GO-ing DEEEP: AN EXPLORATION, ANALYSIS, AND INTEGRATION OF CLIMATE DATASETS INTO A CAPACITY EXPANSION MODELING FRAMEWORK
GO-ing DEEEP: 気候データセットの探索、分析、および容量拡大モデリングフレームワークへの統合 (AI 翻訳)
Asia Zhang, Inês L. Azevedo, Stanford University
🤖 gxceed AI 要約
日本語
本論文は、気候データセットを調査し、GODEEEPアンサンブルを分析して将来シナリオ下での風力・太陽光・水力の変動を予測し、それらを容量拡大モデルPyPSA-USAに統合して米国西部電力系統の最小コスト発電投資を研究した。結果は、近い将来ではポートフォリオの変化は小さいが、長期計画では顕著になり、気候不確実性を無視すると決定コストが時間とともに増大することを示している。
English
This thesis explores climate datasets for renewable resource variability, analyzes the GODEEEP ensemble to project changes in wind, solar, and hydro conditions under future scenarios, and integrates these into the PyPSA-USA capacity expansion model to study least-cost generation investment in the Western Interconnect under climate uncertainty. Results show that portfolio changes are modest near-term but become pronounced later, indicating that ignoring climate uncertainty increases decision costs over longer timescales.
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
本研究の手法は、日本の電力系統計画にも応用可能であり、気候変動を考慮した容量拡大モデルの重要性を示している。日本でも再エネ大量導入に伴い、気候データの統合が求められる。
In the global GX context
This paper provides a practical pathway for incorporating climate uncertainty into long-term power system planning, demonstrating that ignoring climate variability can lead to suboptimal investment over longer horizons. It is relevant for global grid planners and policymakers advancing renewable energy integration.
👥 読者別の含意
🔬研究者:Provides a methodology for integrating climate datasets into capacity expansion models, useful for energy system modelers.
🏢実務担当者:Grid planning teams can adopt this approach to account for climate uncertainty in generation investment decisions.
🏛政策担当者:Highlights the need for climate-aware long-term planning to ensure cost-effective decarbonization.
📄 Abstract（原文）
Decarbonizing the electricity grid while meeting rising electricity demand in the United States is an effort that requires a fundamental overhaul of our generation mix, specifically a massive scaling of renewable energy resources. Long term grid planning using capacity expansion models typically rely on a single historical weather year and treat climate uncertainty as an external factor to the opti- mization. To understand how the impacts of climate change may reshape grid investment decisions in the Western Interconnect (WECC), this thesis proceeds in three parts. First, it reviews publicly available meteorology and climate datasets, assessing their adequacy for capturing wind, solar, and hydro variability and their suitability for integration into capacity expansion models. Second, it analyzes one of these climate datasets, the GODEEEP ensemble produced by PNNL, using sta- tistical methods to characterize how projected wind speeds, solar irradiance, specific humidity, and temperature (meteorological variables ingested to generate wind and solar capacity factors) shift across future climate scenarios. Lastly, it integrates these climate-informed capacity factors into the open-source capacity expansion model PyPSA-USA and conducts a multi-scenario, multi-horizon case study of least-cost generation investment across the WECC under climate-perturbed resource availability. The results indicate that changes to the least-cost portfolio across the climate-scenario sweep remain modest in the near term and become pronounced in the later planning horizon, sug- gesting that the cost of omitting climate uncertainty grows with the timescale of the decision. All together, these contributions outline a practical pathway for incorporating climate uncertainty into long-term power system planning with intention and good will :)
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.25740/jf884tt4245first seen 2026-06-26 04:51:33
🔔 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。