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STOCHASTIC MARKET EQUILIBRIUM MODEL FOR GENERATION PLANNING

Published online by Cambridge University Press:  31 August 2005

J. Barquín ,
E. Centeno  and
J. Reneses
J. Barquín
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: [email protected]
E. Centeno
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: [email protected]
J. Reneses
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: [email protected]
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Abstract

It is widely accepted that medium-term generation planning can be advantageously modeled through market equilibrium representation. There exist several methods to define and solve this kind of equilibrium in a deterministic way. Medium-term planning is strongly affected by uncertainty in market and system conditions; thus, extensions of these models are commonly required. The main variables that should be considered as subject to uncertainty include hydro conditions, demand, generating units' failures, and fuel prices. This paper presents a model to represent a medium-term operation of the electrical market that introduces this uncertainty in the formulation in a natural and practical way. Utilities are explicitly considered to be maximizing their expected profits, and biddings are represented by means of a conjectural variation. Market equilibrium conditions are introduced by means of the optimality conditions of a problem, which has a structure that strongly resembles classical optimization of hydrothermal coordination. A tree-based representation to include stochastic variables and a model based on it are introduced. This approach to market representation provides three main advantages: Robust decisions are obtained; technical constraints are included in the problem in a natural way, additionally obtaining dual information; and large-size problems representing real systems in detail can be addressed.

Type
Papers from the 8th International PMAPS Conference
Information
Probability in the Engineering and Informational Sciences , Volume 19 , Issue 4 , October 2005 , pp. 533 - 546
Copyright
© 2005 Cambridge University Press

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References

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