Electric capacity expansion under uncertain demand: decomposition approaches

Abstract

The authors present a stochastic model of electric capacity expansion planning under uncertainty in demand. The goal of this problem is to determine the most interesting investments (plants and capacity levels) over the considered planning time (up to several years). Periods are divided into smaller subperiods (e.g. weekly or monthly) for which demand is assumed uncertain and modeled as a continuous probability distribution function. This leads to consider the risk associated to each decision for the capacity to be used (electricity generation). A first approach as a nonlinear continuous model is presented. Benders decomposition and Lagrangean relaxation-decomposition are proposed as solution methods, where the structures of the related sub-problems are exploited to speed up the convergence. The authors provide a large computational experience and comparisons within these methods and other general purpose optimization packages, and focus the report on the advantages of each.