Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer. An adaptive robust optimization approach

Ricardo M. Lima*, Augusto Q. Novais, Antonio J. Conejo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

This paper 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 are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units' characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. 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. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. 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.

Original languageEnglish (US)
Pages (from-to)457-475
Number of pages19
JournalEuropean Journal of Operational Research
Volume240
Issue number2
DOIs
StatePublished - 2015

Bibliographical note

Funding Information:
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement N. PCOFUND-GA-2009-246542 and from the Foundation for Science and Technology of Portugal under the Welcome II Programme. The authors would like to thank the anonymous referees whose relevant suggestions have contributed to improve the paper.

Keywords

  • Electricity market
  • OR in energy
  • Renewable energy
  • Robust optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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