A two-stage stochastic programming approach to integrated day-ahead electricity commitment and production scheduling

Abstract

Abstract To ensure stability of the power grid, the electricity suppliers impose a daily day-ahead hourly electricity commitment to large consumers. In case the actual electricity consumption differs from the committed profile, the consumer is obliged to pay penalties. The challenge addressed in this work is to simultaneously determine the optimal day-ahead electricity commitment and the optimal production scheduling. Since the consumers have to commit themselves to the amount of energy they are going to purchase and use for a period of 24 hours one day before the actual electricity demand is realized, a major challenge lies in the uncertainty: equipment failures may reduce the production capacity and can make the actual electricity consumption drastically deviating from the day-ahead electricity commitment. For this purpose a two-stage stochastic mixed-integer linear programming model that considers equipment breakdowns is proposed. The application of the proposed approach to a continuous power intensive plant shows the improvement achieved by solving the stochastic model.