Using stochastic dual dynamic programming and a periodic autoregressive model for wind-hydrothermal long-term planning

In this study, we use a stochastic representation of wind for medium/long-term planning problems that are associated with the operation of hydro-thermal systems. The stochastic dual dynamic programming (SDDP) technique is used in this study. Synthetic wind and hydrological scenarios are generated using a periodic autoregressive model (PAR (p)). This algorithm has wide applicability in countries with a predominantly hydroelectric energy matrix that is associated with high penetration of thermal and wind generation, as in the Brazilian power system. The nonlinearities of the hydraulic production function also was been taking into account. The developed technique can be applied due to the global expansion of power generation over the last two decades with the increasing integration of alternative sources.