Abstract : This thesis is concerned with probabilistic numerical problems about modeling, risk control and risk hedging motivated by applications to energy markets. The main tool is based on stochastic approximation and simulation methods. This thesis consists of three parts. The first one is devoted to the computation of two risk measures of the portfolio loss distribution L: the Value-at-Risk (VaR) and the Conditional Value-at-Risk (CVaR). This computation uses a stochastic algorithm combined with an adaptive variance reduction technique. The first part of this chapter deals with the finite dimensional case, the second part extends the results of the first part to the case of a path-dependency process and the last one deals low discrepancy sequences. The second chapter is devoted with risk minimizing hedging strategies in an incomplete market operating in discrete time using quantization based stochastic approximation. Theoretical results on CVaR hedging are presented then numerical aspects are adressed in a Markovian framework. The last part deals with joint modeling of Gas and Electricity spot prices. The multi-factor model presented is based on stationnary Ornstein process with parameterized diffusion coefficient.