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Energy Management of a Renewable-Powered Alkaline Electrolyzer System: A Comparative Study of Nonlinear Optimization Methods

再エネ駆動のアルカリ水電解システムのエネルギー管理：非線形最適化手法の比較研究 (AI 翻訳)

Loukas Kyriakidis, Jonaed Bin Mustafa Kamal, Saskia Bublitz, Bogdan Dorneanu, H. Arellano-Garcia

Systems and Control Transactions2026-06-19#水素経営インパクト: コスト削減対象セクター: industry
DOI: 10.69997/sct.177956
原典: https://doi.org/10.69997/sct.177956

🤖 gxceed AI 要約

日本語

本研究は、太陽光発電を利用した水素製造システムのエネルギー管理に、ベイズ最適化と内点法を組み合わせたハイブリッド最適化手法BO-IPOPTを提案。従来手法と比較して、計算時間を同等に保ちながら運用コストを低減し、制約条件を満たすことを実証。PV発電の不確実性がシステム性能に与える影響も分析した。

English

This paper proposes BO-IPOPT, a hybrid optimization method combining Bayesian Optimization and Interior Point Optimizer for energy management of a renewable-powered hydrogen production system. It achieves lower operational costs with the same computational time compared to state-of-the-art methods, while satisfying all constraints. The impact of PV generation uncertainty is also analyzed.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本は水素社会の実現を目指しており、再生可能エネルギー由来の水素製造の効率化は重要。本論文のBO-IPOPT手法は、変動する再エネ電源下での運用最適化に寄与し、日本の水素サプライチェーン構築に示唆を与える。

In the global GX context

As hydrogen production scales up globally, efficient energy management under renewable uncertainty is critical. This paper's hybrid optimization approach demonstrates cost savings in real-time operation, relevant for industrial electrolyzer deployment in regions with high PV penetration.

👥 読者別の含意

🔬研究者:This paper provides a novel hybrid optimization method (BO-IPOPT) for nonlinear control of hydrogen systems with renewable energy, offering a benchmark for future studies in energy management.

🏢実務担当者:Hydrogen plant operators can adopt the BO-IPOPT approach to reduce operational costs while ensuring constraint satisfaction in fluctuating renewable conditions.

📄 Abstract（原文）

Energy management plays a crucial role in achieving efficient and sustainable operation of industrial energy systems. With the increasing integration of renewable electricity and the growing complexity of hydrogen production networks, effective control strategies are required to minimize operational costs and carbon footprint. However, the uncertain nature of renewable energy sources, such as photovoltaic (PV) power, complicates their accurate forecasting and challenges the optimal energy management of system components. To deal with uncertainties, the rolling horizon approach (RHA) provides a practical framework for adaptive decision-making by repeatedly solving optimization problems over moving time windows while updating system data in real time. In RHA-based energy management, linear or linearized system models are often employed and optimized by linear methods to reduce computational complexity; however, these simplifications can compromise physical realism and lead to suboptimal decisions. Although RHA can also incorporate local, or global deterministic and stochastic algorithms for nonlinear problems, such approaches frequently suffer from high computational effort, slow convergence, local optima, and difficulty in ensuring constraint satisfaction in large-scale nonlinear systems. To overcome these limitations, this work employs the novel hybrid optimization method “BO-IPOPT”—a combination of Bayesian Optimization (BO) for global exploration and the Interior Point OPTimizer (IPOPT) for rapid local refinement. Applied to an industrial hydrogen production system, BO-IPOPT outperforms state-of-the-art approaches in accuracy and robustness by achieving lower operational costs at the same CPU time while satisfying all constraints. Finally, the influence of the uncertainties in PV generation on the performance of the energy management system is analyzed.

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