Flexible Generation Expansion Planning Considering Representative Days of Load and Renewable Variations

In this paper, a long-term generation expansion planning (GEP) model considering short-term constraints is presented. We propose a single-stage (Static) GEP model that will determine the capacity mix for a target year in the future under different constraints, including techno-economic constraints of thermal and renewable technologies. In order to reduce the computational burden of the proposed model, some representative days for load demand and renewables are extracted based on annual historical data of demand and renewable resources. In this regard, to capture the uncertainty and variability of load demand and renewable resources, a clustering method is utilized. Using the selected representative days, the proposed model is able to give the optimal generation expansion plans in the presence of flexibility requirements. The proposed model is formulated as a Mixed Integer Linear Programming (MILP) model. The proposed MILP model is implemented in GAMS and solved using the CPLEX algorithm.