Emergency backup power robust planning for urban agglomeration power grids with a high proportion of new energy sources in extreme disaster scenarios

This study introduces a novel power supply and network model designed for large-scale power grids with a high proportion of new energy sources in urban agglomerations, applicable to extreme disaster scenarios. By enhancing the IEEE 30 basic calculation examples, the research develops a comprehensive...

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Bibliographic Details
Published in:	Electric power systems research Vol. 247; p. 111715
Main Authors:	Wu, Bo, Wang, Xiuli, Qi, Shixiong, Ma, Xiang
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2025
Subjects:	Advanced integrated multi-objective algorithms Massive extreme scenarios Renewable energy grid Robust and economical decision Sparsity-aware Pareto solutions Renewable energy grid Sparsity-aware Pareto solutions Massive extreme scenarios Advanced integrated multi-objective algorithms Robust and economical decision
ISSN:	0378-7796
Online Access:	Get full text
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Summary:	This study introduces a novel power supply and network model designed for large-scale power grids with a high proportion of new energy sources in urban agglomerations, applicable to extreme disaster scenarios. By enhancing the IEEE 30 basic calculation examples, the research develops a comprehensive framework that emphasizes computational performance and engineering practicality. A massive number of DC power flow calculation scenarios over 110000 and corresponding evaluation indices are proposed, effectively addressing grid states under extreme climate-induced disconnections. Compared to traditional AC power flow methods, this approach demonstrates superior computational efficiency and engineering value. Furthermore, an integrated optimization algorithm that combines two advanced evolutionary algorithms is presented. Compared to 7 commonly used or state-of-the-art algorithms, the novel algorithm proposed in this paper demonstrates superior performance in solving sparse and constrained multi-objective optimization problems. This algorithm is highly effective at managing sparsity and constraints during the optimization process, yielding 8 four-objective Pareto frontier solutions that offer professionals robust and economical decision-making options. [Display omitted] •Proposes a grid model for urban agglomeration with new energy under extreme disaster.•Develops efficient DC power flow methods for extreme climate-induced grid disconnections.•Introduces an advanced algorithm to handle sparsity and multi-objective constraints.•Achieves robust and economical Pareto solutions with highly computational efficiency.
ISSN:	0378-7796
DOI:	10.1016/j.epsr.2025.111715