A Multi-Scale Optimization Framework for Grid-Integrated Electrolysis

Abstract

The increasing penetration of wind and solar resources into the power grid motivates the integration of flexible technologies to dynamically shift power loads in response to grid volatility and emergency events. The water electrolyzer presents a synergistic opportunity to provide flexibility through demand response (DR), while simultaneously electrifying hydrogen production; however, highly dynamic operation schedules accelerate device degradation. This work presents a mixed-integer linear program (MILP) optimization framework to study the multi-scale coupling between short-term operational flexibility provision in electrolysis devices and long-term stack replacement decisions driven by degradation. Active day-ahead market (DAM) participation of a 2.2 MW alkaline water electrolyzer over 22 years is solved as a case study. Our framework reveals that the multi-scale scheduling of DR operation and replacement decisions can extend optimal stack lifetimes by up to 2 years through load-shifting and further reduce lifetime electricity expenses by 33% relative to inflexible constant operation. Furthermore, we quantify key device parameter tradeoffs and next-generation design goals, where our analysis challenges the feasibility of the standard \$1/kg levelized cost of hydrogen (LCOH) production target solely through market arbitrage. Ultimately, this framework quantifies the largely unexploited economic value of multi-scale optimization in grid-integrated electrolysis.