  The Intergovernmental Panel on Climate Change proposes different mitigation
strategies to achieve the net emissions reductions that would be required to
follow a pathway that limits global warming to 1.5{\deg}C with no or limited
overshoot. The transition towards a carbon-free society goes through an
inevitable increase in the share of renewable generation in the energy mix and
a drastic decrease in the total consumption of fossil fuels. Therefore, this
thesis studies the integration of renewables in power systems by investigating
forecasting and decision-making tools. Indeed, in contrast to conventional
power plants, renewable energy is subject to uncertainty. Most of the
generation technologies based on renewable sources are non-dispatchable, and
their production is stochastic and complex to predict in advance. A high share
of renewables is challenging for power systems that have been designed and
sized for dispatchable units. In this context, probabilistic forecasts, which
aim at modeling the distribution of all possible future realizations, have
become a vital tool to equip decision-makers, hopefully leading to better
decisions in energy applications. This thesis focuses on two main research
questions: (1) How to produce reliable probabilistic renewable generation
forecasts, consumption, and electricity prices? (2) How to make decisions with
uncertainty using probabilistic forecasts? The thesis perimeter is the energy
management of "small" systems such as microgrids at a residential scale on a
day-ahead basis. It is divided into two main parts to propose directions to
address both research questions (1) a forecasting part; (2) a planning and
control part.
