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1. A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk

   https://doi.org/10.1111/mice.12843  

   Zhang, Ning; Alipour, Alice (May 2022, Computer-Aided Civil and Infrastructure Engineering)

   ABSTRACT The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course. 

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2. A Remediation Framework Against False Data Injection Cyberattacks Targeting ULTC Transformers to Avoid Voltage Collapse in Smart Distribution Networks

   https://doi.org/10.1109/TIA.2025.3542717  

   Naderi, Ehsan; Asrari, Arash (February 2025, IEEE Transactions on Industry Applications)

   False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks. 

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3. Resilience of renewable power systems under climate risks

   https://doi.org/10.1038/s44287-023-00003-8  

   Xu, Luo; Feng, Kairui; Lin, Ning; Perera, A.T.D.; Poor, H. Vincent; Xie, Le; Ji, Chuanyi; Sun, X. Andy; Guo, Qinglai; O’Malley, Mark (January 2024, Nature Reviews Electrical Engineering)

   Climate change is expected to intensify the effects of extreme weather events on power systems and increase the frequency of severe power outages. The large-scale integration of environment-dependent renewables during energy decarbonization could induce increased uncertainty in the supply–demand balance and climate vulnerability of power grids. This Perspective discusses the superimposed risks of climate change, extreme weather events and renewable energy integration, which collectively affect power system resilience. Insights drawn from large-scale spatiotemporal data on historical US power outages induced by tropical cyclones illustrate the vital role of grid inertia and system flexibility in maintaining the balance between supply and demand, thereby preventing catastrophic cascading failures. Alarmingly, the future projections under diverse emission pathways signal that climate hazards — especially tropical cyclones and heatwaves — are intensifying and can cause even greater impacts on the power grids. High-penetration renewable power systems under climate change may face escalating challenges, including more severe infrastructure damage, lower grid inertia and flexibility, and longer post-event recovery. Towards a net-zero future, this Perspective then explores approaches for harnessing the inherent potential of distributed renewables for climate resilience through forming microgrids, aligned with holistic technical solutions such as grid-forming inverters, distributed energy storage, cross-sector interoperability, distributed optimization and climate–energy integrated modelling. 

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4. Co-Optimized Expansion Planning to Enhance Electrical System Resilience in Puerto Rico.

   Newlun, C.; Figueroa, A.; McCalley, J.; Kimber, A.; O'Neill-Carrillo, E. (July 2019, Proceedings of the 2019 North American Power Symposium)

   In September of 2017, the island of Puerto Rico (PR) was devastated by a category 5 hurricane, Hurricane Maria. The island experienced complete blackout, and full restoration of the electrical system took nearly 11 months to complete. Therefore, it is of high interest to re-develop the infrastructure at the generation, transmission, and distribution levels so that it is hurricane-resilient. This paper describes the methodologies behind developing a more resilient electric infrastructure using a co-optimized expansion planning (CEP) software. First, a model of the PR electric power system was developed to perform long term CEP studies. The CEP tool developed seeks the minimum total cost of the PR system in a 2018-2038 planning horizon while exploring various levels of expansion investment options. The CEP also models the system under extreme events (i.e., hurricanes) to allow for data-driven resilience enhancement decisions. Second, the paper summarizes infrastructure visions that contain resilience investment options; the visions differ in terms of invested amounts of distributed generation and centralized resource. Lastly, key findings from these visions are reported and the CEP model performance is discussed. 

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5. A Benchmark Case for the Grid Survivability Analysis

   https://doi.org/10.1109/PESGM46819.2021.9638124  

   Poroseva, Svetlana V.; Soules, Keith R.; Shafiul Alam, S M; Panwar, Mayank; Hovsapian, Rob (July 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   Among current priorities of the power system analysis is the development of metrics and computational tools for the resilience analysis during catastrophic events. New methods and tools are required for such an analysis and they have to be validated prior application to real systems. However, benchmark problems are not readily available due to the analysis novelty. The current paper presents a case based on the IEEE 14-bus system for this purpose. The grid is simplified to a graph with nodes representing generators, loads, and buses. Power inputs are imported from real-time simulations of the IEEE 14-bus system. Outcomes of all possible combinations of failed elements are presented in terms of probabilities for the grid to survive, partially survive, or fail. Only the power grid's ability to withstand adverse events (survivability) is analyzed. The grid's recoverability, the other part of the resilience analysis, is not considered. 

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