Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance.
Li et al. (Thu,) studied this question.
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