Correlated wind-power production and electric load scenarios for investment decisions

Highlights: ► Investment models require an accurate representation of the involved uncertainty. ► Demand and wind power production are correlated and uncertain parameters. ► Two methodologies are provided to represent uncertainty and correlation. ► An accurate uncertainty representation is crucial to get optimal results. -- Abstract: Stochastic programming constitutes a useful tool to address investment problems. This technique represents uncertain input data using a set of scenarios, which should accurately describe the involved uncertainty. In this paper, we propose two alternative methodologies to efficiently generate electric load and wind-power production scenarios, which are used as input data for investment problems. The two proposed methodologies are based on the load- and wind-duration curves and on the K-means clustering technique, and allow representing the uncertainty of and the correlation between electric load and wind-power production. A case study pertaining to wind-power investment is used to show the interest of the proposed methodologies and to illustrate how the selection of scenarios has a significant impact on investment decisions.