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Extreme weather events increasingly threaten the resilience of power distribution systems, yet conventional restoration solutions remain cost-prohibitive for economically constrained communities. This paper proposes a Time-Sharing Resilience Strategy (TSRS) that rotationally energizes network partitions using minimal distributed energy resources (DERs), prioritizing equitable restoration based on Social Vulnerability Index (SoVI) scores. TSRS leverages a bilevel mixed-integer nonlinear programming (MINLP) framework. The upper level optimizes battery energy storage system (BESS) sizing and placement to minimize costs while ensuring SoVI-weighted energization durations, whereas the lower level employs a reverse breadth-first search (RBFS) algorithm to form load-balanced partitions, placing bypass and segmentation switches to satisfy voltage and reactive power constraints. Key contributions include: 1) an equity-driven partitioning approach that extends energization for high-SoVI communities; 2) a scalable bilevel MINLP model that optimizes DER and switch costs; and 3) validation on a 13,623-node synthetic Ocoee City, FL, network and the IEEE 34-bus feeder, achieving significant cost savings with multi-site BESS under TSRS versus conventional methods, along with enhanced resilience via PV-augmented storage. Simulations confirm TSRS's cost-effectiveness, scalability, and prioritization of vulnerable communities, offering a practical resilience solution.
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Tu et al. (2025) studied this question.
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