  In this paper, we apply Value-at-Risk (VaR) approaches on the problem of
yearly electric generation management. In a classical approach, the future is
modelled as a markov chain and the goal is to minimize the average generation
cost over this uncertain future. However, such a strategy could lead to big
financiallosses if worst case scenarios occur. The two VaR approaches we
propose, precisely aim at robustifying the model. On a practical point of view,
it amounts to introduce a new set of constraints modelling the uncertainties in
the original optimization problem or equivalently to change the dual objective
function. The new optimization problems are solved as efficiently as the
nominal model. Numerical simulations are presented and discussed for this
application.
