A framework for computing power consumption scheduling functions under uncertainty

Achal Agrawal (1) , Olivier Beaude (1) , Samson Lasaulce (1)

1 L2S - Laboratoire des signaux et systèmes

—One of the goals of this paper is to make a step further toward knowing how an electrical appliance should exploit the available information to schedule its power consumption ; mainly, this information corresponds here to an imperfect forecast of the non-controllable (exogenous) load or electricity price. Reaching this goal led us to three key results which can used for other settings which involve multiple agents with partial information: 1. In terms of modeling, we exploit the principal component analysis to approximate the exogenous load and show its full relevance; 2. Under some reasonable but improvable assumptions, this work provides a full characterization of the set of feasible payoffs which can be reached by a set of appliances having partial information; 3. A distributed algorithm is provided to compute good power consumption scheduling functions. These results are exploited in the numerical analysis, which provides several new insights into the power consumption scheduling problem. XXX completer en etant un plus specifique: choix du cout, un ou deux messages forts issus des simulations.

Domaines: Théorie de l'information [cs.IT]; Réseaux et télécommunications [cs.NI]

Achal Agrawal, Olivier Beaude, Samson Lasaulce. A framework for computing power consumption scheduling functions under uncertainty. IEEE Smart Grid Communications, Nov 2015, Miami, United States. ⟨hal-01221638v1⟩