The energy storage power station refers to a system that integrates various energy storage technologies, enabling efficient demand-side management, reducing peak-to-valley differences, and smoothing the load. By adjusting the operational mode of the energy storage facility, electricity generated from distributed sources can be stored or regulated, ensuring high-quality connection to the grid. Alternatively, the system can store excess energy during times of surplus and release it when demand is high, effectively addressing supply-demand imbalances.
The concept of the Energy Internet was first introduced by American scholar Jeremy Rifkin, who emphasized the integration of smart grid technologies with internet-based systems to transform how energy is consumed. One of the key features of the Energy Internet is its ability to support large-scale distributed generation and energy storage systems. This shift moves away from the traditional "out-of-the-box" operation model toward a more flexible "storage and joint supply" approach, making energy storage stations a crucial component of this new energy landscape.
For many years, the dynamic behavior of power grids has been analyzed using a simulation-modeling-solution method based on reduction theory. However, this approach struggles to explain the internal mechanisms behind large-scale outages caused by minor faults. To address this, complex network theory has been applied to analyze the topology of the grid, study fault propagation, and optimize system design.
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. The introduction of an energy storage system changes the system’s topology and functionality. A substation equipped with energy storage can act 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 topological structure. The "role conversion" of these stations has the most direct influence on network proportionality. Assortative mixing, also known as proportionality, is a characteristic of complex networks where nodes tend to connect with similar ones (assortative) or dissimilar ones (disassortative).
Researchers have extensively studied the application planning of energy storage stations, developing models tailored for different scenarios. These models typically focus on economic benefits. Additionally, the small-world and scale-free characteristics of power grids have been thoroughly examined. Studies show that increased proportionality can disrupt the system’s critical behavior, leading to more frequent and larger-scale cascading failures.
Based on this analysis, this paper investigates the changes in proportional mixing after integrating energy storage stations into the grid. The goal is to uncover the internal mechanisms behind variations in proportionality and provide 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 Station
â— represents a hub node; â—‹ represents 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. Understanding this aspect is vital for analyzing the structural vulnerability of power systems, the propagation mechanism of cascading failures, and the dynamic behavior of the grid.
Through the analysis of the gl-mixing patterns in different grid structures, it was found that differences in topological structure and functional roles lead to variations in non-proportional characteristics. 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 small-world properties are the main factors contributing to certain structural disproportions.
To study the impact of different energy storage access modes on network topology, a storage model based on random and regular methods was developed. By analyzing the variation of the proportional coefficient using real-world network parameters, it was found that as more energy storage nodes are added, the proportional coefficient increases, meaning the network becomes more assortative. This increase in proportionality leads to a more uneven distribution among node types, weakening the grid’s resilience against cascading failures.
(b) Grid Topology After Adding an Energy Storage Station
â— represents a hub node; â—‹ represents 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. Understanding this aspect is vital for analyzing the structural vulnerability of power systems, the propagation mechanism of cascading failures, and the dynamic behavior of the grid.
Through the analysis of the gl-mixing patterns in different grid structures, it was found that differences in topological structure and functional roles lead to variations in non-proportional characteristics. 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 small-world properties are the main factors contributing to certain structural disproportions.
To study the impact of different energy storage access modes on network topology, a storage model based on random and regular methods was developed. By analyzing the variation of the proportional coefficient using real-world network parameters, it was found that as more energy storage nodes are added, the proportional coefficient increases, meaning the network becomes more assortative. This increase in proportionality leads to a more uneven distribution among node types, weakening the grid’s resilience against cascading failures.Pv Branch Connector,MC4 Y Type Connector,MC4 Branch,MC4 Branch Adapter
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