Zhang, GangZhang, FengZhang, XinWu, QiuweiMeng, Ke2020-06-022020-06-022020-05-26Zhang G, Zhang F, Zhang X, et al., (2020) A multi-disaster-scenario distributionally robust planning model for enhancing the resilience of distribution systems. International Journal of Electrical Power and Energy Systems, Volume 122, November 2020, Article number 1061610142-0615https://doi.org/10.1016/j.ijepes.2020.106161http://dspace.lib.cranfield.ac.uk/handle/1826/15472Resilience oriented network planning provides an effective solution to protect the distribution system from natural disasters by the pre-planned line hardening and backup generator allocation. In this paper, a multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources (ORs) of various natural disasters, and random probability distribution of line outages (PDLO) that are incurred by a certain natural disaster. The OR uncertainty is represented by the defined probability-weighted scenarios with stochastic programming, and the PDLO uncertainty is modeled as the moment based ambiguity sets. Moreover, the disaster recovery strategies of network reconfiguration and microgrid formation are integrated into the pre-disaster network planning for resilience enhancement in both planning and operation stages. Then, a novel primal cut based decomposition solution method is proposed to improve the computational efficiency of the proposed model. In particular, the equivalent reformulation of the original MDS-DRM is first derived to eliminate the PDLO-related variables. Then, the reformulation problem is solved by the proposed primal cut based decomposition method and linearization techniques. Finally, Simulation results are demonstrated for IEEE 13-node, 33-node and 135-node distribution systems to validate the effectiveness of the proposed method in enhancing the disaster-induced network resilience.enAttribution-NonCommercial-NoDerivatives 4.0 InternationalDistributionally robust methodResilienceNetwork planningPower distribution systemStochastic programmingUncertaintyArticle