1.1

Research background

In recent years, facing the problem of energy transition, the country has continuously promoted the development of renewable energy. In December 2020, the Central Economic Work Conference set the goal of “China’s carbon dioxide emissions strive to peak by 2030 and achieve carbon neutrality by 2060”. The 14th Five-Year Plan set the target of reducing carbon dioxide emissions per unit of GDP by 18%, implementing the target of Nationally Determined Contributions to address climate change by 2030, and anchor-setting efforts to achieve carbon neutrality by 2060. Relevant national policies indicate that “carbon peak, carbon neutrality” has become the core issue of China’s modernization, and the key to solve this problem lies in the effective development and utilization of photovoltaic, wind power and other new energy¹.

new technologies such as big data and digital twins have been gradually applied in China’s industrial field, but their application in the collaborative interaction between source network, load storage, and other aspects is still immature². The interaction mechanism and operation mode of high proportion distributed photovoltaic and other multi type, multi agent, source, load, and storage resources in the county energy internet are complex, and advanced technical means such as data mining, artificial intelligence, and digital twins are integrated to promote the integration and application of energy technology and information technology, breaking through strong fluctuations, multi-dimensional monitoring, real-time perception of information status, construction of multiple source device twins, and digital twin biological management Multiple technical bottlenecks such as collaborative interaction and deduction of multi-level digital twins of the energy internet, and the development of a county level energy internet source network load storage collaborative interactive digital twin system to support the digital construction of the energy internet, thereby guiding the safe and efficient operation of the county level energy internet physical system, improving the safe operation level of the power grid and the new energy consumption capacity^3-4.

1.2

Purpose and significance

Cluster analysis refers to the process of grouping collections of physical or abstract objects into classes of similar objects. The goal of cluster analysis is to collect data on a similar basis and classify it into different classes or clusters. Therefore, objects in the same cluster have great similarities, while objects in different clusters have great differences. The quality of clustering results is judged by the set property, including the separation between classes and the coupling within classes. Cluster analysis is one of the main tasks of data mining, which can be used as an independent tool to obtain the distribution of data, observe the characteristics of each cluster data, and focus on the specific cluster cooperation for further analysis. Traditional clustering algorithms can be divided into five categories: partition method, hierarchical method, density-based method, grid-based method and model-based method. When dealing with actual complex data, especially for high-dimensional data and large data, traditional clustering methods often fail. In this case, highdimensional clustering analysis emerges at the historic moment and becomes an important research direction of cluster analysis⁵.

Due to the wide variety, large scale, and diverse operational characteristics of county level energy internet source and load resources, cluster analysis is one of the methods used in the project for distributed source and load storage resource aggregation. Through distributed source and load storage resource aggregation, a virtual power plant is formed to participate in the interactive operation of the energy internet⁶.

Based on the spatiotemporal characteristics of distributed source and load resources within the county energy internet, and facing the requirements of distributed multi-level collaborative interaction of the county energy internet, a full process management mechanism and modeling technology suitable for the digital twin of the county energy internet are proposed, laying the foundation for achieving efficient collaborative interaction and operational deduction of the county energy internet based on the digital twin, Through the twin discovery authentication modeling mechanism, model consistency and scalability are achieved, improving the digital management capabilities of the energy internet, and helping to achieve distributed source load storage resources to participate in the interactive operation of the county level energy internet under the protection of privacy information.

1.3

Research level at home and abroad

In China, in terms of digital twin layered dynamic polymerization. Document⁷ describes the definition, composition, and characteristics of digital twins, and proposes the connotation of digital power grid based on digital twins for the first time. The concept, characteristics, and derivative relationship between digital power grid and smart grid are analyzed in detail. A digital power grid architecture including physical power grid, perception layer, transmission layer, data layer, platform layer, and application layer is constructed, and the key technologies of digital power grid are summarized. Based on the concept, basic architecture, and characteristics of digital twinning, the document ⁸ proposes the connotation of digital twinning power grids, constructs a framework for digital twinning power grids, explains the operation mode of digital twinning power grids, analyzes the key technologies of digital twinning power grids, and proposes typical applications of digital twinning technology in power grid enterprises from four levels: equipment layer, power grid layer, business layer, and operation management layer. The above documents have constructed digital twin power grid systems from different application levels under the same power grid, and have not conducted in-depth research on hierarchical dynamic aggregation of digital twins at different levels at the virtual power plant level.

In foreign countries, in terms of digital twin layered dynamic polymerization. Literature ⁹ proposes a six layer digital twin architecture and describes its prototype software implementation. The issues and principles of consumer infrastructure ontology modeling are described, including customer loads, low-voltage distribution network components, small power generation devices, and electrical energy storage devices. The digital twin model composed of the proposed method is used to calculate the optimal configuration of renewable energy hybrid power supply systems. Document ¹⁰ proposes a dynamic scheduling management method for digital twin simulation computing tasks in multiple energy systems. Based on the stable computing completion time of simulation computing tasks, a comprehensive priority index was designed to quantify the execution order of simulation computing tasks. On the basis of this comprehensive priority index, a task allocation mechanism aimed at minimizing the computing completion time of applications was proposed. The above literature proposes a hierarchical construction method for digital twins and a dynamic scheduling management method for digital twins, but does not consider the dynamic and hierarchical aggregation between digital twins.

2.1

Dynamic polymerization principle of source charge storage digital twins

First, combined with the goal of power grid safe operation and new energy consumption under the background of county energy Internet, the interactive business needs of different subjects under the power trading and operation regulation mode are studied. Then, based on the comprehensive analysis of the subject, topological relationship, spatio-temporal characteristics and other factors of various digital twins of source, load and storage. The principles of digital twin polymerization based on hierarchical polymerization, distributed polymerization, spatio-temporal dynamic polymerization and complementary regulatory polymerization are proposed.

