  We develop an open-source Python software integrating flexibility needs from
Variable Renewable Energies (VREs) in the development of regional energy mixes.
It provides a flexible and extensible tool to researchers/engineers, and for
education/outreach. It aims at evaluating and optimizing energy deployment
strategies with high shares of VRE; assessing the impact of new technologies
and of climate variability; conducting sensitivity studies. Specifically, to
limit the algorithm's complexity, we avoid solving a full-mix cost-minimization
problem by taking the mean and variance of the renewable production-demand
ratio as proxies to balance services. Second, observations of VRE technologies
being typically too short or nonexistent, the hourly demand and production are
estimated from climate time-series and fitted to available observations. We
illustrate e4clim's potential with an optimal recommissioning-study of the 2015
Italian PV-wind mix testing different climate-data sources and strategies and
assessing the impact of climate variability and the robustness of the results.
