Electrified refineries in the Power Flow Network
電力潮流ネットワークにおける電化製油所 (AI 翻訳)
Sampriti Chattopadhyay, A. I. Torres, I. E. Grossmann, Saif R. Kazi
🤖 gxceed AI 要約
日本語
本論文は、産業用電力需要と送電網の双方向相互作用を考慮した、電化製油所のグリッド認識最適化フレームワークを提案する。直流最適潮流モデルから得られる限界価格を用いて、石油精製所の運用最適化に組み込む。電化ボイラー、電解槽、水素貯蔵、炭素回収設備を備えた製油所のケーススタディでは、価格受容モデルと比較して運用コストが7%削減された。
English
This paper proposes a grid-aware optimization framework for electrified refineries that captures two-way interactions between industrial electricity usage and power flows. Using a DC Optimal Power Flow model to generate locational marginal prices, the framework embeds a surrogate model into refinery operational optimization. A case study with electric boilers, electrolyzers, H2 storage, and carbon capture shows 7% cost savings over a price-taker model. The method also incorporates demand uncertainty via chance-constrained DC-OPF.
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
日本の製油所は脱炭素化圧力が高まっており、自家発電や系統連系の最適化が重要である。本手法は、電力市場価格が自社需要に影響されることを考慮した運用最適化を可能にし、日本の製油所のコスト削減と脱炭素化に貢献し得る。
In the global GX context
This paper addresses a critical gap in industrial decarbonization: the interaction between large industrial loads and grid pricing. It provides a practical optimization method that can be applied globally to refineries and other energy-intensive industries seeking to electrify while minimizing costs. The grid-aware approach offers a more realistic planning tool than traditional price-taker models.
👥 読者別の含意
🔬研究者:This work presents a grid-aware optimization framework using surrogate models from DC-OPF, which can be extended to other industrial processes or integrated with renewable energy planning.
🏢実務担当者:Refinery operators can apply this method to reduce operating costs by anticipating how their power demand shifts electricity prices, especially when investing in electric boilers, electrolyzers, or carbon capture.
🏛政策担当者:Policymakers should consider grid-aware industrial optimization as a tool for cost-effective decarbonization, as it reveals synergies between industrial electrification and grid management.
📄 Abstract（原文）
Industrial decarbonization has heightened interest in electrifying major chemical processes, but existing planning methods typically assume fixed electricity prices and overlook how industrial power use affects the grid. This work introduces a grid-aware optimization framework that captures two-way interactions between industrial electricity usage and the power flows within the grid. We use the DC Optimal Power Flow (DC-OPF) model to generate Locational Marginal Prices across refinery demand levels and embed a surrogate reflecting the relationship between the power demand and the prices into an operational optimization problem for a partially electrified refinery. The surrogate model is embedded within the optimization problem using disjunctive reformulations and off-the-shelf packages such as OMLT (Optimization and Machine Learning Toolkit). In a case study considering an oil refinery with installed electric boilers, electrolyzers, H2 storage, and post-combustion carbon capture infrastructure, the grid-aware approach lowers operating costs by 7% relative to a price-taker model by anticipating how the refinery’s own demand shifts electricity prices. The method is also shown to incorporate the effect of demand uncertainty at other grid nodes by embedding a surrogate model trained using data generated by a chance-constrained DC Optimal Power Flow.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.69997/sct.116194first seen 2026-07-01 05:51:16
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