Abstract
Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).
Highlights
The optimal design and management of these supergrids is a difficult task, since it is necessary to manage large systems that include heterogeneous power grids from different countries.Most investigations in power systems often analyse optimisation problems such as optimal power flow, unit commitment, and economic dispatch, among others [1,2]
The solutions to these problems are often determined by the symmetry of the admittance and Jacobian matrices [3,4], and the topology of high-voltage transmission lines that connect the power produced at generating stations to substations, at which point the power flow is derived to other transmission lines or stepped down in voltage, and submitted across power distribution lines into the end users
Different projects aim to promote an efficient and reliable transmission grid in North America, including the Tres Amigas superstation. This superstation is the first version of this supergrid vision, since it is projected as a high-voltage direct current (HVDC) super-node asynchronously connecting the existing alternating current (AC) networks intended to link the three North American grids: the Eastern Interconnection, the Western Interconnection, and Texas
Summary
The optimal design and management of these supergrids is a difficult task, since it is necessary to manage large systems that include heterogeneous power grids from different countries. Taking into account the fact that worldwide demand for electricity has been increasing and will continue to, it is necessary to ensure the reliable and secure operation of electricity transmission networks to efficiently transport energy from generation sources to electricity consumers. To achieve this goal, decisions need to be supported by expert systems able to process a large number of variables. This paper evaluates the performance of evolutionary approaches for community detection in supergrids These algorithms, which are guided by the modularity index [23] and consider different degrees of abstraction (i.e., detect any number of communities), enable a flexible and adaptive analysis of the power grid.
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