Work in collaboration with Novais, A.Q. and Conejo, A.J.

• Class schedule:  Wednesday, Feb. 5th, 2014  from  02:00 pm to 03:00 pm
  
• Location: Building 1, Room 4214

This talk addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem,  two variants of a constraint generation algorithm based on Benders Decomposition  will be presented, and their characteristics discussed. Both algorithms are used  to solve two case studies based on two producers. The effect  of the producers' approach, whether  conservative or more risk prone, is also investigated by  solving each instance for multiple values of the so-called budget parameter. It  was possible to conclude that this parameter influences markedly the producers' strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.

Ricardo M. Lima is a Marie Curie Fellow at the National Laboratory of Energy and Geology (LNEG) in Lisbon, Portugal. He received the Licentiate degree in 1999, and the Ph.D. degree in 2006, both in Chemical Engineering from the Faculty of Engineering, University of Porto, Portugal. He has worked with Ignacio E. Grossmann as a post-doc fellow in the Department of Chemical Engineering at the Carnegie Mellon University (CMU), PA, USA in 2006-2008, and has continued as a Researcher at the CMU in 2008-2011, with a joint position as Invited Researcher in PPG Industries in 2008-2011. He joined LNEG in 2011. His main research interests include mathematical programming, robust optimization, applied optimization, energy systems, and the design and scheduling/planning of industrial processes.