Spatio-Temporal Multi-Criteria Optimization Framework for Strategic Siting of Large-Scale Solar–Hydrogen Hybrid Plants: A Case Study of India (Rajasthan–Gujarat Corridor)
大規模太陽光‐水素ハイブリッド発電所の戦略的立地選定のための時空間多基準最適化フレームワーク：インド（ラジャスタン‐グジャラート回廊）のケーススタディ (AI 翻訳)
Kunj Kumar Dubey, Sujeet Kumar Singh, Bhim Kumar Mahato
🤖 gxceed AI 要約
日本語
本研究では、大規模太陽光水素製造施設の最適立地を決定するための時空間最適化フレームワークを提案。AHP、Starfish最適化アルゴリズム、モンテカルロシミュレーションを組み合わせ、インドのラジャスタン州とグジャラート州で検証した結果、カッチ地域が最適地点として82%の確率で選ばれた。
English
This study proposes a spatio-temporal optimization framework for siting large-scale solar-hydrogen production facilities, combining AHP, Starfish Optimization Algorithm, and Monte Carlo simulation. Applied to high-potential regions in India, Kutch emerges as the best site with 82% probability, demonstrating the framework's robustness.
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
インドのグリーン水素導入戦略に資するが、日本でもSSBJ報告やエネルギー政策と関連し、水素インフラ立地評価手法として参照可能。
In the global GX context
While focused on India, the framework provides a transferable decision-support tool for global green hydrogen infrastructure planning, relevant to ISSB-aligned transition finance and national hydrogen strategies.
👥 読者別の含意
🔬研究者:A novel hybrid optimization approach for green hydrogen site selection that integrates stochastic and deterministic methods.
🏢実務担当者:Provides a multi-criteria framework that can be adapted for corporate renewable hydrogen project development and site screening.
🏛政策担当者:Offers quantitative evidence for prioritizing regions for green hydrogen hubs, informing national hydrogen roadmaps.
📄 Abstract（原文）
The need for effective green hydrogen infrastructure planning has increased because renewable energy resources are abundant in areas that are transitioning to sustainable energy systems. This study presents a spatial-temporal optimization framework which employs multi-criteria evaluation to determine optimal locations for large-scale solar powered hydrogen production facilities through the combination of Analytical Hierarchy Process and Starfish Optimization Algorithm and Monte Carlo Simulation. The framework evaluates site suitability based on a comprehensive set of criteria, including solar irradiance, temperature, water availability, infrastructure accessibility, land suitability, and demand distribution. The model is applied to selected high-potential regions in Rajasthan and Gujarat, where normalized multi-criteria datasets are processed within a GIS-based environment. The AHP-derived weights indicate that solar irradiance (0.22), water availability (0.15), and grid accessibility (0.14) are the most influential factors. Initial suitability analysis identifies Kutch and Ahmedabad as leading candidates with scores of 0.69 and 0.66, respectively. The application of SFOA further improves these scores to 0.74 and 0.70, demonstrating enhanced optimization performance with convergence exceeding 0.94. The study uses Monte Carlo simulation with 1000 iterations to test robustness by introducing random fluctuations to essential parameters which include solar power and water supply and demand. Kutch achieved the highest suitability score which reached 0.73 with Achieved stability which showed low standard deviation of 0.04 while Ahmedabad scored 0.69. Jaisalmer shows more variability because of its existing infrastructure and environmental limitations. The probability analysis shows that Kutch has an 82% chance of being the best site. The study results show that Rajasthan operates as a renewable energy production area while Gujarat acts as a processing and export center for hydrogen. The proposed framework demonstrates strong capability as a decision-support tool for hydrogen infrastructure planning by combining deterministic optimization with stochastic validation. The approach provides policymakers with practical solutions while building an Indian green hydrogen economy which can function in multiple regions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.63148/01.2026024first seen 2026-06-27 05:26:55
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。