Abstract-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 dayahead 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. The electric energy network is undergoing a great technological evolution with the development of the smart grid concept, which allows more interaction between the supply-side and the demand-side of the network by taking advantage of advanced information and communication technology. These bases pose a plethora of new optimization and control issues related to stability, reliability, and pricing policies of the network. Demand-side management (DSM) comprises the different initiatives intended to modify the time pattern and magnitude of the load demand. In particular, letting the consumers directly control and manage their individual consumption patterns, combined with time-dependent electricity prices, results in a grid that is more secure and efficient, easier to operate, and that facilitates the integration of renewable energy, distributed energy generation, and storage. A day-ahead demand-side optimization provides the supply-side with an estimation of the amount of energy to be delivered to the demand-side over the time-period of analysis [1] . Nonetheless, additional costs are incurred by the supply-side when the consumption schedule is not correctly predicted by the users, and are transferred to the demand-side in the form of penalty charges [2] , [3] . The aim of this paper is to complement the deterministic model proposed in [4] , [5] by taking into account the randomness of the users' energy consumption and non-dispatchable generation. For this purpose, we propose a two-stage pricing model. In the first stage, the price per unit of energy is agreed during the day-ahead bidding process and explicitly depends
For instance, the authors from reference REF propose a real-time cost function with penalization terms in which the users minimize the expected monetary expenses.
Day-ahead bidding strategies for demand-side expected cost minimization
2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)
2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)
Engineering Computer Science