the system cost and comprehensive benefits are calculated, and an appropriate weighted system is selected as the comprehensive target value of the control architecture.

[0065] On the one hand, this application identifies key hubs in the second node to determine the hub node, and performs system decoupling identification on the second node to determine the autonomous node. It then performs a first-stage optimization on the second and third nodes. On the other hand, while keeping the hub node and autonomous node unchanged, this application performs a second-stage optimization on the node to be adjusted. Through these two stages of optimization, suitable user resources are selected for the aggregator platform.

[0066] This application constructs and optimizes a control architecture, with the second layer as the core, building a multi-layered interactive architecture comprising a regional control and management platform, virtual power plants (aggregators), and adjustable user resources. While ensuring the security of data transmission and storage, and guaranteeing system reliability, it optimizes reasonable node connections, taking into account system cost, maintainability, and the adjustment requirements of the virtual power plants. This effectively achieves flexible use and secure, efficient connection of adjustable resources, and the method presented in this application has universal applicability.

[0067] This application constructs a control architecture through clustering, performs aggregation optimization of the control architecture based on complex network theory, determines the selection of user resources at each level based on the optimal center distance method, and adopts different linking methods and control calculation strategies for different user resources to obtain an optimized control architecture result. This improves the reliability, stability and security of virtual power plant scheduling, while increasing the reasonable profit margin for aggregators and user resources.

[0068] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the steps of a hierarchical architecture optimization method for a virtual power plant in this application, including the following steps:

[0069] S001. Establish initial conditions for user resources aggregators, collect relevant resources, and aggregate user resources.

[0070] S002. Determine the key nodes, i.e. the hub nodes, and determine the distribution location of the aggregators corresponding to the hub nodes.

[0071] S003. Build the network architecture of the virtual power plant, i.e. the initial control architecture, evaluate the comprehensive evaluation score, score each node, optimize and adjust the layering and link relationships based on the scores of each node, and then optimize the network structure.

[0072] Furthermore, if the optimization of the space frame structure meets the first preset optimization condition, then the specific key nodes and edges are adjusted based on the optimized space frame structure; if not, then step S003 is repeated.

[0073] S004. Optimize the overall system, that is, optimize the target control architecture. If the optimized target control architecture does not meet the second preset optimization condition, repeat step S003. If it meets the condition, output the result and simulate the operation of the overall system to collaboratively optimize the target control architecture of the virtual power plant and output the regional control.

[0074] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the steps of the second method for optimizing the hierarchical architecture of a virtual power plant in this application, including the following steps:

[0075] S005. Initialize the user resource network and determine the variable conditions for linking each aggregator user, providing a basis for subsequent optimization operations based on the comprehensive target value.

[0076] S006. Calculate the network distance between each node, calculate and analyze the economic indicators and other indicators of each node, and output a comprehensive weighted value including security indicators, economic indicators and other indicators.

[0077] S007. Based on the comprehensive weighted value of each node, the virtual power plant network is initially optimized, and then the entire system is iterated, and the network distance and reliability are calculated.

[0078] S008. If the optimization result of the network architecture meets the third preset optimization condition, then the output structure is converged; otherwise, step S006 is repeated.

[0079] As one implementation of this application, key hub identification is performed on the second nodes based on hub topology connections and economic value data of the second nodes, in order to determine the hub node used to characterize the key aggregator platform among multiple second nodes, including:

[0080] The comprehensive hub score of the second node is calculated based on the hub topology connection relationship and the economic value data of the second node.

[0081] Nodes whose comprehensive hub scores fall within the preset comprehensive hub score range are identified as hub nodes used to characterize key aggregator platforms.

[0082] As one implementation of this application, system decoupling identification of second nodes is performed based on hub topology connections to determine decoupling nodes among multiple second nodes, including:

[0083] The second node is determined based on the hub topology connection relationship. If the number of user resources connected to a second node is greater than or equal to the preset number of decoupling resources, then the second node is determined as a decoupling node among multiple second nodes.

[0084] As one implementation of this application, the node to be adjusted is optimized, including:

[0085] The overall target value of the initial control architecture is set based on the security indicators, economic indicators, and comprehensive indicators of each node in the initial control architecture.

[0086] The nodes to be adjusted are optimized until the overall target value meets the preset target value. The link speed is varied from high to low to verify the optimization target value, and layered iterative calculations are performed. The overall target value is iteratively calculated using methods such as gradient descent to optimize the objective function.

