The transmission expansion planning (TEP) aims at identifying new reinforcements for the network, allowing a technically adequate connection between demand and generation of electric energy, both foreseen for a given planning horizon. A good expansion plan must ensure a balance between investment and operating costs, while maintaining a satisfactory level of security of the electric energy supply. However, identifying good expansion plans for TEP has become an increasingly difficult task. This is mainly due to the characteristics and dimensions of the current systems, the nonlinearity and combinatorial nature of the optimization problem, and the uncertainties present in the data. Forecasting errors, equipment unavailability, and the availability of natural resources are parameters that vary in a random way and insert a high degree of uncertainty in the electrical system, which proportionally increases with the planning horizon. One of the most relevant uncertainties to be managed in the upcoming decades will be the generation capacity from renewable sources, particularly wind power, due to its great variability. Storage devices will allow better use of these sources and, therefore, it becomes necessary to develop computational tools capable of considering such devices in the TEP problem. This dissertation presents a new methodology to support the TEP problem by inserting electric energy storage to increase the use of renewable energy in the system, while respecting the security restrictions of the network, following the demand curve and taking into account the operational variables of these devices. The possibility of including electric energy storage systems is evaluated through a costbenefit analysis. The proposed methodology is applied to a test system, subject to various operating conditions, and the obtained results are widely discussed.