Multi-Agent device-level modeling framework for demand scheduling

A problem receiving increasing attention is autonomously adjusting the electricity demand of consumers in response to real-Time supply. The current literature mostly operates under the assumption that it is desirable to have an autonomous energy management system. However, autonomous demand-side management requires that smart agents understand their decision space. To model the decision space, it is key to model the scheduling constraints of individual devices under the agent's control. Some constraints can be set by the owners, e.g., deadlines, while others are physical constraints. Recent work in demand-side management is based on a wide variety of models. In this work, we develop a standardized, device-level modeling framework characterizing the device categories by their constraints. Our models enable researchers and practitioners to develop and compare demand-side management algorithms and programs. Further, we evaluate the scheduling complexity of the device categories and effects of different device combinations on the complexity. Our empirical results suggest that mixing different device categories can improve scheduling time.