In power trading, the interactive business needs of different subjects are as follows: power generation enterprises, power users, and power trading markets. In the power operation regulation mode, the interactive business demands among different subjects are as follows: power dispatching center, power generation enterprise and power user, and power market regulator.

The principle of dynamic aggregation of digital twins means that the modeling results should be updated and optimized over time to better reflect the actual situation and realize the dynamic interaction between digital twins and physical systems. This principle helps the digital twin always keep up-to-date data and accurate models, making it more realistic and more accurate to predict future performance. At the same time, it can also help the digital twin to carry out real-time data exchange and interaction between the actual equipment, so as to realize more adaptive, efficient and safe automated operation. The principles of digital twin polymerization based on different polymerization modes are as follows:

2.2

Dynamic aggregation technology of virtual power plant digital twins based on spatiotemporal dynamic characteristics of source network load storage

Firstly, the ownership subject, topological relationship, spatio-temporal characteristics and regulatory characteristics of device-level digital twins in the energy Internet are analyzed. Secondly, topological changes of device-level digital twins in different levels of county energy Internet are analyzed by dynamic topological analysis method. Then, dynamic coupling characteristics of device-level digital twins in different spatio-temporal scales are analyzed based on dynamic characteristics of source charge and storage. Finally, based on clustering, intelligent optimization and other analysis methods, the space-time dynamic polymerization technology of virtual power plant digital twin and complementary regulation polymerization technology are proposed.

Fig. 2.

Dynamic aggregation technology of virtual power plant digital twins based on spatiotemporal dynamic characteristics of source network load storage.

Device-level digital twin refers to a digital twin that is modeled and simulated based on a single device. In the energy Internet, device-level digital twins are typically created and used by:

Power generation enterprises: Power generation enterprises can use device-level digital twins for modeling and simulation of their power generation equipment to monitor equipment status in real time and optimize equipment operation strategies to improve power generation efficiency and reduce operating costs.

Power grid enterprises: Power grid enterprises can use equipment-level digital twins for modeling and simulation of their transmission equipment, so as to monitor equipment status in real time, manage and dispatch power operation, and ensure the safety and stability of the power grid.

Power users: Power users can use device-level digital twins to model and simulate their electrical equipment to monitor equipment status in real time and optimize energy consumption control strategies to reduce energy costs and improve efficiency.

The topological relationships of device-level digital twins are usually single point connections or point-to-point connections. In terms of spatio-temporal characteristics, device-level digital twins generally represent the real-time status and historical operation data of a single device, which can be analyzed in time domain, space domain or spatio-temporal combination. In terms of regulation characteristics, device-level digital twins can realize regulation and optimization of power equipment through real-time control of real-time data, such as improving power generation efficiency and reducing energy consumption of power equipment.

The dynamic topological analysis method is used to monitor and analyze the topological relationship between the devicelevel digital twins in the energy Internet in real time. The topological changes of the device hierarchy topology, the device group hierarchy topology, the station hierarchy topology, and the subsystem hierarchy topology are found. After understanding the topological changes between different levels of energy equipment, the dynamic coupling characteristics of the digital twins at the level of source, load and storage equipment in different spatio-temporal scales are analyzed. The dynamic coupling characteristics of source-load-storage device-level digital twins exist in different spatio-temporal scales, and the coupling characteristics are affected by the topological structure and running state of the energy equipment. In the time scale, the digital twin can represent the historical data and real-time state of energy equipment, while in the space scale, the digital twin can reflect the distance and correlation between different energy equipment. Therefore, the dynamic topological analysis method can be used to analyze the spatio-temporal dynamic characteristics of the source, load and storage device-level digital twins in different levels of county energy networks, so as to realize the dynamic coupling characteristics analysis of the source, load and storage device-level digital twins.

2.3

Hierarchical polymerization framework and process of multilevel virtual power plant digital twin

Firstly, according to the multi-level polymerization relationship between various distributed resources and virtual power plants at the source, load and storage side, a suitable hierarchical polymerization framework and polymerization method were proposed. Secondly, according to the layered management and control structures of different spatial scales, such as county energy Internet station, feeder, power transformation and supply area and county whole network, the layered polymerization framework and polymerization method of county energy Internet side are proposed. Finally, according to the technical requirements of various kinds of digital twins of source, load and storage and virtual power plants participating in the collaborative interaction of energy Internet at each level of county, the hierarchical polymerization framework and polymerization method of virtual power plants at the source, load and storage side to the county network are proposed.

Fig. 3.

Hierarchical polymerization framework and process of multilevel virtual power plant digital twin.

Based on the multi-level aggregation relationship between various distributed resources and virtual power plants at the source, load and storage side, an adaptive hierarchical aggregation framework and corresponding aggregation method can be established to balance the efficiency and accuracy of the system. The possible hierarchical polymerization framework and polymerization methods include low level polymerization, seed node level polymerization, upper level polymerization, etc., which can realize the multi-level polymerization of resources and the overall regulation of virtual power plant, and obtain more economic and social benefits.

According to the layered management and control structures of different spatial scales, such as county energy Internet station area, feeder line, power transformation and supply area and county whole network, the following layered polymerization framework and corresponding polymerization methods can be proposed:

According to the technical requirements of various kinds of source load storage and virtual power plant digital twins participating in the collaborative interaction of energy Internet at different levels of county, the following hierarchical polymerization framework and corresponding polymerization methods are proposed: