Day-ahead DSM techniques in the smart grid allow the supply-side to know in advance an estimation of the amount of energy to be provided to the demand-side during the upcoming day. However, a pure day-ahead optimization process cannot accommodate potential real-time deviations from the expected energy consumption by the demand-side users, neither the randomness of their renewable sources. This paper proposes a day-ahead bidding system based on a pricing model that combines: i) a price per unit of energy depending on the day-ahead bid energy needs of the demand-side users, and ii) a penalty system that limits the real-time fluctuations around the bid energy loads. In this day-ahead bidding process, demand-side users, possibly having energy production and storage capabilities, are interested in minimizing their expected monetary expense. The resulting optimization problem is formulated as a noncooperative game and is solved by means of suitable distributed algorithms. Finally, the proposed procedure is tested in a realistic setup.
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