病院部門におけるエネルギー転換に適した技術のエネルギー・経済分析
Energy and Economic Analysis of Technologies Suitable for Energy Transition in the Hospital Sector (原題)
Giulia Anna Maria Castorino, Lucrezia Manservigi, Pier Ruggero Spina, Mauro Venturini
🤖 gxceed AI 要約
日本語
病院部門を対象に、2050年までのエネルギー転換に有効な変換技術と蓄電システムを特定する。粒子群最適化と混合整数線形計画法を統合し、多世代エネルギーシステムの容量と運用を同時最適化する。イタリア・トリノの仮想病院に適用した結果、将来シナリオの投資はNPV最大460万ユーロで収益性が確認され、初期投資は耐用年数内に回収される。電力価格と燃料費が収益性に与える影響も感度分析で示す。
English
This study identifies energy conversion and storage technologies likely to lead the hospital sector's transition to 2050. It integrates particle swarm optimization with mixed-integer linear programming to jointly optimize sizing and operation of a multi-generation energy system, applied to a hypothetical hospital in Torino, Italy. Future-scenario investments are profitable (NPV up to €4.6M) with positive discounted ROI and payback well within system lifetime. Sensitivity analysis shows how electricity and fuel prices affect profitability.
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
病院等の公共施設はScope1・2排出とエネルギーコストの双方で重要であり、本手法は日本企業・自治体の脱炭素投資計画やSSBJ対応のScope算定・削減戦略に応用可能な最適化枠組みを提供する。
In the global GX context
Hospitals are energy-intensive public facilities whose decarbonization matters for Scope 1/2 accounting and transition planning. The optimization framework offers a replicable method for sizing and operating multi-generation systems under future energy-price uncertainty, relevant to TCFD/ISSB transition-plan disclosures and public-sector net-zero strategies.
👥 読者別の含意
🔬研究者:多世代エネルギーシステムの容量・運用同時最適化にPSOとMILPを統合した手法と、価格感度分析の枠組みが参考になる。
🏢実務担当者:病院・公共施設の脱炭素投資判断において、将来の電力・燃料価格変動下での収益性評価と設備サイジングに活用できる。
🏛政策担当者:公共施設のエネルギー転換支援策を設計する際、価格シグナルが投資回収に与える影響を踏まえた制度設計の示唆を得られる。
📄 Abstract（原文）
The energy sector is currently facing the challenge of achieving energy transition targets while maintaining a reliable and economically sustainable energy supply. In this context, this study aims to identify the energy conversion technologies and storage systems that are expected to play a leading role in the hospital sector up to 2050. To this end, an optimization approach is developed to simultaneously optimize both the size and operation of a multi-generation energy system (MES) by means of the integration of two algorithms, i.e., particle swarm optimization and mixed-integer linear programming. The proposed approach is applied to a hospital hypothetically located in the city of Torino (Italy). For the considered projections, investments for future scenarios are profitable with NPV values up to 4.6 M€ and a discounted return on investment that is always positive in all the investigated cases. In addition, the initial investment is recovered well before the end of the MES’ expected lifetime. An additional takeaway from this study is the sensitivity analysis that provides guidelines about the influence of both electricity price and fuel cost on investment profitability. Furthermore, the proposed optimization framework supports operational decision-making by identifying cost-effective sizing and management strategies under different future energy scenarios, thereby facilitating informed investment planning and contributing to the energy transition of hospitals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19194489first seen 2026-10-09 04:40:23
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