Structuring Bilateral Energy Contract Portfolios in Competitive Markets

  • Antonio Alonso-Ayuso
  • Nico di Domenica
  • Laureano F. EscuderoEmail author
  • Celeste Pizarro
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 163)


A multistage complete recourse model for structuring energy contract portfolios in competitive markets is presented for price-taker operators. The main uncertain parameters are spot price, exogenous water inflow to the hydro system and fuel-oil and gas cost. A mean-risk objective function is considered as a composite function of the expected trading profit and the weighted probability of reaching a given profit target. The expected profit is given by the bilateral contract profit and the spot market trading profit along the time horizon over the scenarios. The uncertainty is represented by a set of scenarios. The problem is formulated as a mixed 0–1 deterministic equivalent model. Only 0–1 variables have nonzero coefficients in the first-stage constraint system, such that the continuous variables only show up in the formulation of the later stages. A problem-solving approach based on a splitting variable mathematical representation of the scenario clusters is considered. The approach uses the twin node family concept within the algorithmic framework presented in the chapter. The Kyoto protocol-based regulations for the pollutant emission are considered.


Energy trading contracts portfolio Stochastic programming Mean risk Mixed 0–1 models Splitting variable Branch-and-fix coordination 



This research has been supported by the grants RM URJC-CM-2008-CET-3703 and RIESGOS CM from Comunidad de Madrid, and OPTIMOS2 MTM2009-14039-C06-03 and PLANIN MTM2009-14087-C04-01 from Ministry of Science and Innovation, Spain.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Antonio Alonso-Ayuso
    • 1
  • Nico di Domenica
    • 2
  • Laureano F. Escudero
    • 3
    Email author
  • Celeste Pizarro
    • 3
  1. 1.Departamento de Estadística e Investigación OperativaUniversidad Rey Juan CarlosMóstoles (Madrid)Spain
  2. 2.Value Lab.MilanoItaly
  3. 3.Departamento de Estadística e Investigación OperativaUniversidad Rey Juan CarlosMóstoles (Madrid)Spain

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