When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program. The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and other operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few semidefinite programs in most cases. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.

In this paper, an optimal energy storage sizing method is proposed for networked microgrids (NMGs) considering reliability and resilience enhancement. A bi-level optimization model for energy storage sizing is developed. The upper-level model is focused on the optimal energy storage sizing problem, aiming at maximizing annual profit. The lower-level problem is aimed at operation optimization for profit maximization under multiple operating scenarios, i.e., normal operation, typical fault, and extreme fault scenarios. The bi-level model is converted into a mixed-integer linear program (MILP), which can be solved by off-the-shelf optimization solvers such as CPLEX. For comparison, the optimal energy storage size schemes are obtained for the NMGs and non-networked microgrids (NNMGs), respectively. The results indicate that the required energy storage size can be reduced while the operating profit is improved by interconnecting the microgrids (MGs). The results also indicate that the energy interaction in NMGs enables the enhancement of both reliability and resilience during grid outages.INDEX TERMS Energy storage system (ESS), networked microgrids (NMGs), optimal sizing, reliability, resilience.

After major outages, local power sources, including mobile power sources (MPSs), can be coordinated to serve critical loads in distribution systems (DSs). Repair crews (RCs) are sent to repair faulted components. Both mobile emergency resources, i.e., MPSs and RCs, need to travel through the transportation system (TS) before they reach the destination for service. However, traffic congestion may happen after natural disasters and impact the dispatch of the MPSs and RCs. Therefore, the dynamic traffic state in the TS should be considered for efficient dispatch of mobile emergency resources. This paper proposes a framework to determine critical load restoration strategy for the DS, considering the dispatch strategy of the MPSs and RCs in the TS. The cell transmission model (CTM) is used to formulate the weighted dynamic traffic assignment problem (WDTA) in the TS as a linear program (LP), aiming at minimizing the total prioritized travel time of the MPSs and RCs. For the DS, the multi-period critical load restoration problem (CLR-DS) is formulated as a mixed-integer linear program (MILP) to maximize the cumulated service time to critical loads. Unbalanced three-phase power flow and time-varying topological constraints are considered. Case studies validate the effectiveness of the proposed method.

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With the rapid development of electric vehicles, they have become an important part of urban distribution and transportation networks. The power distribution network and transportation network are coupled by electric vehicle clusters and integrated through strong interactions, creating a coupled system. This paper presents the study on their collaborative responses is essential to reduce losses and improve urban resilience during unconventional events. First, the multidimensional and deep‐level time‐varying closed‐loop coupling effects of the power distribution network and urban transportation network coupled by electric vehicle clusters are analysed under unconventional events. Second, based on the different scales of unconventional events, a summary of relevant studies is made on the collaborative response strategies of the coupled system to urban local power outages and large‐scale blackouts following unconventional events. Finally, future research directions are discussed.