A risk function for the stochastic modeling of electric capacity expansion

Abstract

Abstract:We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate-size problems are presented along with comparison to a general-purpose nonlinear optimization package.

A Review of Robust Operations Management under Model Uncertainty

“…However, cost uncertainty has been considered by many in robust OM. Different types of cost considered include fixed ordering and purchase cost (Yu 1997), production and labor‐related cost (Leung and Wu 2004), fixed location and transportation cost (Kouvelis and Yu 1997), and capacity expansion cost (Marin and Salmeron 2001).…”

Planning and Scheduling under Uncertainty: A Review Across Multiple Sectors

“…Marín and Salmerón presented a stochastic model for capacity expansion planning under capacity deterioration and demand uncertainty. The model combines techniques of stochastic optimization, robust optimization, and statistical decision rules.…”