Optimal Energy Transition Planning: Navigating the Trade-Offs between Short-Term and Long-Term Decision-Making
最適なエネルギー転換計画：短期と長期の意思決定のトレードオフを乗り越える (AI 翻訳)
Xin Hui Cheng, Irene Moser, Stephen Doliente, Bing Shen How, Viknesh Andiappan
🤖 gxceed AI 要約
日本語
本論文は、複数エネルギーキャリアと空間最適化を統合した容量拡大・廃止計画モデルを提案。マレーシア・サラワクのケーススタディから、完全先見は低コストで円滑な移行を実現する一方、近視眼的計画は排出量超過リスクがあることを示す。中間排出目標の導入が近視眼的計画下でも長期的目標達成に有効であることを実証。
English
This paper presents a multi-vector, multi-nodal optimization model for least-cost capacity expansion and retirement planning, applied to Sarawak, Malaysia. Results show perfect foresight enables smoother transitions at lower cost, while myopic foresight risks overshooting emissions. Incorporating intermediate emissions targets under myopic foresight effectively aligns near-term actions with long-term objectives.
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
日本の電力会社（地域グリッド）でも、長期計画と短期運用のバランスが課題。本論文の示す中間目標（2030年目標など）の導入効果は、日本のエネルギー基本計画改定にも示唆を与える。特に、各社が策定する設備投資計画において、近視眼的な意思決定のリスクを定量的に評価できる点が有用。
In the global GX context
This work contributes to global capacity expansion modeling by explicitly comparing perfect vs myopic foresight and highlighting the value of intermediate emissions targets. It offers practical insights for countries (e.g., in Southeast Asia and beyond) that rely on centralized grid planning and face uncertainties in policy and technology costs.
👥 読者別の含意
🔬研究者:Demonstrates a novel integrated optimization framework for energy transition planning, valuable for extending to other regions or incorporating stochastic elements.
🏢実務担当者:Provides quantitative evidence on how intermediate emissions targets can mitigate risks of myopic planning, useful for utility planners and energy strategy teams.
🏛政策担当者:Highlights the importance of setting intermediate targets to guide long-term decarbonization, relevant for designing national energy roadmaps.
📄 Abstract（原文）
Transforming emission-intensive energy systems into low-carbon configurations requires strategic planning that balances efficiency, costs, and emissions reductions. While many capacity expansion models support long-term investment planning, only a few integrate multiple energy vectors, spatial distribution optimization, and foresight approaches to identify realistic pathways. This work presents a multivector, multinodal optimization model for least-cost capacity expansion and retirement planning, formulated as a mixed-integer linear programming model. The model incorporates different foresight approaches to examine their influences on technology choices, investment timing, technology deployment sites, and system-wide costs and emissions. Applied to a case study in Sarawak, Malaysia, results show that perfect foresight supports smoother transitions with lower costs, whereas myopic foresight risks overshooting emissions and missing long-term targets. Importantly, incorporating intermediate emissions targets under myopic foresight helps align near-term actions with long-term objectives, which is essential when short-term uncertainties make myopic decision-making unavoidable. Although myopic planning raises the capital expenditure by 18.1%, incorporating intermediate emissions targets provides structured transition pathways and prevents capacity overexpansion.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1021/acs.iecr.5c03999first seen 2026-06-15 04:55:15
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