Decomposing a Multi-Scale Optimization Framework for Grid-Integrated Electrolysis using Aggregate-Informed Benders

Abstract

Demand response (DR) operation of electrolysis devices is gaining traction to capitalize on volatile electricity markets, but their dynamic operation poses challenges to the durability and lifespan of these systems. Our recently developed multi-scale optimization framework for grid-integrated electrolysis systems studied the impacts of DR and effects on device durability. A major hurdle in that work is tractably scaling the model to include participation in high-frequency electricity markets and/or longer time horizons. To address this, in this work, we developed an aggregate-informed Benders decomposition method to tractably solve large instances of this problem structure. To illustrate the efficacy, we solve a case study with market participation in the day-ahead market (DAM) and real-time markets (RTM) for up to 40 years to effectively capture the effects of device lifespan change. We compare our decomposition algorithm to both commercial solvers and traditional Benders decomposition. We find that incorporating aggregate subproblem information accelerates convergence, reducing the final optimality gap by up to ~81% relative to traditional Benders on the 40-year horizon instances that otherwise stall near 85%. We additionally apply the algorithm to find that RTM participation offers economic advantages under the higher price volatility.

Disclosure

“bles in linear programs. Operations Research DELCARATION OF USE OF AI 28:403-418 (1980) https://doi.org/10.1287/opre.28.2.403 Generative AI was used in programming the cluster- 9. Chattopadhyay S, Gandhi R, Grossmann IE, Torres ing algorithm and in producing the figures. The authors AI. A novel algorithm for bounding the optimal reviewed and edited the conten”

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Code generation, completion, or debugging

Multiplier

2

Verified

2026-08-23T19:03:18.706095Z

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