While one can exploit the storage properties and thus the deferability or anticipation potential of many classes of power system loads (such as thermal loads) as an increasingly needed tool to mitigate renewable sources variability, the challenge to do so in an optimal and coherent manner is immense. This is in view of the sheer number and dynamic diversity of the loads that can be involved in any large-scale application of such an approach. The smartDESC concept is a control architecture that was developed for this purpose. It builds on the more pervasive communication means currently available (such as Advanced Metering Infrastructures), as well as the mathematical tools of (i) aggregate load modeling, (ii) renewable energy forecasting, (iii) optimization theory, deterministic or stochastic, and (iv) the recent developments in control of large-scale systems based on game theory and called mean-field (MF) control theory, which allow producing a scalable yet optimal approach to the decentralized control of large pools of loads. This paper presents the building blocks of the smartDESC architecture, together with an associated simulator and simulation results.