Contracting strategies for generation companies with ambiguity aversion on spot price distribution

Abstract

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 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. Under this framework, decisions are made under ambiguity, which happens whenever the decision maker is aware that a given set of scenarios and probabilities represent only an approximation of the true underlying distribution. In this work, robust optimization is used to account for ambiguity aversion in the optimal contracting strategy of renewable generation companies. A case study with data from the Brazilian system is shown to illustrate the applicability of the proposed methodology.