Impact of battery degradation on energy cost and carbon footprint of smart homes

Abstract

Optimal energy management in residential buildings with battery storage devices largely relies on the implementation of accurate battery degradation models. A proper wear model to use for the battery scheduling at homes should be universally applicable to different battery technologies and precisely model the impact of the major parameters that contribute to the capacity loss. In this work, a technology-agnostic battery wear model has been proposed to address the degradation caused by the irregular cycling of batteries in residential buildings. The proposed wear model provides the DoD-associated degradation for charging/discharging events between random SoCs only by employing the lifecycle curve of batteries which is usually provided by battery manufactures. This model has been formulated in the framework of mixed-integer programming (MIP) such that it can be directly incorporated into MIP optimization models to provide optimal unit commitment solutions without sacrificing other objectives of the problem. To facilitate the implementation of the nonlinear wear coefficient in MIP models, a curve-fitted version of the wear coefficient is used in the MIP problem. A comprehensive study on the costs and carbon footprint of a smart home has been carried out in this paper by producing a MIP problem that incorporates the proposed battery wear model. The problem has been solved with different approaches to minimize costs, capacity loss, and emissions of the system. The optimization results show that the application of the presented wear model managed to lower the cost, carbon footprint, and battery degradation by 78%, 30%, and 81% respectively.