The current state of the art method used for medium/long-term planning studies of hydrothermal power system operation is the Stochastic Dual Dynamic Programming (SDDP) algorithm. The computational savings provided by this method notwithstanding, it still relies on major system simplifications to achieve acceptable performances in practical applications. Simplifications in the planning stage in contrast to the actual implementation might induce time inconsistent policies and, consequently, a sub-optimality gap. Time inconsistency in hydrothermal planning might be induced by, for instance, assuming a constant coefficient production for hydro plants, reservoir aggregation, neglecting Kirchhoff s voltage law, and neglecting security criteria in planning models, which are then incorporated in implementating models. Unaccounted for reservoir depletion and inadequate spinning reserve deliverability situations that were observed in the Brazilian power system might be induced by time inconsistency. And this can lead to higher operational costs. Both these consequences are utterly negative since they pose the system to a great systemic risk of energy rationing or ultimately, system blackouts. In addition, the suboptimility gap may also lead to energy markets distortions. Hence, it seems reasonable that further investigations on consequences of time inconsistency in hydrothermal planning should be undertaken. Along these lines, this work proposes an extension to previous work on the subject of time inconsistency to measure the effects of modeling simplifications in the SDDP framework for hydrothermal operation planning. The approach consists of using a simplified model for planning the system, which is done by means of the assessment of the recourse (cost-to-go) function, and a detailed model for its operation (implementation of the policy). Case studies involving simplifications in transmission lines modeling and in security criteria are carried out. Nevertheless, the focus of this work is on the later source as it is more difficult to address due to the complexity involved in the characterization of this effect. However, incorporating security criteria in planning models poses a major challenge to system operators. This is because the size of the model tends to grow exponentially as tighter security criteria are adopted. Motivated by this, the main objective of this work is to propose a new framework that allows security criteria to be incorporated in planning models and consequently ensure reserve deliverability in planning policies. The problem formulation is a multiperiod stochastic extension of Adjustable Robust Optimization (ARO) based models already proposed in literature to successfully address the dimensionality issue regarding the incorporation of security criteria n - K and its variants. The solution methodology involves a hybrid Robust-SDDP algorithm that by means of sharing active contingency states amongst periods and possible inflow scenarios in the SDDP algorithm is capable of achieving computational tractability. Then, with the proposed approach it is possible to (i) address the optimal scheduling of energy and reserve in hydrothermal power systems ensuring reserve deliverability under an n - K security criterion and (ii) assess the cost and side effects of disregarding security criteria in the planning stage.