In this paper we present an evaluation framework for predictions of binary events in probabilistic electricity price forecasting. It employs the MSE-equivalent QPS together with the DM test and allows for further insights about deficiencies of the considered models. Additionally, techniques from the field of classification are considered, which extend our framework and are particularly suited for the evaluation of predictions of rare events. We consider binary events with direct applicability to a generator's daily decision making such as profitability of a pumped-hydro storage plant and evaluate the respective forecasts statistically. We show that the task of forecast evaluation can be simplified from assessing a multivariate distribution over prices to assessing a univariate distribution over a binary outcome, fully characterized by a single probability.