In this study, a multiperiod model that considers uncertainty in the gas feedstock fuel price is developed for the optimal design of electric power systems. The optimization problem was formulated as a multiperiod stochastic programming model using the GAMS (R) modeling system. Previous studies have analyzed the United Arab Emirates' (UAE) power infrastructure either using a deterministic point of view or simulation tools (e.g., MESSAGE and MARKAL). These previous research has demonstrated that natural gas will remain playing a significant role as key feedstock fuel in the UAE's power sector. However, the present work is designed to be the first to consider different supply options for the natural gas feedstock (i.e., domestic, pipeline imports, and LNG imports) and electricity imports in the UAE power sector. Moreover, the natural gas supply and electricity import options are considered to be decision variables in the problem's formulation. Additionally, the considered case studies assumed a realistically existing power infrastructure for the UAE, whereas previous works considered the planning of the UAE power infrastructure as a Greenfield project. Also, to the authors' knowledge this is the first work to consider a robust optimization model for planning the UAE power infrastructure under uncertainty in the long term horizon. The model was used to study the planning of the power plant infrastructure in the UAE between 2015 and 2040 under uncertainty in the natural gas price. The optimization results show that the model is a valuable tool for planning the optimal power plant infrastructure of the country, reducing levelized electricity costs, and mitigating social and environmental damages. (C) 2015 Elsevier Ltd. All rights reserved.