Improving the resilience of power grids against typhoons with data‐driven spatial distributionally robust optimization

Abstract

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data‐driven spatial distributionally robust optimization (DS‐DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment‐based ambiguity set of the failure distribution based on historical typhoon data. A two‐stage DS‐DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column‐and‐constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13‐node reliability test system projected in the Hong Kong region.

Game‐theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment

“…In contrast, in a decentralized approach, more than one decision‐maker with various objective functions is present in the system, presenting different attitudes regarding the others. Many studies have been conducted using bi‐level or tri‐level optimization, analyzing the problem of critical infrastructure network restoration after disruption in a centralized and decentralized environment, considering various assumptions while focusing on maximizing the resilience or minimizing the vulnerability of the network grid (Bell et al., 2008; Brown et al., 2006; Ghorbani‐Renani et al., 2020; 2021; Lo Prete & Radhakrishnan, 2023; Ouyang, 2017; Qiao et al., 2007; Yin et al., 2023; Zhao & Zeng, 2013). Note that high computation time is prevalent in this solution approach, especially when more than two decision‐makers are involved in the decision‐making environment (Gupta et al., 2015; Liu et al., 2021).…”