Multi-Period Optimization of Multi-Timescale Energy Systems: Application to Solid-Oxide Electrolysis Cells

Abstract

A presentation detailing the recent progress in the optimization of Solid-Oxide Cells under Chemical Degradation. The existing methods typically involve a quasi-steady state assumption to make this large-scale problem tractable. Here, we introduce an extension to this method that allows for adaptive coupling of degradation with the fast-timescale process based on a variety of error thresholds. This reduces the error accumulated during long-term optimization of the SOC under degradation and allows for decision making at multiple timescales. The results presented include long-term operating profiles of the SOC under degradation with both steady-state and fluctuating day to day operation.