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PyPSAを用いた製油所エネルギーシステムのデータ駆動最適化：柔軟性と統合的脱炭素オプションの探求

Data-Driven Optimisation of Refinery Energy Systems: Exploring Flexibility and Integrated Decarbonisation Options with PyPSA (原題)

Francesco Ghionda, Diego Viesi, Edoardo Gino Macchi, Corrado Gagliani, Ionela Simona Toma, Attilio Tozza

Sustainability📚 査読済 / ジャーナル2026-08-27#エネルギー転換Origin: EU経営インパクト: コスト削減対象セクター: refining
DOI: 10.3390/su18178809
原典: https://doi.org/10.3390/su18178809

🤖 gxceed AI 要約

日本語

本研究は、PyPSAを用いて製油所の多エネルギーシステムの運用最適化と脱炭素技術の評価を行うデータ駆動型フレームワークを提案。年間の時間分解能で設備の柔軟性とセクターカップリングを考慮し、コスト最小化を実現。ケーススタディでは年間約60万トンCO2の排出を最大7.2%削減しつつ、システムコストを参照ケースと同等に維持できることを示した。既存設備の柔軟な運用が運用コスト削減の鍵となる。

English

This study proposes a data-driven optimization framework using PyPSA to evaluate refinery energy systems, integrating operational flexibility and decarbonization technologies. The model achieves up to 7.2% annual emissions reduction (around 600 ktonCO2eq/y) while maintaining system costs comparable to the reference case. Flexible scheduling of existing assets emerges as a key driver of operational savings, highlighting the potential of industrial multi-energy systems for demand-side flexibility.

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・化学産業はSSBJ開示やGX推進政策の下で脱炭素投資が急務。本手法は既存設備の柔軟運用と技術導入を統合評価する実践的ツールを提供し、日本のエネルギー集約産業の脱炭素計画や投資判断に示唆を与える。

In the global GX context

This work contributes to global GX scholarship by demonstrating a data-driven, open-source approach (PyPSA) for optimizing industrial energy systems, aligning with ISSB/CSRD disclosure requirements for transition planning. It provides a replicable framework for assessing cost-effective decarbonization strategies in energy-intensive industries, relevant for global refining and chemical sectors.

👥 読者別の含意

🔬研究者:Provides a validated PyPSA-based framework for multi-energy system optimization with hourly resolution, useful for industrial decarbonization research.

🏢実務担当者:Offers a tool to evaluate cost-effective decarbonization options and operational flexibility in refinery energy systems, aiding investment and transition planning.

🏛政策担当者:Demonstrates the potential of industrial demand-side flexibility and sector coupling, informing policies for industrial decarbonization and grid integration.

📄 Abstract（原文）

Industrial refineries operate as complex multi-energy systems where electricity, process fuels, steam, and multiple energy carriers interact across tightly coupled processes. This work presents a data-driven optimisation framework implemented in PyPSA to analyse operational dispatch and evaluate alternative decarbonisation technologies in a refinery energy system. The model integrates detailed historical operational data and hourly efficiency profiles to replicate assets’ behaviour and explore different technology configurations over a reference year. The objective function includes annualised capital expenditures and operating costs, including maintenance and energy carrier costs, enabling a consistent comparison of candidate technologies within the operational optimisation. The analysis provides insights into the potential contributions of existing assets’ flexibility and sector coupling to the identification of cost-efficient refinery decarbonisation strategies. Key performance indicators include total system cost, technology utilisation factors, and the resulting configuration of the energy system. The proposed full-year, hourly resolved LP approach offers a data-driven tool for exploring operational improvements and evaluating technology options under realistic operating conditions in industrial energy systems. The identified cost-optimal solutions for the multi-energy sub-system object of the study achieve annual emissions reductions of up to 7.2% of around 600 ktonCO2eq/y while maintaining annual system costs comparable to those of the reference case. Across all scenarios, flexible scheduling of the existing generation assets consistently emerges as a key driver of operational savings, highlighting the potential of industrial multi-energy systems to provide market-responsive demand-side flexibility through the adaptation of energy dispatch in response to electricity market signals.

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