Two-stage stochastic programming model for the thermal optimal day-ahead bid problem with physical future contracts

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 optimiza- tion strategies of the Generation Companies. One main characteristic of MIBEL's Derivatives Market is the existence of physical futures contracts; they imply the obligation to settle physically the energy. The market regulation 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 and it could imply changes in the optimal planning, both in the optimal bidding and in the unit commitment. The goal of this work is the optimization of the coordination between the physical futures contracts and the Day-Ahead bidding following this regulation. We propose a stochastic quadratic mixed-integer programming model which maximizes the expected profits taking into account futures contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy and power planning production (unit commitment) for the thermal units of a price-taker Generation Company. The uncertainty of the day-ahead market price is included in the stochastic model through a scenario set. There has been applied both simulation and reduction techniques for building this scenario set from a time series ARIMA model. The implementation of the model is done with the modeling language AMPL. Implementation details and some first computational experiences for small real cases are presented