Generation expansion planning under uncertainty with emissions quotas

Abstract

Generation expansion planning for hydro-thermal power systems aims to find optimal investment decisions among a set of possible power plant projects. For any given investment plan, expansion planning models must account for investment costs as well as expected operational costs. Additionally, when modeling hydro-thermal power systems, one must consider the inherent uncertainty in hydro inflows because variations in inflows can significantly impact operational decisions. To model the expansion planning problem for a hydro-thermal power producer, we propose a novel decomposition algorithm, based on Benders decomposition. The Benders master problem computes the investment decisions while the separation problems are emissions-constrained, least-cost, and stochastic hydro-thermal scheduling problems. The separation problems are solved using a standard stochastic dual dynamic programming (SDDP) algorithm. To demonstrate the effectiveness of the approach, we present a case study for Panama's power system. The computational results allow us to price carbon dioxide emissions reductions, aiding the evaluations of environmental policies.