This dissertation proposes a new transmission planning method for electric power systems with large planned additions of uncertain intermittent renewable resources. The major contribution of this dissertation is applying stochastic programming that represents two uncertain parameters, wind and load, to transmission planning. We apply an ad hoc partition method to approximate the bivariate random variables of load and wind. A two-stage stochastic transmission planning problem is repeatedly solved by replacing continuous random variables with approximations that have a more refined partition at each iteration. A candidate solution is provided when improvement is not observed at an optimal value, even with more refined approximations. Numerical results show the efficiency of the method. However, if the number of samples is not sufficient to represent the original random variable's characteristics, the solution may be poor. Therefore, we employ a sampling method using Gaussian copula in order to generate as many random samples as necessary. The problem is replicated and solved using a fixed number of samples generated by Gaussian copula. In order to asses solution quality, a 95\%-confidence interval on the optimality gap is formed. A candidate stochastic solution for transmission investment is used to simulate the operation of a utility-scale storage system. A mixed integer program (MIP) is applied to this formulation. As a case study, the Electric Reliability Council of Texas (ERCOT) wind and load data is employed, along with a simplified model of the transmission system. Energy storage is also considered. The storage operation shifts wind power from off-peak hours to on-peak hours, and its wind power generation shows a close character to that of a base load generator. / text