The Electric Power System Simulator (SimSEE) is a model that optimizes and simulates the operation of an electric system and has been widely used to analyze the integrated power system in Uruguay. Among the stochastic processes that SimSEE needs to represent are the streamflow inputs to the hydroelectric dams, which constitute a key factor for both energy dispatches and mid-term planning. A dominant feature of streamflow time series is that they show very high interannual variability, which represents a major uncertainty for energy operation in Uruguay. Part of this variability is associated with El Niño-Southern Oscillation (ENSO) phenomena. In this work, a climate index associated with ENSO is incorporated to the stochastic generator of streamflow series used by the SimSEE. Forty six-month-long simulations are performed with and without such modifications. We find that the system operator can take advantage of the ENSO-related information incorporated to SinSEE in 65% of the cases (semesters) considered, reducing the total costs of operation. This result still holds in a scenario with larger firm capacity. The amounts saved, however, are reduced as the firm capacity increases. The enhanced generation options associated with a larger firm capacity diminish the overall costs of generation, especially in times of low streamflow, rendering the savings associated with a more efficient management of the reservoir less significant. Keywords : economic impact, electric system simulator, ENSO, stochastic process, streamflow representation