[0087] As one implementation of this application, a comprehensive target value for the initial control architecture is set based on the security indicators, economic indicators, and comprehensive indicators of each node in the initial control architecture, including: the comprehensive target value is calculated by the following formula:

[0088]

[0089] In the formula, E is the overall target value of the initial control architecture, W1 is the weighting coefficient of the safety index, and S i Let W1 be the safety index for each node i, W2 be the weighting coefficient for the economic index, and P be the weighting coefficient for the economic index. i W3 represents the economic indicators for each node i, and W3 is the weighting coefficient of the comprehensive indicators. i This is the comprehensive index for each node i.

[0090] As one implementation of this application, optimizing the node to be adjusted includes: adjusting the position of the node to be adjusted in the initial control architecture and the connection relationship between each node to be adjusted.

[0091] Furthermore, S410, preliminary assessment of the control architecture, which includes a first level, a second level, and a third level. The first level represents the control center, the second level represents the aggregator platform, and the third level represents the users. The aggregator platform and users are regarded as nodes of the control architecture, and the relationships between them are regarded as edges of the control architecture.

[0092] The control architecture is labeled with hub nodes and autonomous nodes. Hub nodes are nodes that have a significant impact on network performance and stability. Hub nodes are aggregator platforms with the highest connectivity or the best geographical location. Autonomous nodes are large users with the highest connectivity or the best geographical location. The initial control architecture is analyzed, including the degree distribution of each node, the network clustering coefficient, and the average path length.

[0093] S420. Perform preliminary calculations of the security and economic risks of the control architecture, considering the security risks caused by subsequent changes in the control architecture, and the economic risks caused by changes in the distance between nodes in the control architecture. Understand the basic characteristics and existing problems of the control architecture.

[0094] S430. Optimize the control architecture. After identifying and determining the hub node and autonomous node, while keeping the hub node and autonomous node unchanged, define the link cost and link distance between each node in the control architecture. By changing the link cost and reducing the link distance between each node, optimize the position of the node to be adjusted in the initial control architecture, optimize the connection relationship between each node to be adjusted, and thus optimize the control architecture. This effectively improves the security and reliability of the virtual power plant center-aggregator-user architecture, generating an optimized control architecture.

[0095] S440. Implement optimization strategies, select appropriate optimization algorithms, such as genetic algorithms and ant colony algorithms, to optimize the control architecture, generate multiple possible control architecture optimization schemes, and select the optimization scheme that meets the requirements to optimize the control architecture.

[0096] Specifically, hub nodes are crucial for network connectivity and stability, but are also vulnerable to attacks or failures. Autonomous nodes handle large data transmission volumes, lack redundancy, and are overly reliant on single resources; failures in these nodes can lead to significant incidents. After identifying hub and autonomous nodes, the weakest nodes in the control architecture are optimized by increasing redundancy and reliable protection measures, adding reliable links and adopting more reliable communication methods, improving the system structure, and incorporating new iterative computations.

[0097] Furthermore, simulations were conducted under various typical operating conditions based on the different optimization requirements of aggregators (different adjustment speeds and adjustment ranges), and corresponding optimization strategies were provided according to different technical requirements.

[0098] S450. Evaluate the optimization effect. After optimizing the node to be adjusted, compare the scheduling performance of the control architecture before and after optimization to evaluate the optimization effect. If the optimization effect is not ideal, repeat steps S410 to S440.

[0099] Through the above steps, complex network theory is effectively utilized to optimize the hierarchical structure and mutual links between the regional control and management platform, the aggregator platform, and the users, thereby improving the performance and stability of the virtual power plant, and ultimately enhancing its security and economic benefits.

[0100] As one implementation of this application, constructing an initial control architecture for a virtual power plant includes: clustering user resource data based on acquired data, and constructing the initial control architecture for the virtual power plant based on the clustering results. The clustering results include two or more of the following: distributed energy resources, electric vehicles, energy storage devices, commercial loads, and industrial loads. Cluster analysis is performed on aggregators and user resources using key parameters such as user resource regulation capacity, linkage status, input conditions and costs, output characteristics, expected benefits, and economic requirements.

[0101] Furthermore, based on the acquired user resource data, clustering and comprehensive comparison are performed to network the users and aggregators of the virtual power plant. The users of the virtual power plant are clustered according to five typical resource characteristics. Combined with the market requirements of user-side demand as input conditions, and with various types of load aggregators as the foundation, the initial control architecture of the virtual power plant is constructed based on the clustering results. A hierarchical tree-like distributed collaborative control and communication structure is established as the initial control architecture, providing a foundation for the subsequent optimization of the target control architecture.

