Embedding resilience in energy system deep decarbonisation pathways for emerging economies: The case of India
新興経済国におけるエネルギーシステムの深い脱炭素化経路へのレジリエンスの組み込み：インドの事例 (AI 翻訳)
Praveen P, Fernando Plazas-Niño, Balachandra Patil
🤖 gxceed AI 要約
日本語
本研究は、インドの電力部門を対象に、構造的移行の不確実性を考慮した長期エネルギー計画にレジリエンスを組み込むモデリング手法を提案する。最低限の確実かつディスパッチ可能な容量（FDC）要件を課すことで、20%のFDC要件では再生可能エネルギー普及率への影響はわずか（約1%減少）で、コスト上昇も僅かであるが、40%以上では水力・バイオマスの限界により、CCS付き石炭や原子力が必要となることを示した。
English
This paper proposes a modeling approach to embed resilience in long-term energy system planning for deep decarbonization, applied to India's electricity sector. By imposing minimum firm and dispatchable capacity (FDC) requirements, it shows that a 20% FDC constraint reduces renewable penetration by only ~1% with a minimal cost premium, but higher FDC requirements exhaust hydropower and biomass potentials, necessitating low-carbon firm resources like coal with CCS and nuclear. The results highlight the need for diversified low-carbon portfolios in emerging economies.
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
インドを事例とするが、日本においても再生可能エネルギーの大量導入に伴う系統安定性やレジリエンスの確保は重要な課題である。本論文が提案するFDC要件の導入は、日本の長期エネルギー計画やGX政策における電源構成の多様化の議論に示唆を与える可能性がある。
In the global GX context
This paper contributes to the global debate on deep decarbonization in emerging economies, highlighting the need for resilience and diversified low-carbon portfolios. It offers a novel modeling approach that operationalizes resilience through FDC constraints, relevant for energy transition planning in countries with infrastructure and governance uncertainties. The findings support the integration of technologies like CCS and nuclear alongside renewables.
👥 読者別の含意
🔬研究者:Energy system modelers and climate policy researchers should note the novel integration of resilience constraints via FDC requirements into long-term optimization models.
🏢実務担当者:Corporate sustainability teams in the energy sector can use the insights on FDC requirements to inform capacity planning and technology portfolio decisions.
🏛政策担当者:Policymakers in emerging economies can apply the modeling framework to balance renewable deployment with grid reliability, considering firm dispatchable capacity.
📄 Abstract（原文）
The technical feasibility of 100% renewable energy (RE) pathways in electricity systems depends on enabling conditions such as modern, fast-response grid infrastructure, mature demand response, large-scale storage deployment, the availability of non-variable renewable resources and adequate governance capacity. These conditions may be difficult to realise in many countries in the Global South that face structural transition uncertainties, including infrastructure gaps, renewable resource limitations, and governance issues. This creates a need to embed resilience into the long-term planning frameworks for increasing the likelihood of achieving deep decarbonisation. The study proposes a modelling approach that limits the impact of the above uncertainties by operationalising resilience through the imposition of minimum firm and dispatchable capacity (FDC) requirements within a long-term energy system optimisation model. The framework is applied to India’s electricity sector through a time horizon until 2050, employing six different scenarios. The results demonstrate that deep decarbonisation under realistic Indian conditions requires a diversified portfolio of low-carbon technology alongside RE. The key finding is that a 20% FDC requirement reduces renewable penetration by only ~1% relative to the unconstrained scenario, with a minimal cumulative investment premium of 2.7% (USD 39 billion), largely because hydropower can satisfy the constraint at low cost. However, under higher FDC requirements of 40% and 60%, hydropower and biomass potentials are exhausted, favouring the introduction of low-carbon firm resources such as coal with carbon capture and nuclear into the capacity mix. These additions reduce the storage requirements by 51% and 84%, respectively.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.33774/coe-2026-fgqpj-v2first seen 2026-05-14 22:06:47
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