This paper proposes the inclusion of chance constrained programming as an alternative to include risk aversion in the long-term power generation planning problem of hydrothermal systems, solved by stochastic dual dynamic programming (SDDP). It is proposed a less restrictive approach as compared to traditional methods of risk aversion that have been used in the Brazilian system, such as risk aversion curve (CAR) or risk aversion surface (SAR). A two-stage Benders decomposition subproblem is considered for each SDDP scenario, where the second stage subproblem is labeled CCP-SAR. The objective is to yield an operational policy that explicitly considers the risk of load curtailment several months ahead, while considering in the CCP-SAR subproblem a continuous multivariate normal distribution for the random variables related to energy inflows to the reservoirs. The feasible region for this chance constrained subproblem is outer approximated by linear cuts, using the bisection method which gradients were calculated using Genz s code. The first part of this dissertation solves the multi-stage deterministic CCP-SAR problem by an iterative procedure, for a given initial vector storage for the system. The second part presents the probabilistic risk aversion surface, for a range of values of initial storage.