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
Abstract
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.
Biography
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.