A risk function for the stochastic modeling of electric capacity expansion

We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on t he electric sector, although the methodology can be used in other applicati ons. The goals of the model are deciding which energy types must be install ed, and when. Another goal is providing an initial generation plan for shor t periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled unde r the assumption that the demand is a random vector. The cost of the risk a ssociated with decisions that may need some tuning in the future is include d in the objective function. The proposed scheme to solve the nonlinear sto chastic optimization model is Generalized Benders' decomposition. We also e xploit the Benders' subproblem structure to solve it efficiently. Computati onal results for moderate-size problems are presented along with comparison to a general-purpose nonlinear optimization package. (C) 2001 John Wiley & Sons, Inc.