「低炭素産業団地の分布的ロバスト計画：サウジアラビア東部州のソース・負荷不確実性下でのCCS–Power-to-Methanol連携」の複製データとコード
Replication data and code for "Distributionally Robust Planning of Low-Carbon Industrial Parks: CCS–Power-to-Methanol Coupling under Source–Load Uncertainty in the Eastern Province of Saudi Arabia" (原題)
Majdi Argoubi
🤖 gxceed AI 要約
日本語
本リポジトリは、CCSとPower-to-Methanol（P2M）を統合した産業団地のエネルギーシステム計画モデル（DRO）の入力データ・パラメータ・コードを提供する。2つの代表シナリオ（K-meansクラスタリングで生成）と設備投資・運用パラメータ、段階的炭素取引価格を含む。MATLAB/YALMIPのC&CGアルゴリズムのテンプレートも含む。
English
This repository provides input data, parameters, and code for a two-stage distributionally robust optimization (DRO) model for planning a low-carbon industrial park integrating CCS and power-to-methanol (P2M). It includes two representative scenarios from K-means clustering, techno-economic parameters, and a MATLAB/YALMIP template for the C&CG algorithm. The case study is an industrial park in Saudi Arabia's Eastern Province.
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、産業団地の脱炭素化が急務であり、CCSやe-fuel（メタノール）の導入が検討されている。本モデルは不確実性下での最適設計を示すもので、日本のコンビナートや臨海部の産業集積地における計画に応用可能。ただし、サウジアラビアのデータに基づくため、日本のエネルギー価格や炭素価格に合わせた調整が必要。
In the global GX context
Globally, this work contributes to the literature on integrated energy system planning with CCS and power-to-X, addressing source-load uncertainty via DRO. It offers a replicable framework for low-carbon industrial park design, relevant to regions pursuing industrial decarbonization and hydrogen-based fuels. The data and code enable benchmarking and adaptation to other contexts.
👥 読者別の含意
🔬研究者:Provides a reproducible DRO model and data for CCS-P2M integrated planning, useful for benchmarking and methodological extensions.
🏢実務担当者:Offers a planning framework for industrial parks considering CCS and e-methanol, though adaptation to local parameters is required.
🏛政策担当者:Demonstrates a quantitative approach to evaluate low-carbon industrial park configurations, informing incentives for CCS and P2M.
📄 Abstract（原文）
This repository contains the input data, model parameters, and supporting code for the two-stage distributionally robust optimization (DRO) capacity-planning model of a park-level integrated energy system (IES) coupling carbon capture (CCS) and power-to-methanol (P2M), described in theassociated paper. Contents:- data/: the two representative per-unit operating scenarios (24 hourly steps × 5 uncertainty variables) obtained by improved K-means clustering, their empirical probabilities (0.569 / 0.431), and the base values used for de-normalization.- parameters/: equipment investment/O&M/lifetime, thermodynamic and efficiency parameters, market and stepped-carbon-trading parameters, energy-storage parameters, and capacity bounds (all monetary values in USD).- code/: a working Python utility that loads, de-normalizes and plots the input scenarios, and an illustrative MATLAB/YALMIP template showing the structure of the column-and-constraint generation (C&CG) algorithm.- figures/: de-normalized scenario plot. The equipment cost and efficiency parameters were compiled from publicly available techno-economic literature. The operating profiles arerepresentative daily scenarios for an industrial park in the Eastern Province of Saudi Arabia. The complete production optimization model isavailable from the corresponding author on reasonable request.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.22010219first seen 2026-08-22 04:55:44 · last seen 2026-08-22 04:55:46
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