Peak shaving through real-time scheduling of household appliances: Fid-Bau Portal

Peak shaving through real-time scheduling of household appliances

Caprino, Davide / Della Vedova, Marco L. / Facchinetti, Tullio

Highlights • Electric load management based on real-time scheduling is proposed for peak leveling. • Common household appliances are modeled in terms of timing parameters. • Peak load reduction improves for aggregations of electricity users. • Predictable peak shaving is achieved in worst- and average-case conditions. • Peak load is reduced up to 46% preserving the quality of service of each load.

Abstract The problem of limiting the peak load of the power consumed by a set of electric loads has been largely addressed in over 5 decades of research on power systems. The motivation of such attention arises from the benefits that a smoother load profile brings to the management of power systems. This paper illustrates an approach to the peak shaving problem that leverages the real-time scheduling discipline to coordinate the activation/deactivation of a set of loads. The real-time scheduling is an active research topic in the field of computing systems. The innovative idea proposed in this paper is to apply existing real-time scheduling algorithms and analysis methods to the management of power loads. This solution requires an adequate modeling of considered devices in order to derive a representation in terms of timing parameters. The modeling approach enables the handling of a set of heterogeneous loads in a coordinated manner. In particular, this paper focuses on the modeling and management of household appliances. For this purpose, a set of the most common appliances is modeled and their activation is controlled by the proposed scheduling policy. Realistic assumptions are made on the daily usage of each device. The derived results show an effective and predicable reduction of the peak load while guaranteeing the user comfort associated with the load operation. The peak load of a single apartment is reduced by the 8% in the average case and by the 41% w.r.t. the worst-case. Considering the coalition of several apartments, the scheduling approach achieves a peak load reduction up to 46%.