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The aim of this study is to present a method for solving the planning and expansion problems in the distribution systems in order to determine the location and size of new substations and the development of the existing transformers and to find the optimal structure for feeders and selecting the suitable cross-section for feeders in the presence of wind turbines. The objective function of the problem has been mathematically formulated considering uncertainty conditions in the presence of wind turbines and a pseudo-dynamic method for multi-stage design. Also the genetic algorithm has been used as an optimization tool for solving the problem. In addition, the performance of the above mentioned method on a distribution network has been discussed as well.

High‐impact, low‐probability events that cause significant annual damages seriously threaten the health of distribution networks. The effects of these events have made the expansion planning for distribution systems something beyond the traditional reliability criteria, so there is an ever‐increasing need for modifications in current planning approaches and focusing on the resilience in the expansion planning of distribution networks. The new attitude dealing with resilience and distributed generation sources in distribution networks necessitates a fundamental reconsidering of traditional distribution network planning methods. Here, by modelling common natural disasters such as floods and storms, an appropriate index is introduced to evaluate the distribution network resilience in the presence of distributed generation (DG) sources, including conventional gas‐fired and photovoltaic sources. Then, by presenting an appropriate model for load and photovoltaic production, the problem of comprehensive distribution network planning, including substations, feeders, and DG sources, is mathematically formulated as a multi‐objective optimization problem to improve resilience and optimize costs. Furthermore, a non‐dominated sorting genetic algorithm is used to solve the problem of comprehensively planning a resilient distribution network. Implementation of the proposed model on the IEEE 54‐bus sample network shows that network resilience can be improved with minimum cost by optimal network planning.

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