The development of studies related to the power applications and economic feasibility of energy storage resources in electricity distribution networks has become promising considering the reduction in the cost of energy storage. Such technology can minimize the intermittence of renewable sources, provide the displacement of peak loads, extend the expansion of the electricity grid infrastructure, among other benefits. In this sense, this dissertation intends to explore and evaluate an integer-mixed linear optimization model, which is originally non-linear, for the planning and operation of energy storage systems inserted in a distribution system that may present unbalanced loads. The model seeks to minimize operation and investment costs, meeting systemic constraints, coordinating the different resources of a distribution system. The resources available in this work are the operation of the On Load Tap Changer (OLTC) of the substation, the possibility of load cuts and, finally, the sizing and dispatch of batteries in the system. For a more realistic analysis, a robust formulation of the load uncertainty and a representation of consumption profiles through typical scenarios, established by similarity clustering, using K-Means data mining algorithm are also addressed. The modified test system IEEE 123 bus is used in the evaluation of the described methodology and indicates the operational and economic feasibility of inserting energy storage devices in the context of the proposed work.