Leveraging Quantum Annealing for Large-Scale Household Energy Scheduling with Hydrogen Storage
Abstract
Hydrogen integration into microgrids facilitates the absorption of intermittencies from renewable energy resources. However, significant challenges remain due to complex optimization problems, particularly in large-scale applications involving multiple fuel cells (FCs) and electrolyzers (ELs) with numerous binary decision variables. This paper presents a hierarchical quantum annealing (QA) model predictive control-based power allocation framework aimed at accelerating these optimization problems. First, in a day-ahead stage, the framework determines the startup and shutdown of the FCs and ELs. The short-term stage then refines the output power of the FCs and the hydrogen generation rate of the ELs. The feasibility is evaluated through a case study consisting of multiple households in Australia. Our findings demonstrate that while the traditional optimization approach performs satisfactorily in scenarios with a small number of households, the QA approach becomes more appropriate and effectively solves the problem within an acceptable range as the number of connected households increases.
Keywords
Cite
@article{arxiv.2603.07823,
title = {Leveraging Quantum Annealing for Large-Scale Household Energy Scheduling with Hydrogen Storage},
author = {Arash Khalatbarisoltani and Amin Mahmoudi and Jie Han and Muhammad Saeed and Wenxue Liu and Jinwen Li and Solmaz Kahourzade and Amirmehdi Yazdani and Xiaosong Hu},
journal= {arXiv preprint arXiv:2603.07823},
year = {2026}
}