Thermal-Mass Coupled Modeling and Optimal Scheduling of Aluminum Smelting Loads for Safe Demand Response Participation

Abstract

Aluminum smelting loads (ASLs) offer substantial flexibility potential, yet their actual participation in demand response remains conservative owing to the risk of operational failures. Power adjustments disturb the electrolyte temperature and superheat, which may lead to electrolyte freeze-out or yield loss. Existing ASL demand response methods often rely on oversimplified dynamic models of the electrolytic cell's thermal behavior, such as linear models and the Equivalent Thermal Parameter (ETP) model, which may either violate temperature constraints or underestimate the available flexibility. To address this limitation, this paper proposes a Thermal-Mass Coupled (TMC) dynamic model. The model captures the essential physical mechanisms of electrolytic cells, including alumina feed dynamics, ledge phase-change dynamics, and state-dependent current efficiency. The resulting nonlinear relations are reformulated into a tractable mixed-integer linear programming (MILP) model using Taylor approximations and McCormick-envelope reformulations. In the illustrative PBDR and IBDR case studies, the nonlinear reference-model assessment shows that the TMC model achieves net economic improvements 122.4% and 325.1% higher, respectively, than the ETP model. The proposed framework can therefore support deeper demand response participation by aluminum smelters while explicitly assessing adherence to operational safety constraints.