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Korean Journal of Artificial Intelligence / Korean Journal of Artificial Intelligence, (E)2508-7894
2023, v.11 no.2, pp.1-6
https://doi.org/https://doi.org/10.24225/kjai.2023.11.2.1
Yeonwoo LEE
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Abstract

This paper presents a comprehensive scheme for assessing the importance of multiple microgrids (MGs) network that includes distributed energy resources (DERs), renewable energy systems (RESs), and energy storage system (ESS) facilities. Due to the uncertainty of severe weather, large-scale cascading failures are inevitable in energy networks. making the assessment of the structural vulnerability of the energy network an attractive research theme. This attention has led to the identification of the importance of measuring energy nodes. In multiple MG networks, the energy nodes are regarded as one MG. This paper presents a modified PageRank algorithm to assess the importance of MGs that include multiple DERs and ESS. With the importance rank order list of the multiple MG networks, the core MG (or node) of power production and consumption can be identified. Identifying such an MG is useful in preventing cascading failures by distributing the concentration on the core node, while increasing the effective link connection of the energy flow and energy trade. This scheme can be applied to identify the most profitable MG in the energy trade market so that the deployment operation of the MG connection can be decided to increase the effectiveness of energy usages. By identifying the important MG nodes in the network, it can help improve the resilience and robustness of the power grid system against large-scale cascading failures and other unexpected events. The proposed algorithm can point out which MG node is important in the MGs power grid network and thus, it could prevent the cascading failure by distributing the important MG node's role to other MG nodes.

keywords
Page Rank Algorithm, Energy Flow, Smargrid, Artificial Intelligence, Technology, Microgrid

Korean Journal of Artificial Intelligence