Power distribution systems in the US are commonly supported by wood utility poles. These assets require regular maintenance to enhance the reliability of power delivery to support many dependent functions of the society. Limitations in budget, however, warrant efficient allocation of limited resources based on optimal preventive maintenance plans. A few studies have developed risk-based metrics to support risk-informed decision making in preventive maintenance planning for power distribution systems. However, integration of risk-based metrics and optimization for enhancing the life-cycle resilience of distribution systems has not been explored. To address this gap, this paper proposes a mixed-integer nonlinear programming (MINLP) model to maximize the life-cycle resilience of aging power distribution systems subject to multi-occurrences of hurricane events using an optimal risk-based maintenance planning. For this purpose, a risk-based index called the Expected Outages is proposed and integrated into the optimization problem to minimize the total expected number of power outages in the entire planning horizon. Various uncertainties in the performance of poles under stochastic occurrences of hazards are taken into account through advanced fragility models and an efficient recursive formulation that models the uncertainty of precedent pole failures. The proposed approach is applied to a large, realistic power distribution system for long-term maintenance planning given a total budget limit and different levels of periodic budget constraints. The resulting optimization problems are solved through the branch and bound algorithm. Results indicate that applying the presented methodology leads to a significant enhancement of the life-cycle resilience of distribution systems compared to the commonly implemented strength-based maintenance strategy set by National Electric Safety Code.

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With the increasing reliance on the constant flow of electricity, risk-based management strategies are increasingly needed to ensure that with limited available resources, the grid can maintain high reliability and resilience. A growing concern in meeting this objective is the impact of climatic extremes, as the wide exposure of the power grid infrastructure has resulted in a system that is inherently vulnerable to extreme climatic hazards which are exacerbated by climate change. Analyzing the likelihood of damage induced by extreme hazards is critical for developing riskinformed strategies. Overhead structures, in particular, may experience a wide spectrum of damage types and degrees during hurricanes. Beyond the collapse state of transmission towers, which has been investigated in the past, non-collapse damage states in lattice towers require further attention as they can assist with performancebased design, grid recovery planning, and hardening decisions in preparation for extreme events. The present study establishes a set of performance-based limit states for lattice transmission towers subject to wind-induced extreme loadings. Specifically, five damage states including no damage, slight, moderate, and extensive damage, and collapse are defined. These limit states are founded on the nonlinear behavior of lattice towers and the type and severity of failures in tower elements and connections, as they relate to the repair or replacement requirements of towers. Focusing on a double circuit vertical steel lattice transmission tower as a case study, the proposed limit states are evaluated by generating a large number of random realizations of a diverse set of uncertain variables including those related to wind

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