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米国電力市場における天候依存型再生可能エネルギー普及と停電リスクの因果分析

Causal Analysis of Weather-Dependent Renewable Energy Penetration and Blackout Risk in the U.S. Electricity Market (原題)

Dinh Nghi Dung Le

Macquarie Universityジャーナル2026-08-05#エネルギー転換Origin: US対象セクター: power
DOI: 10.25949/33158450
原典: https://doi.org/10.25949/33158450

🤖 gxceed AI 要約

日本語

本研究は、米国の州別データ（2001-2023年）を用いて、天候依存型再生可能エネルギー（WD-RES）の普及が停電リスクに与える因果効果を、一般化ランダムフォレスト（GRF）による因果機械学習で推定した。地域の気象条件や経済的脆弱性、蓄電容量などの異質性を考慮し、優先的投資ゾーンを特定。再生可能エネルギー移行における送電網の強靭化とエネルギー正義に政策的示唆を与える。

English

This study uses U.S. state-level data (2001-2023) and causal machine learning (generalized random forest) to estimate the causal effect of weather-dependent renewable energy (WD-RES) penetration on blackout risk. It identifies heterogeneous effects across regions with varying weather, economic vulnerability, and storage capacity, highlighting priority zones for adaptive investments to enhance grid resilience and energy justice.

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

日本では再生可能エネルギー導入拡大に伴い、系統安定性と停電リスクの評価が重要課題。本研究の因果推論アプローチは、日本の地域特性に応じた蓄電池配置や送電網投資の優先順位付けに示唆を与える。

In the global GX context

Globally, this research contributes to the growing literature on climate risk and energy transition by providing a causal framework to assess the resilience implications of renewable penetration. It offers actionable insights for policymakers balancing decarbonization with grid reliability, relevant to regions like the EU and US.

👥 読者別の含意

🔬研究者:Provides a novel causal ML approach to quantify the heterogeneous effects of renewables on blackout risk, advancing methods in climate risk and energy systems analysis.

🏢実務担当者:Offers a data-driven tool to identify priority zones for storage and backup investments, aiding grid operators and utilities in resilience planning.

🏛政策担当者:Highlights the need for adaptive investments and energy justice considerations in renewable transition policies to mitigate blackout risks.

📄 Abstract（原文）

The increasing penetration of weather-dependent renewable energy sources (WD-RES) introduces a dual challenge for power system resilience. While critical for decarbonization, their intermittency and climate-driven variability exacerbate grid vulnerabilities during extreme weather. Existing models fail to isolate the causal role of WD-RES in blackout risks or account for economic factors in resilience outcomes. This study closes these gaps by integrating causal machine learning and cross-sector risk analysis into a generalized random forest (GRF) framework. Using U.S. state-level data (2001–2023), the research quantifies the heterogeneous effects of WD-RES penetration on blackout severity across regions with varying weather conditions, economic vulnerability, storage capacity, and infrastructure characteristics. GRF-driven clustering identifies priority zones where adaptive investments, for instance, storage redistribution and backup capacity expansion, can most effectively mitigate reliability of power system. By mapping interactions between WD-RES, climate extremes and economic factors, this research provides policymakers with actionable tools to enhance grid resilience while addressing energy justice in the transition to renewable energy.

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