Stochastic optimal day-ahead bid with physical future contracts

Publication TypeConference Paper
Year of Publication2008
AuthorsCristina Corchero; F.-Javier Heredia
Conference NameInternational Workshop on Operational Research 2008
Series TitleI.W.OR. International Workshop on Operations Research
Conference Date05-07/06/2008
PublisherDept. of Statistics and Operational Research, Univ. Rey Juan Carlos.
Conference LocationDept. of Statistics and Operational Research, Univ. Rey Juan Carlos, Madrid, Spain.
Type of WorkInvited presentation
ISBN Number978-84-691-3994-3
Key Wordsstochastic programming; electricity markets; day-ahead market; futures contracts; MIBEL; modellization; research
The reorganization of electricity industry in Spain has finished a new step with the start-up of the Derivatives Market. Nowadays all electricity transactions in Spain and Portugal are managed jointly through the MIBEL by the Day-Ahead Market Operator and the Derivatives Market Operator. This new framework requires important changes in the short-term optimization strategies of the Generation Companies.
One main characteristic of MIBEL’s derivative market is the existence of short-term physical futures contracts; they imply the obligation to settle physically the energy. The regulation of our market establishes the mechanism for including those physical futures in the day-ahead bidding of the Generation Companies. Thus, the participation in the derivatives market changes the incomes function. The goal of this work is the optimization of the coordination between the physical products and the day-ahead bidding following this regulation because it could imply changes in the optimal planning, both in the optimal bidding and in the unit commitment.
We propose a stochastic mixed-integer programming model to coordinate the Day-Ahead Market and the physical futures contracts of the generation company. The model maximizes the expected profits taking into account futures contracts incomes. The model gives the optimal bidding strategy for the Day-Ahead Market as long as the simultaneous optimization for power planning production and day-ahead market bidding for the thermal units of a price-taker generation company. Thus, the model gives the optimal bid, particularly the instrumental-price bid quantity and its economic dispatch, and it provides the unit commitment.
The uncertainty of the day-ahead market price is included in the stochastic model through a scenario tree. There has been applied both reduction and generation techniques for building this scenario tree from an ARIMA model. Results applying those different approaches are presented.
The implementation is done with a modelling language. Implementation details and some first computational experiences for small real cases are presented.
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