再生可能エネルギー源と水素貯蔵を備えたハイブリッドマイクログリッドのスケジューリング：データ相関不確実性モデリングと半凸緩和
Scheduling for Hybrid Microgrids With Renewable Energy Sources and Hydrogen Storage: Data-Correlated Uncertainty Modeling and Semi-Convex Relaxation (原題)
Zipeng Liang, C. Chung, X. Yin, Qin Wang, Haoyong Chen, Jizhong Zhu
🤖 gxceed AI 要約
日本語
再エネ・水素貯蔵・EVを統合したハイブリッドマイクログリッドの運用最適化手法を提案。データ駆動型の相関不確実性集合と半凸緩和により、非凸制約と再エネ不確実性を効率的に処理する。数値事例で既存手法より高いクリーンエネルギー導入率を達成できることを示した。
English
This paper proposes an energy management framework for hybrid microgrids integrating renewables, hydrogen storage, and EVs. It combines a data-correlated uncertainty set with a semi-convex relaxation to handle non-convex constraints and RES uncertainty efficiently. Case studies show higher clean-energy penetration than state-of-the-art methods.
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
日本では再エネ大量導入と水素社会実現がGX政策の柱であり、マイクログリッド運用の高度化は地域脱炭素・電力系統安定化に直結する。水素貯蔵とEVを統合した運用最適化は、企業の再エネ調達や系統連系コスト低減に寄与しうる。
In the global GX context
Globally, this work supports the integration of variable renewables and hydrogen storage into microgrids, a key element of power-sector decarbonization pathways. It offers a computationally efficient method for managing uncertainty and non-convex constraints, relevant to energy transition planning and grid flexibility.
👥 読者別の含意
🔬研究者:非凸制約と不確実性を扱うロバスト最適化の新しい緩和手法として、エネルギーシステム研究に有用。
🏢実務担当者:マイクログリッド運用コストの低減と再エネ導入率向上のための実装可能な最適化アプローチを提供する。
🏛政策担当者:水素・EV統合マイクログリッドの普及促進に向けた系統運用・市場設計の参考になる。
📄 Abstract（原文）
The increasing integration of renewable energy sources (RESs), hydrogen energy storage (HES), and electric vehicles (EVs) is critical to the decarbonization of electric power production; however, it introduces significant challenges for microgrid energy management owing principally to RES uncertainty and the non-convex constraints associated with HES and EV operations. The present work addresses these challenges by applying data-driven uncertainty set optimization and semi-convex relaxation approaches. First, a probability-driven data-correlated uncertainty set is developed using historical data distributions to generate RES uncertainty representations with reduced conservativeness under significantly reduced computational burden. Second, a closed-loop semi-convex relaxation procedure is designed to manage the non-convex operational constraints of HES and EV operations, where the NP-hard nature of these constraints is addressed by iteratively enforcing only violated binary conditions, which ensures solution accuracy with minimal computational overhead. Third, the resulting tri-level robust energy management model is solved efficiently by a customized column-and-constraint generation algorithm incorporating the proposed relaxation procedure. As a result, only those non-convex constraints that are violated in the relaxed solution are included in the energy management solution process. The superiority of the proposed approach over existing state-of-the-art methods in enabling higher penetrations of clean energy technologies is demonstrated through numerical case studies on a practical hybrid microgrid system.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tste.2026.3704151first seen 2026-09-25 05:07:07 · last seen 2026-09-29 05:20:31
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