Energy storage power stations are advanced systems that integrate various energy storage technologies, enabling efficient demand-side management, reducing the difference between peak and valley loads, and smoothing out power consumption. By adjusting the operational mode of these stations, the electricity generated by distributed sources can be stored or regulated, allowing for a higher-quality connection to the grid. Alternatively, during periods of surplus energy, the station can store electricity and release it when supply is limited, helping to balance the energy demand and supply.
The concept of the Energy Internet was first introduced by renowned American scholar Jeremy Rifkin, sparking global interest. This innovative approach integrates internet-based smart grid technologies to transform traditional energy usage patterns. Rifkin emphasized that supporting large-scale distributed generation and energy storage systems is a key characteristic of the Energy Internet. The conventional "standalone" operation model of power systems will be replaced by a more integrated "storage and joint supply" model, with energy storage power stations playing a crucial 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 caused by small faults. By applying complex network theory, researchers analyze the topology of the grid to study fault propagation and critical dynamic behaviors, leading to system reconstruction 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. With the integration of energy storage systems, the structure and functionality of the system change. An energy storage-equipped substation can act as a load node during charging and as a generation node during discharging.
Due to its dual role as both a generator and a storage unit, an energy storage power station significantly influences grid operations, altering the grid's topology. The "role conversion" of these stations has the most direct impact on the network's proportionality—also known as assortativity. Assortativity refers to the tendency of nodes with similar characteristics to connect, while disassortativity occurs when nodes tend to connect with those of different characteristics.
Scholars 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 analyzed. Research indicates 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 and research on typical power grid similarities, this paper 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, providing a foundation for understanding the evolution of smart grid topologies and the dynamic behavior of power systems.
(a) Grid topology
(b) Grid topology after accessing the energy storage power station
â— represents a generation node; â—‹ represents a load node
Figure 2: Grid topology of an energy storage power station
In conclusion, assortativity is a unique feature of complex network topologies and plays a significant role in influencing the self-organized critical behavior of networks. Studying assortativity is essential for understanding 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 modes in different grid structures, it was found that differences in topological structure and functional positioning lead to variations in the non-assortative characteristics of the grid. A power grid model based on the Newman-Watts (NW) small-world network was developed. The distribution analysis of the model's proportionality coefficient, characteristic path length, and clustering coefficient revealed that small-world properties are a major factor contributing to structural disproportionality.
To investigate the impact of different energy storage access modes on network topology, a storage model based on random and regular methods was designed. By analyzing the variation of the proportionality coefficient using actual network parameters, it was found that as energy storage nodes are continuously connected to the network, the proportionality coefficient increases, meaning the network becomes more assortative. This enhanced proportionality leads to a more uneven distribution among different node types, thereby weakening the grid’s resilience to cascading failures.
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