Pre-hurricane optimal placement model of repair teams to improve distribution network resilience

•A method is proposed for improvement of distribution network resilience.•Using Monte Carlo simulation and fragility curves, failure samples are generated.•A forward dynamic programming algorithm is used to obtain the path of repair teams.•The objective function considers the importance of loads and the cost of outages.•The numerical results proved that the proposed model is accurate and efficient. Predictive planning for restoration and emergency reaction before occurrence of hurricane is an effective action in reducing time and cost of electricity interruption and improving resilience in overhead distribution networks. Traditionally, approaches in load and system recovery are constructed based on reliability studies, with the consideration of equipment failure. However, widespread outages due to the extreme natural events with low probability and high destruction intensity have different properties in comparing the interruptions due to the failures in equipment. Therefore, this paper proposes a new method for restoration and emergency reaction planning. The purpose of this method is resilience improvement of distribution network against hurricane. In the proposed pre-hurricane repair team placement model (PHRTPM), using Monte Carlo simulation method and fragility curves, different failure samples of poles and conductors of medium voltage distribution network are generated according to the predicted speed of hurricane. Furthermore, the forward dynamic programming algorithm is used to determine the path of the repair teams. In this method, using a square grid topology, the optimum locations of utility teams are achieved. The objective function of problem considers the importance of loads and the cost of outages. In the numerical studies, the proposed model is implemented on a modified and real distribution network in Iran which consisted of 81 buses. The results are discussed and the sensitivity analysis is performed for various parameters.