不確実な容量から低炭素経路へ：インド電力部門のための台形ファジー線形計画法アプローチ
From Uncertain Capacity to Low Carbon Pathways: A Trapezoidal Fuzzy Linear Programming Approach for India’s Power Sector (原題)
A. Vinay Bhushan, Chetan V. Hiremath, Mahantesh Halgatti, Soumya Gadag, S. C. Patil
🤖 gxceed AI 要約
日本語
インドの電源別容量計画に台形ファジー線形計画法を適用し、不確実性下での発電量最大化とCO2排出最小化のトレードオフを分析。アルファカットでクリスプ化し、パレートフロンティアを導出。計画の確信度が高まると実行可能領域が縮小し、最大発電量は11,659 TWh/年から2,927 TWh/年に減少、CO2指標も低下することを示した。
English
This paper applies trapezoidal fuzzy linear programming to India's power capacity planning, modeling uncertainty in capacity expansion. It derives a Pareto frontier between maximizing electricity generation and minimizing lifecycle CO2 emissions. As planning confidence increases, the feasible trade space contracts, reducing maximum generation from 11,659 TWh/year to 2,927 TWh/year while lowering the CO2 indicator. The method offers a transparent link between optimistic and conservative assumptions and low-carbon pathways.
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、電源計画の不確実性を扱う手法として参考になる。特に、再生可能エネルギー導入拡大に伴う系統安定性や容量確保の議論において、ファジー計画法による柔軟な評価は、日本の長期エネルギー需給見通しや次期エネルギー基本計画の策定に示唆を与える。
In the global GX context
Globally, this paper contributes to energy planning under uncertainty, relevant for countries pursuing low-carbon transitions. Its fuzzy linear programming approach offers a transparent method to handle uncertain capacity expansion, which is valuable for integrated resource planning and aligning with climate targets. The Pareto frontier analysis provides insights into trade-offs between generation and emissions, useful for policymakers and utilities.
👥 読者別の含意
🔬研究者:Methodological contribution: fuzzy linear programming for power planning under uncertainty, with a clear Pareto frontier.
🏢実務担当者:Potentially useful for utility planners or consultants in capacity expansion modeling, though India-specific.
🏛政策担当者:Insights into how planning confidence affects feasible low-carbon pathways, relevant for energy policy design.
📄 Abstract（原文）
India’s electricity system must expand generation fast enough to support economic growth while also reducing the emissions intensity of the power mix. When future capacity expansion is uncertain and better described by credible ranges than by single-point estimates, fuzzy linear programming becomes a suitable planning tool because it represents feasibility in graded rather than purely binary terms . This paper develops a trapezoidal fuzzy linear programming framework for India’s source-wise electricity capacity planning across coal, gas, solar, wind, hydro, and nuclear technologies. Installed capacity, core expansion bands, and outer support are represented as trapezoidal fuzzy numbers; alpha-cuts are then used to convert the fuzzy model into crisp linear-programming . The model has two goals: maximize annual electricity generation and minimize lifecycle CO2 emissions. Published lifecycle harmonization studies are used to justify the emissions ordering across technologies, with coal and gas remaining substantially more carbon intensive than solar, wind, hydro, and nuclear. Solving the crisp subproblems over yields a Pareto frontier that clearly shows how the feasible trade space contracts as planning confidence increases . The results indicate that the maximum-generation solution declines from 11,659.03 TWh/year at to 2,926.61 TWh/year at , while the corresponding CO2 indicator falls from 2.0478 to 1.6792. The minimum-emissions solution remains at the 2,500 TWh/year demand floor, while its CO2 indicator rises from 1.3426 to 1.4480 as the feasible set narrows. These results show that trapezoidal fuzzy linear programming provides a transparent way to connect optimistic and conservative planning assumptions to low-carbon electricity expansion pathways.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70917/ijcisim-2026-5309first seen 2026-09-02 04:52:34
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