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段階的炭素取引と再エネ不確実性を考慮した産業パーク統合エネルギーシステムの二段階分布ロバスト最適化スケジューリング

Two-Stage Distributionally Robust Optimization Scheduling for Industrial Park Integrated Energy Systems Considering Tiered Carbon Trading and Renewable Energy Uncertainty (原題)

Hai Jiang, Li Xiong, HaoShuai Jia, Guijun Liu, Rongbo Chen, Feiyang Ma, Haoming Liu

Energies📚 査読済 / ジャーナル2026-10-19#炭素価格Origin: CN経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.3390/en19204950
原典: https://doi.org/10.3390/en19204950

🤖 gxceed AI 要約

日本語

産業パークの電熱ガス水素統合エネルギーシステムを対象に、段階的炭素取引コストと再エネ出力不確実性を組み込んだWasserstein距離ベースの二段階分布ロバスト最適化モデルを構築。水素貯蔵が再エネ電力の時間移転と効率的吸収を実現し、従来のボックス不確実性ロバスト手法より経済性と堅牢性のバランスに優れることをアルミ系産業パークのケースで示した。

English

This paper builds a Wasserstein-distance two-stage distributionally robust optimization model for an electricity-heat-gas-hydrogen integrated energy system in industrial parks, minimizing operation, energy purchase, tiered carbon trading, and curtailment costs under renewable uncertainty. A hydrogen storage system enables cross-period transfer and efficient renewable accommodation, and an aluminum-materials park case shows a better economy-robustness balance than conventional box-uncertainty robust optimization.

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

中国の段階的炭素取引制度を前提とした最適化だが、炭素コストを運用意思決定に内部化する手法は、日本企業がICPやGX-ETS下で炭素価格を織り込む際の参考になる。産業パーク単位の脱炭素運用設計として、SSBJ開示以前のScope1・2削減実務に示唆を与える。

In the global GX context

The model internalizes tiered carbon-trading costs into day-ahead operational decisions, offering a template for how carbon pricing can be embedded in industrial energy scheduling. For global disclosure scholarship, it links operational carbon-cost optimization to the emissions-reduction evidence that underpins TCFD/ISSB transition planning.

👥 読者別の含意

🔬研究者:炭素価格と再エネ不確実性を同時に扱う分布ロバスト最適化の定式化と求解手法が参考になる。

🏢実務担当者:産業パークのエネルギー運用に炭素コストと水素貯蔵を組み込む設計の実務的示唆が得られる。

🏛政策担当者:段階的炭素取引制度が産業部門の運用意思決定と再エネ吸収に与える効果を制度設計に反映できる。

📄 Abstract（原文）

Industrial parks serve as the core carriers for low-carbon transition in the industrial sector, while multi-energy complementary integrated energy systems (IESs) represent the key technological pathway for achieving efficient decarbonization in such parks. However, the output volatility of high-penetration renewable energy sources, coupled with the scheduling complexity under carbon emission constraints, poses severe challenges to day-ahead decision-making for park-level integrated energy systems. To address this issue, this paper developed a two-stage distributionally robust optimization (DRO) scheduling model based on the Wasserstein distance. First, an electricity–heat–gas–hydrogen IES architecture is established, and a day-ahead scheduling optimization model is formulated with the objective of minimizing the sum of system operation and maintenance costs, energy purchase costs, tiered carbon trading costs, and renewable curtailment penalty costs. Second, to cope with the uncertainty of wind and solar power outputs, the recourse decisions are parameterized after uncertainty achievement, transforming the original problem into a single-level program that can be directly solved by commercial solvers. Finally, simulation verification is conducted using an aluminum-based new material industrial park as a case study. The results demonstrate that the hydrogen energy storage system can effectively achieve cross-period transfer and efficient accommodation of renewable electricity. Compared with the conventional box-uncertainty robust optimization method, the proposed approach achieves a superior balance between scheduling economy and operational robustness, validating the effectiveness and engineering practicality of the model.

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