The main topic of this dissertation is the recovery operation of distribution networks after extreme weather events. The study focuses on coordinating crews, equipment, and distribution network operations. While utilities have established protocols for recovering distribution networks, areas for continued development still exist, especially with the increase of distributed generators and controllable switches. After major weather events, one of the greatest challenges that operators face is managing the large influx of crews required to repair the damage and to reestablish normal network operations. The aim of this work is to improve current practices and provide assistance to utilities in their decision-making process to efficiently restore the system. The main objectives of this research are summarized as follows: 1) develop a stochastic program to prepare human resources and equipment before extreme weather events; 2) co-optimize repair scheduling and power operation of distribution networks; and 3) design solution algorithms for solving the above problems. For an upcoming storm, utilities should have a preparation plan that includes warehousing restoration supplies, securing staging sites (depots), and prepositioning crews and equipment. Pre-storm planning enables faster and more efficient post-disaster deployment of crews and equipment resources to damage locations. To assist utilities in making this important preparation, we develop a two-stage stochastic mixed integer linear program. The first stage determines the depots, number of crews in each site, and the amount of equipment. The second stage is the recourse action that deals with acquiring new equipment and assigning crews to repair damaged components in realized scenarios. The objective of the developed model is to minimize the costs of depots, crews, equipment, and penalty costs associated with delays in obtaining equipment and power restoration. We consider the uncertainties of damaged lines, number and type of equipment required, and expected repair times.

In the post-disaster phase, two approaches are presented for co-optimizing repair and restoration in distribution networks. First, a novel mixed integer linear program model is formulated for co-optimizing crews, resources, and distribution network operations. In addition, a framework for integrating different types of photovoltaic (PV) systems in the restoration process is developed. We consider line crews for damage repairs and tree crews for obstacle removal. The model is solved using a new algorithm that utilizes the neighborhood search method to iteratively improve the solution. The algorithm is used in a dynamically changing environment to handle the uncertainty of the repair time. In the second approach, a two-stage SMIP is developed to model the stochastic repair and restoration problem. A decomposition approach, combined with the Progressive Hedging algorithm, is used for solving the stochastic problem.