A stochastic robust approach to deal with the generation and transmission expansion planning problem embedding renewable sources

The objective of this chapter is to propose and deal with an optimization model that helps the system planner to identify the optimal investments in generation and transmission projects. To this end, a dynamic stochastic adaptive robust approach is proposed to solve the problem of generation and transmission expansion planning. The advantage of the dynamic stochastic adaptive robust approach is that it considers long and short-term uncertainties. Long-term uncertainty refers to year-to-year changes, including maximum demand and available generation capacity in the system during each year of the planning horizon. In contrast, short-term uncertainty represents the production of renewable capacity dependent on climate and load during the planning horizon base year. The dynamic stochastic adaptive robust approach is formulated through a three-level problem. The three-level outline is embedded into a two-level problem. Finally, this last one is figured out by the Benders decomposition method. The proposed model is applied to an equivalent network of the Mexican electricity system. The results show that the expansion plans achieved allow solving the real expansion needs of the system, considering long and short-term uncertainties.