  Demand-Response (DR) programs, whereby users of an electricity network are
encouraged by economic incentives to rearrange their consumption in order to
reduce production costs, are envisioned to be a key feature of the smart grid
paradigm. Several recent works proposed DR mechanisms and used analytical
models to derive optimal incentives. Most of these works, however, rely on a
macroscopic description of the population that does not model individual
choices of users. In this paper, we conduct a detailed analysis of those models
and we argue that the macroscopic descriptions hide important assumptions that
can jeopardize the mechanisms' implementation (such as the ability to make
personalized offers and to perfectly estimate the demand that is moved from a
timeslot to another). Then, we start from a microscopic description that
explicitly models each user's decision. We introduce four DR mechanisms with
various assumptions on the provider's capabilities. Contrarily to previous
studies, we find that the optimization problems that result from our mechanisms
are complex and can be solved numerically only through a heuristic. We present
numerical simulations that compare the different mechanisms and their
sensitivity to forecast errors. At a high level, our results show that the
performance of DR mechanisms under reasonable assumptions on the provider's
capabilities are significantly lower than