[0102] Furthermore, the hierarchical control architecture of this application is operable for distributed aggregators, improves computational and control efficiency, has good modularity and high integration, and can effectively improve the reliability of the entire virtual power plant. Corresponding software programs are set up to complete the structural configuration and optimization of the virtual power plant, massive user resources, and aggregators, enabling better implementation of aggregator control over user resources and achieving a comprehensive technical-economic balance of the virtual power plant system.

[0103] With the advancement of virtual power plants, the increasing number of user resources and the resulting complexity of resource characteristics have led to a shift in virtual power plants compared to related technologies that passively accept commands. The virtual power plant in this application utilizes different linking methods for communication to achieve varying response speeds and employs a hierarchical integrated control and scheduling strategy, thereby improving the reliability and stability of user resource scheduling. Simultaneously, it enables aggregators, as intermediaries within the virtual power plant, to more effectively obtain resource support, thereby increasing their own operational efficiency.

[0104] This application provides a hierarchical architecture optimization device for a virtual power plant, referring to... Figure 5 , Figure 5 This is a schematic diagram of a hierarchical architecture optimization device for a virtual power plant according to an embodiment of this application, including a construction module 100, a hub module 200, an autonomous module 300, and an optimization module 400;

[0105] Module 100 is used to construct the initial control architecture of the virtual power plant. The initial control architecture includes a first level, a second level, and a third level. The first node in the first level represents the regional control and management platform, multiple second nodes in the second level represent the aggregator platform, and multiple third nodes in the third level represent the first type of user and the second type of user. The aggregator platform to which the first type of user belongs has a larger scale than the aggregator platform to which the second type of user belongs.

[0106] Hub module 200 is used to identify key hubs of second nodes based on hub topology connections and economic value data of second nodes, in order to determine the hub node used to characterize the key aggregator platform among multiple second nodes.

[0107] The autonomous module 300 is used to identify the system decoupling of the second node based on the hub topology connection relationship, so as to determine the decoupling node among multiple second nodes, and to use multiple third nodes connected to the decoupling node as autonomous nodes to represent the first type of user.

[0108] The optimization module 400 is used to optimize the nodes to be adjusted while keeping the hub node and autonomous node unchanged, so as to obtain the target control architecture of the virtual power plant; wherein, the nodes to be adjusted are the other nodes in the initial control architecture other than the hub node and autonomous node.

[0109] As one embodiment of this application, the hub module 200 includes:

[0110] The comprehensive hub score of the second node is calculated based on the hub topology connection relationship and the economic value data of the second node;

[0111] Nodes whose comprehensive hub scores fall within a preset comprehensive hub score range are identified as hub nodes used to characterize key aggregator platforms.

[0112] As one embodiment of this application, the autonomous module 300 includes: judging the second node based on the hub topology connection relationship; if the number of user resources connected to a certain second node is greater than or equal to the preset number of decoupling resources, then the second node is determined as a decoupling node among the plurality of second nodes.

[0113] As one embodiment of this application, the optimization module 400 includes:

[0114] The overall target value of the initial control architecture is set based on the security indicators, economic indicators, and comprehensive indicators of each node in the initial control architecture;

[0115] The nodes to be adjusted are optimized until the overall target value meets the preset target value.

[0116] As one embodiment of this application, the optimization module 400 includes:

[0117] The comprehensive target value is calculated using the following formula:

[0118]

[0119] In the formula, E is the comprehensive target value of the initial control architecture, W1 is the weighting coefficient of the safety index, and S i Let W1 be the safety index for each node i, W2 be the weighting coefficient for the economic index, and P be the weighting coefficient for the economic index. i W3 represents the economic indicators for each node i, and W3 is the weighting coefficient of the comprehensive indicators. i This is the comprehensive index for each node i.

[0120] As one embodiment of this application, the optimization module 400 includes: adjusting the position of the node to be adjusted in the initial control architecture and the connection relationship between the nodes to be adjusted.

[0121] As one embodiment of this application, the construction module 100 includes: clustering based on acquired user resource data, and constructing an initial control architecture for a virtual power plant based on the clustering results. The clustering results in the construction module 100 include two or more of distributed energy resources, electric vehicles, energy storage devices, commercial loads, and industrial loads.

[0122] This application provides a computer device, including:

[0123] The memory and processor are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the aforementioned hierarchical architecture optimization method for a virtual power plant.

[0124] This application provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the aforementioned hierarchical architecture optimization of a virtual power plant.

