Abstract: b'For the integration of renewable energy sources, power grid operators need\nrealistic information about the effects of energy production and consumption to\nassess grid stability.\n  Recently, research in scenario planning benefits from utilizing generative\nadversarial networks (GANs) as generative models for operational scenario\nplanning.\n  In these scenarios, operators examine temporal as well as spatial influences\nof different energy sources on the grid.\n  The analysis of how renewable energy resources affect the grid enables the\noperators to evaluate the stability and to identify potential weak points such\nas a limiting transformer.\n  However, due to their novelty, there are limited studies on how well GANs\nmodel the underlying power distribution.\n  This analysis is essential because, e.g., especially extreme situations with\nlow or high power generation are required to evaluate grid stability.\n  We conduct a comparative study of the Wasserstein distance,\nbinary-cross-entropy loss, and a Gaussian copula as the baseline applied on two\nwind and two solar datasets'