Modelling and optimization of solar integrated green hydrogen supply chain management: a genetic approach
太陽光統合グリーン水素サプライチェーン管理のモデリングと最適化：遺伝的アプローチ (AI 翻訳)
Anjali, Anand Chauhan, Abhinav Goel
🤖 gxceed AI 要約
日本語
グリーン水素のサプライチェーンを最適化する研究。太陽エネルギーと処理済み廃水を利用し、混合整数線形計画法と遺伝的アルゴリズムで総コストを最小化。感度分析により、設備投資と輸送手段がコストに大きく影響することを示し、パイプラインが長距離で圧縮水素トラックより8.5%経済的であると結論。正味現在価値と内部収益率から財務的安定性も確認。
English
This study designs and optimizes a sustainable green hydrogen supply chain using solar energy and treated wastewater. A mixed-integer linear programming model and genetic algorithm minimize total costs including installation, production, storage, and transportation. Sensitivity analysis shows that capital investment and transport mode significantly affect costs, with pipelines being 8.5% more economical than compressed hydrogen trucks for long distances. Financial indicators confirm the viability of the proposed supply chain.
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
日本では水素基本戦略に基づきグリーン水素の供給網構築が進められている。本論文の最適化手法は、国内の太陽光発電と水素プラントの立地選定やコスト評価に応用可能であり、政策立案や投資判断に示唆を与える。
In the global GX context
This paper contributes to global hydrogen supply chain optimization, relevant for countries investing in green hydrogen infrastructure. The use of a genetic algorithm for cost minimization and sensitivity analysis on transport modes provides practical insights for project developers and policymakers working towards decarbonization targets.
👥 読者別の含意
🔬研究者:Provides a decision-support framework integrating optimization and financial analysis for green hydrogen supply chains.
🏢実務担当者:The cost breakdown and sensitivity analysis can guide investment and operational decisions for solar-integrated green hydrogen projects.
🏛政策担当者:Highlights critical cost drivers and infrastructure choices (e.g., pipelines vs. trucks), informing subsidy design and infrastructure planning.
📄 Abstract（原文）
The continued growth in energy demand and a pressing need to lower greenhouse gas emissions have shifted the focus toward clean and sustainable energy alternatives. The purpose of this research study is to produce green hydrogen powered by renewable energy sources that reduces the dependency on fossil fuels and supports the transition to a low-carbon energy system. The main goal of this study is to design and optimize a sustainable and cost-effective green hydrogen supply chain network using solar energy and treated wastewater. A mixed-integer linear programming model is designed to minimize the system’s total cost, which consists of installation, production, storage and transportation costs. Financial indicators like net present value, internal rate of return and payback period are evaluated for strategic and investment decision management. A genetic algorithm is used to optimize the total cost of the network by locating the optimum solution. Numerical example reveals that the solar plant installation cost and the green hydrogen plant installation cost are the main contributors to the cost, which account for 64.71% and 32.91%, respectively. Sensitivity analysis highlights that the capital investment and the transport mode significantly influence the total cost. It is found that pipelines are 8.5% economical than compressed hydrogen trucks for long distances. A positive net present value and 18.21 % internal rate of return confirm the financial stability of the proposed supply chain. The proposed framework provides valuable insights for policymakers, planners and industry stakeholders, helping to accelerate hydrogen and support the global framework.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/jm2-11-2025-0641first seen 2026-07-20 05:27:09
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