[0125] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0126] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A hierarchical architecture optimization method for a virtual power plant, characterized in that, include: Construct an initial control architecture for a virtual power plant, the initial control architecture comprising a first level, a second level, and a third level; The first node at the first level is used to represent the regional regulation and management platform; the multiple second nodes at the second level are used to represent the aggregator platform; and the multiple third nodes at the third level are used to represent the first type of user and the second type of user. The aggregator platform to which the first type of user belongs has a larger scale than the aggregator platform to which the second type of user belongs. Based on the hub topology connection relationship and the economic value data of the second node, the second node is identified as a key hub to determine the hub node used to characterize the key aggregator platform among the plurality of second nodes. Based on the hub topology connection relationship, the second node is decoupled and identified in the system to determine the decoupled node among the plurality of second nodes. The plurality of third nodes connected to the decoupled node are used as autonomous nodes to represent the first type of user. Specifically, the second node is judged based on the hub topology connection relationship. If the number of user resources connected to a certain second node is greater than or equal to the preset number of decoupled resources, then the second node is determined as the decoupled node among the plurality of second nodes. While keeping the hub node and the autonomous node unchanged, the nodes to be adjusted are optimized to obtain the target control architecture of the virtual power plant; specifically, the comprehensive target value of the initial control architecture is set based on the safety indicators, economic indicators and comprehensive indicators of each node in the initial control architecture; the link relationship between the nodes to be adjusted is optimized until the comprehensive target value meets the preset target value; wherein, the nodes to be adjusted are other nodes in the initial control architecture besides the hub node and the autonomous node.

2. The method according to claim 1, characterized in that, The process of identifying key hubs for the second node based on hub topology connections and the economic value data of the second node, in order to determine the hub node used to characterize the key aggregator platform among the plurality of second nodes, includes: The comprehensive hub score of the second node is calculated based on the hub topology connection relationship and the economic value data of the second node; Nodes whose comprehensive hub scores fall within a preset comprehensive hub score range are identified as hub nodes used to characterize key aggregator platforms.

3. The method according to claim 1, characterized in that, The step of setting the comprehensive target value of the initial control architecture based on the security indicators, economic indicators, and comprehensive indicators of each node in the initial control architecture includes: the comprehensive target value is calculated by the following formula: In the formula, The overall target value of the initial control architecture is... The weighting coefficient for safety indicators For each node Safety indicators, The weighting coefficient for economic indicators. For each node Economic indicators The weighting coefficient for the comprehensive indicators. For each node A comprehensive indicator.

4. The method according to claim 1, characterized in that, The optimization of the node to be adjusted includes: adjusting the position of the node to be adjusted in the initial control architecture and the connection relationship between the nodes to be adjusted.

5. The method according to claim 1, characterized in that, The initial control architecture for constructing the virtual power plant includes: clustering based on acquired user resource data, and constructing the initial control architecture for the virtual power plant based on the clustering results. The clustering results include two or more of the following: distributed energy, electric vehicles, energy storage devices, commercial loads, and industrial loads.

6. A hierarchical architecture optimization device for a virtual power plant, characterized in that, It includes a building module, a hub module, an autonomous module, and an optimization module; The construction module is used to construct the initial control architecture of the virtual power plant. The initial control architecture includes a first level, a second level, and a third level. A first node on the first level is used to represent a regional control and management platform. Multiple second nodes on the second level are used to represent aggregator platforms. Multiple third nodes on the third level are used to represent first-type users and second-type users. The aggregator platform to which the first-type users belong has a larger scale than the aggregator platform to which the second-type users belong. The hub module is used to identify key hubs for the second node based on the hub topology connection relationship and the economic value data of the second node, so as to determine the hub node used to characterize the key aggregator platform among the plurality of second nodes. The autonomous module is used to perform system decoupling identification on the second node based on the hub topology connection relationship, so as to determine the decoupling node among the plurality of second nodes, and to use the plurality of third nodes connected to the decoupling node as autonomous nodes for characterizing the first type of user; specifically, the second node is judged based on the hub topology connection relationship, and if the number of user resources connected to a certain second node is greater than or equal to the preset number of decoupling resources, then the second node is determined as the decoupling node among the plurality of second nodes; The optimization module is used to optimize the nodes to be adjusted while keeping the hub node and the autonomous node unchanged, to obtain the target control architecture of the virtual power plant; specifically, it sets the comprehensive target value of the initial control architecture based on the safety indicators, economic indicators and comprehensive indicators of each node in the initial control architecture; it optimizes the link relationship between the nodes to be adjusted until the comprehensive target value meets the preset target value; wherein, the nodes to be adjusted are other nodes in the initial control architecture besides the hub node and the autonomous node.

7. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform a hierarchical architecture optimization method for a virtual power plant according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute a hierarchical architecture optimization method for a virtual power plant according to any one of claims 1 to 5.

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