The objective of this work is to present a methodology for the strategic bidding (or bid-based) problem of a hydropower based company, taking into account multiple hydro plants, time-coupling, multiple inflow scenarios and illustrate its application for real case studies. It is initially show that the bid-based dispatch for a hydro plant can be formulated as a stochastic dynamic programming (SDP) recursion scheme, where the state variables are the storage levels and the past inflows. As widely known, the computational effort of the SDP algorithms restricts its applications for systems with just a few reservoirs, which is not the case of the real world systems. Therefore, the approach proposed in this thesis is to extend the stochastic dual dynamic programming (SDDP) scheme, usually applied to cost minimization problems, to the strategic bidding problem. This is done through two main steps: (i) use of a quantity-only bidding scheme (similar to the Cournot model of economic equilibria); (ii) SDDP recursion, which is based on a linear approximation by piecewise linear segments and thus requires that the underlying problem to be convex. This is not necessarily observed in the strategic bidding problem. Thus, the proposed approach consists in approximating, at each stage, the future benefit function (FBF) by its concave hull, which then assures that the SDDP scheme can be applied to solve the multi-stage and stochastic strategic bidding problem of a company with a portfolio of several hydro plants. The proposed approach is illustrated with examples and case studies from real hydro systems from Rumania and El Savador, where market power analysis will be presented.