Aging-aware Home Energy Management System under Probabilistic Forecasts

Abstract

Home Energy Management Systems (HEMS) play a key role in the electrification of the residential sector. In a future where different energy carriers must be converted and used seamlessly, HEMS are responsible for orchestrating all energy assets within a home. To determine the power flows, the HEMS must be aware of its impact on the aging of the system's batteries. On the other hand, since this is typically performed through optimization models, various variables are needed, such as ambient temperature, solar radiation, and initial conditions. These inputs are uncertain and require proper forecasting. This paper presents the operation of an aging-aware HEMS using probabilistic forecasts of exogenous information, directly relating them to the quality of the degradation control. The relationship is studied under deterministic and stochastic optimal control settings.