Section: New Results

Green Networking and Smart Grids

Participants : Sara Alouf, Eitan Altman, Nicaise Choungmo Fofack, Delia Ciullo, Alain Jean-Marie, Giovanni Neglia.

In [64] the issue of energy efficiency in Orthogonal Frequency-Division Multiple Access (OFDMA) wireless networks is discussed by D. Tsilimantos, J.-M. Gorce (Inria project-team Socrate ) and E. Altman. Their interest is focused on the promising concept of base station (BS) sleep mode, introduced recently as a key feature in order to dramatically reduce network energy consumption. The proposed technical approach fully exploits the properties of stochastic geometry, where the number of active cells is reduced in a way that the outage probability, or equivalently the signal to interference plus noise (SINR) distribution, remains the same. The optimal energy efficiency gains are then specified with the help of a simplified but yet realistic BS power consumption model. Furthermore, the authors extend their initial work by studying a non-singular path loss model in order to verify the validity of the analysis and finally, the impact on the achieved user capacity is investigated. In this context, the significant contribution of this paper is the evaluation of the theoretically optimal energy savings of sleep mode, with respect to the decisive role that the BS power profile plays.

S. Alouf, A. Jean-Marie and D. Ciullo have started the modeling of wireless communication base stations with autonomous energy supply (solar, wind). One challenge is to account for the random and non-stationary input of energy. A second challenge is to find the correct time and space granularity of the model, so as to ensure both the practical relevance of the model and numerical tractability. The activity will be backed up by a measurement campaign on the Com4Innov platform (http://www.com4innov.com/ ), that will provide information on energy consumption of different traffic patterns.

Energy demand aggregators are new actors in the energy scenario: they gather a group of energy consumers and implement a demand-response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can pay the energy demand aggregator to reduce the load by turning off some of its consumers loads or postponing their activation. Currently this operation involves only greedy energy consumers like industrial plants. In [48] , [78] A. Jean-Marie and G. Neglia in collaboration with G. Di Bella, L. Giarré, M. Ippolito and I. Tinnirello (all from Univ. of Palermo, Italy) have studied the potential of aggregating a large number of small energy consumers like home users as it may happen in smart grids. In particular they have addressed the feasibility of such approach by considering which scale the aggregator should reach in order to be able to control a significant power load. The challenge of the study derives from residential users' demand being much less predictable than that of industrial plants. For this reason they have resorted to queuing theory to study analytically the problem and quantify the trade-off between load control and tolerable service delays.