Energy spot price is characterized by its high volatility and difficult prediction, representing a major risk for energy companies, especially those that rely on renewable generation. The typical approach employed by such companies to address their mid- and long-term optimal contracting strategy is to simulate a large set of paths for the uncertainty factors to characterize the probability distribution of the future income and, then, optimize the company s portfolio to maximize its certainty equivalent. In practice, however, spot price modeling and simulation is a big challenge for agents due to its high dependence on parameters that are difficult to predict, e.g., GDP growth, demand variation, entrance of new market players, regulatory changes, just to name a few. In this sense, in this dissertation, we make use of robust optimization to treat the uncertainty on spot price distribution while renewable production remains accounted for by exogenously simulated scenarios, as is customary in stochastic programming. We show that this approach can be interpreted from two different point of views: stress test and aversion to ambiguity. Regarding the latter, we provide a link between robust optimization and ambiguity theory, which was an open gap in decision theory. Moreover, we include into the optimal portfolio model, the possibility to consider an energy call option contract to hedge the agent s portfolio against price spikes. A case study with realistic data from the Brazilian system is shown to illustrate the applicability of the proposed methodology.