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.