Estimación de la Región de Operación Factible con Consideraciones de Incertidumbre en Redes de Distribución
不確実性を考慮した配電ネットワークにおける実行可能運用領域の推定 (AI 翻訳)
Kevin Aarón Raudales De vicente, Gastón Orlando Suvire, Andrés Arturo Romero Quete
🤖 gxceed AI 要約
日本語
本レビュー論文は、分散型エネルギーリソース（DER）の統合を考慮した、不確実性下での配電ネットワークにおける実行可能運用領域（FOR）の推定手法を検討する。確率的、可能性論的、確率的、ロバスト、ハイブリッド、IGDT、ファジーロバストアプローチを網羅し、特にラテンアメリカのようなデータ不足環境でのシステムレジリエンス向上のための限界と機会を強調する。
English
This review paper examines methods for estimating the Feasible Operation Region (FOR) in distribution networks under uncertainty, considering the integration of Distributed Energy Resources (DER). It covers probabilistic, possibilistic, stochastic, robust, hybrid, IGDT, and fuzzy-robust approaches, highlighting limitations and opportunities for improving system resilience, especially in data-scarce environments like Latin America.
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
日本でも分散型電源の増加に伴い、配電ネットワークの柔軟性評価が重要となっている。本論文の不確実性モデリング手法は、日本の電力システムにも応用可能な知見を提供する。
In the global GX context
This review contributes to the global discourse on distribution network flexibility by synthesizing uncertainty quantification methods for FOR estimation. It is particularly relevant for regions scaling up DER integration, such as Europe and the US, and highlights challenges in data-scarce environments.
👥 読者別の含意
🔬研究者:Provides a comprehensive review of uncertainty methods for FOR estimation in distribution networks.
🏢実務担当者:Offers insights into quantifying flexibility and uncertainty in distribution systems for DER integration.
📄 Abstract（原文）
The energy transition is redefining power distribution through the increasing integration of Distributed Energy Resources (DER), which introduces new challenges for system operation and planning. The Feasible Operation Region (FOR) has emerged as a key tool to quantify and communicate flexibility in distribution networks. However, estimating the FOR under uncertainty remains complex due to the variability of renewable generation, demand fluctuations, and limited data. This article presents a comprehensive review of uncertainty quantification and modeling methods used in the estimation of the FOR, including probabilistic, possibilistic, stochastic, and robust approaches, as well as hybrid, IGDT and fuzzy-robust models. It also analyzes the impact of these methodologies on flexibility estimation, identifies limitations in current practices, and highlights opportunities for improving system resilience. Particular attention is given to emerging techniques and their relevance for data-scarce environments like Latin America. The findings under-score the importance of developing integrated uncertainty models that address both physical and cyber risks to enhance decision-making in active distribution networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://revistas.unal.edu.co/index.php/SICEL/article/view/121207first seen 2026-05-15 16:56:33
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