Aging-aware battery dispatch optimization for grid applications

Abstract

Day by day the need for autonomy, efficiency, reliability, and sustainability in the power generation, drives researchers to seek alternative energy resources. Therefore, the demand for renewable energy sources (RES) is increasing worldwide, which is followed by the rising contemporary challenges due to the non-dispatchable nature of some of the RESs e.g. solar and wind. In this context, battery storage systems (BESS) appear as a remedy to attenuate the fluctuations caused by the variable power generation. However, despite their rapidly declining prices and improving capabilities, BESSs are yet to be economically feasible in most applications. Thus, their optimized operation plays a major role in reducing their cost. As reported in the literature, there is still a wide gap between BESS and optimization studies. Namely, including state-of-the-art battery aging models within the power system optimization has not been investigated sufficiently.This study presents the construction of an aging-aware energy management system (EMS) for stationary BESSs based on lithium-ion batteries, specifically, lithium-iron-phosphate cells. The EMS incorporates battery degradation costs by using high-fidelity semi-empirical battery models along with a mixed-integer predictive control framework. The proposed method is demonstrated on several grid applications such as energy management in island microgrids and battery dispatch in energy arbitrage markets.