The energy storage power station refers to a system that integrates various energy storage technologies, enabling efficient demand-side management, reducing the difference between peak and valley loads, and smoothing out the overall load. By adjusting the operational mode of the energy storage power station, the electricity generated by distributed sources can be stored or regulated, allowing for high-quality grid connection. Alternatively, during periods of surplus energy, the system can store electricity and release it when there is a shortage, effectively addressing supply-demand imbalances.
American scholar Jeremy Rifkin was among the first to introduce the concept of the Energy Internet, which has since garnered significant attention globally. The Energy Internet combines smart grid technologies with internet-based systems to transform traditional energy consumption patterns. Rifkin emphasized that supporting large-scale distributed generation and distributed energy storage is one of the key features of the Energy Internet. This shift will move away from the conventional "standalone" operation model of power systems toward a more integrated "storage and joint supply" approach, with energy storage stations playing a central role in this transformation.
For many years, the dynamic behavior of power grids has been analyzed using the “simulation-modeling-solution†method based on reduction theory. However, this approach struggles to explain the internal mechanisms behind large-scale blackouts triggered by minor faults. By applying complex network theory, researchers have studied fault propagation and critical dynamics through topological analysis, leading to improved system design and optimization.
Traditionally, power system nodes are categorized into two types: generation nodes and load nodes. For example, a power plant is a generation node, while a substation is a load node. However, the introduction of energy storage systems changes the system’s topology and functionality. An energy storage substation can function as a load node when charging and as a generation node when discharging.
Due to its dual role as both a power generator and a storage unit, an energy storage power station significantly impacts grid operations and alters the grid's topology. The "role conversion" of these stations has a direct effect on the proportionality of the network. Assortative mixing, also known as proportionality, is a characteristic of complex networks where nodes with similar features tend to connect. If nodes prefer to connect with dissimilar ones, it is called disassortative mixing.
Researchers have extensively studied the application planning of energy storage power stations, developing models tailored to different energy storage scenarios. These models typically focus on economic benefits. Additionally, the small-world and scale-free properties of power grids have been deeply explored. Studies suggest that increasing proportionality gradually weakens the system’s critical behavior, leading to more frequent and larger cascading failures.
Based on this analysis, this paper investigates how the proportional mixing in the grid changes after the integration of energy storage power stations. The goal is to uncover the internal mechanisms driving differences in proportionality, thereby providing insights into the evolution of smart grid topologies and the dynamic behavior of power systems.
(a) Grid topology
(b) Grid topology after adding an energy storage power station
â— represents a hair node; â—‹ is a powered node
Figure 2: Grid topology of an energy storage power station
In conclusion, assortative mixing is a unique feature of network topologies that significantly influences self-organized critical behavior. Studying this aspect is crucial for understanding the structural vulnerabilities of power systems, the mechanisms of cascading failures, and the dynamic behavior of the grid.
Through the analysis of the GL-mixing modes in different grid structures, it was found that the disparity in topological structure and functional roles leads to variations in the non-proportional characteristics of the grid. A power grid model based on the Newman-Watts small-world network was designed. The distribution analysis of the model’s proportional coefficient, characteristic path length, and clustering coefficient revealed that the small-world property is a primary factor contributing to structural disproportionality.
To investigate the impact of different energy storage access modes on the network, a storage model based on random and regular methods was developed. By analyzing the variation of the proportional coefficient according to actual network parameters, it was found that as energy storage nodes are continuously connected to the grid, the proportional coefficient increases, meaning the proportionality becomes stronger. This increased proportionality results in a more uneven distribution between different types of nodes, ultimately weakening the grid’s resilience to cascading failures.
(b) Grid topology after adding an energy storage power station
â— represents a hair node; â—‹ is a powered node
Figure 2: Grid topology of an energy storage power station
In conclusion, assortative mixing is a unique feature of network topologies that significantly influences self-organized critical behavior. Studying this aspect is crucial for understanding the structural vulnerabilities of power systems, the mechanisms of cascading failures, and the dynamic behavior of the grid.
Through the analysis of the GL-mixing modes in different grid structures, it was found that the disparity in topological structure and functional roles leads to variations in the non-proportional characteristics of the grid. A power grid model based on the Newman-Watts small-world network was designed. The distribution analysis of the model’s proportional coefficient, characteristic path length, and clustering coefficient revealed that the small-world property is a primary factor contributing to structural disproportionality.
To investigate the impact of different energy storage access modes on the network, a storage model based on random and regular methods was developed. By analyzing the variation of the proportional coefficient according to actual network parameters, it was found that as energy storage nodes are continuously connected to the grid, the proportional coefficient increases, meaning the proportionality becomes stronger. This increased proportionality results in a more uneven distribution between different types of nodes, ultimately weakening the grid’s resilience to cascading failures.Car Wireless Charger,Wireless Car Charger,Magesafe Car Charger,Usb Car Charger
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