This thesis applies a stochastic programming approach to the techno-economic analysis of a wind farm coupled with a pumped storage facility. The production of an optimal day-ahead generating schedule is considered. Wind forecasts contain an element of random error, and several methods of addressing this uncertainty in the optimization process are compared. The methods include robust and reliability-based design optimization in addition to a combination of both approaches, and results indicate that reliability-based design optimization is best-suited to this particular problem. Based on a set of wind forecast error scenarios and historical data, a probability-weighted forecast wind generation scenario set is developed. Reliability constraints are imposed to meet a minimum of 80% of the generating schedule time intervals. This methodology is applied to a case study on Vancouver Island. Preliminary results show that when compared to the base case of a standalone wind farm on Vancouver Island, a wind farm coupled with pumped storage can prove to be economically competitive with pumped storage capital costs below $1.53 million/MW installed pumped storage capacity and a firm energy price of $130/MWh.