Interval TrendRank method for grid node importance assessment considering new energy

Qingyu Su, Cong Chen, Xin Huang and Jian Li

Applied Energy, 2022, vol. 324, issue C, No S0306261922009461

Abstract: It is of great significance to identify the important nodes accurately and rapidly for preventing accidents in the power system. In this paper, an Interval TrendRank algorithm for identifying key nodes in complex power grids is proposed. The algorithm takes into account the scheduling role of information networks, the instability of new energy generation, and the system topology. The algorithm uses an Extreme Learning Machine based on Genetic Algorithm optimization for new energy generation power prediction. The algorithm uses a TrendRank value function to represent the importance of system nodes. The TrendRank value can iteratively calculate the TrendRank interval value according to the weighted distribution strategy of internally linked nodes, and then rank them. The comparison of four performance metrics fully verifies the effectiveness and superiority of the method.

Keywords: New energy; Extreme learning machine; Genetic algorithm; Complex network; Smart grid; Interval TrendRank (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2022.119647

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