The stochastic fluctuation characteristics of intermittent renewable energy sources and energy loads, as well as their multi-energy interactions and dependencies, have negligible effects on the operation and analyses of integrated energy systems. Determining how to model the probability characteristics of such systems with high calculation accuracy using limited scenarios is a major difficulty of uncertainty description. This study proposes the use of an optimum quantile method based on the Wasserstein distance metric to generate a typical scenario set in an integrated energy system considering energy correlations based on weather conditions. The use of discrete variables, as opposed to continuous variables based on sampling techniques such as Monte Carlo simulations, sets this study apart from other studies. The uncertainties of a typical network containing power, heat, and gas are analysed, and the results show that the proposed method can produce a typical scenario set with good precision. (C) 2017 Elsevier Ltd. All rights reserved.