  With the growth of complexity and extent, large scale interconnected network
systems, e.g. transportation networks or infrastructure networks, become more
vulnerable towards external disruptions. Hence, managing potential disruptive
events during the design, operating, and recovery phase of an engineered system
therefore improving the system's resilience is an important yet challenging
task. In order to ensure system resilience after the occurrence of failure
events, this study proposes a mixed-integer linear programming (MILP) based
restoration framework using heterogeneous dispatchable agents. Scenario-based
stochastic optimization (SO) technique is adopted to deal with the inherent
uncertainties imposed on the recovery process from nature. Moreover, different
from conventional SO using deterministic equivalent formulations, additional
risk measure is implemented for this study because of the temporal sparsity of
the decision making in applications such as the recovery from extreme events.
The resulting restoration framework involves a large-scale MILP problem and
thus an adequate decomposition technique, i.e. modified Lagrangian dual
decomposition, is also employed in order to achieve tractable computational
complexity. Case study results based on the IEEE 37-bus test feeder demonstrate
the benefits of using the proposed framework for resilience improvement as well
as the advantages of adopting SO formulations.
