Energy storage power stations are systems that integrate various energy storage technologies, enabling efficient demand-side management, reducing the difference between peak and off-peak loads, and smoothing the overall grid load. By adjusting the operational mode of these stations, the electricity generated by distributed sources can be stored or regulated, ensuring high-quality integration with the power grid. Alternatively, when there is an abundance of electricity, the station can store it and release it during times of scarcity, effectively addressing supply-demand imbalances.
The concept of the Energy Internet was first introduced by American scholar Jeremy Rifkin, who emphasized its potential to transform traditional energy usage patterns through smart grid technologies. One of the key features of the Energy Internet is its support for large-scale distributed generation and storage systems. The conventional "out-of-the-box" operation model of the power system is expected to shift towards a more integrated "storage and joint supply" approach, making energy storage stations one of the most critical components of the future energy infrastructure.
Historically, dynamic behavior analysis in power grids has relied heavily on the "simulation-modeling-solution" method based on reduction theory. However, this approach struggles to explain the internal mechanisms behind large-scale outages caused by small initial faults. To address this, complex network theory has been applied to analyze the topological structure of the grid, study fault propagation mechanisms, and optimize system design.
Traditionally, power system nodes are categorized into two types: generation nodes (e.g., power plants) and load nodes (e.g., substations). With the integration of energy storage systems, the topology and functionality of the grid change. For instance, an energy storage substation may act as a load node during charging and as a generation node during discharging.
Due to their dual role as both generators and storage units, energy storage power stations significantly influence grid operations and alter the grid’s topology. The “role conversion†of these stations has the most direct impact on the proportionality of the network. Proportionality, or assortative mixing, refers to the tendency of similar nodes to connect with each other. If a node tends to connect with dissimilar nodes, it is called disassortative mixing.
Scholars have extensively studied the planning and application of energy storage power stations, developing models tailored to different energy storage applications. These models typically focus on economic benefits. Additionally, the small-world and scale-free characteristics of power grids have been widely researched. Studies show that increased proportionality can gradually undermine the system's critical behavior, leading to more frequent and larger-scale cascading failures.
Based on the above analysis, this paper further explores how the introduction of energy storage power stations affects the proportional mixing within the grid. The goal is to uncover the internal mechanisms driving changes in proportionality, thereby contributing to the understanding of smart grid evolution and the dynamic behavior of power systems.
(a) Grid Topology
[Image: http://i.bosscdn.com/blog/11/3P/14/G2_0.jpg]
(b) Grid Topology After Adding an Energy Storage Power Station
[Image: http://i.bosscdn.com/blog/11/3R/92/R2_0.jpg]
â— represents a generator node; â—‹ represents a load node
Figure 2: Grid Topology of an Energy Storage Power Station
In conclusion, assortative mixing is a unique feature of complex network topologies, playing a significant role in the self-organized critical behavior of the power grid. Studying this phenomenon is crucial for understanding structural vulnerabilities, cascading failure mechanisms, and the dynamic behavior of power systems.
Through the analysis of different grid structures, it was found that variations in topological structure and functional roles lead to differences in the non-proportional characteristics of the grid. A power grid model based on the NW small-world network was developed. Analysis of the model’s proportional coefficient, characteristic path length, and clustering coefficient revealed that small-world properties are a primary cause of structural disproportionality.
To investigate the impact of different energy storage access modes on the network, a storage model using random and regular methods was designed. By analyzing the variation of the proportional coefficient based on real-world network parameters, it was found that as more energy storage nodes are added, the proportional coefficient increases, enhancing the proportionality of the network. This increased proportionality leads to a more uneven distribution among different node types, ultimately weakening the grid’s resilience against cascading failures.
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