Battery energy storage control : modeling, uncertainty, and applications

Battery energy storage systems (BESS) can restore flexibility to power systems that grow increasingly constrained from proliferation of variable renewable power and retirement of fossil-fuel based generation. BESS are often controlled through an energy management system (EMS), which may not have access to detailed, physical models developed by battery manufacturers. The research outlined in this dissertation makes substantive contributions to quantify, reduce, and ultimately account for the effects of battery system modeling uncertainty on energy management and control. Battery models for optimal control are reviewed in detail and metrics for their accuracy and uncertainty are derived. Optimal parameter identification and adaptive modeling methods are developed to reduce model uncertainty as much as possible. As some uncertainty always remains, a method for risk-averse model predictive control for BESS is developed to account for uncertainty and hedge control decisions to reduce optimistic shortfall. Lastly, the potential for controller performance improvement to bolster the value of BESS on the grid is demonstrated. Together, more accurate, adaptive models working in conjunction with risk-adverse control algorithms dismantle the impacts of model uncertainty on energy storage grid integration. These contributions represent an advance to the state-of-the-art in the engineering methods for addressing molding uncertainty in BESS control.Electrical and Computer Engineerin