4.6 Article

Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 100234-100246

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3207775

Keywords

Renewable energy sources; Smart grids; Power system stability; Power generation; Analytical models; Social networking (online); Computational modeling; Power grids; Intermittent renewable energy; smart grid; link analysis; leverage analysis; grid partitions

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Smart grid construction provides basic conditions for the grid connection of renewable energy sources, but the integration of large-scale intermittent renewable energy sources increases the complexity of power system operation, requiring optimization and integration to ensure flexibility and stability. Dividing the smart grid into logical clusters helps overcome challenges caused by the grid connection of intermittent renewable energy sources.
Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of medium- and large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.

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