AI-Enabled Digital Twins and Power System Asset Management for Long-Term Grid Investment Planning
AI駆動デジタルツインと電力系統資産管理による長期系統投資計画 (AI 翻訳)
Mitch D
🤖 gxceed AI 要約
日本語
本論文は、AI駆動デジタルツインと電力系統資産管理を統合したOptimTwinフレームワークを提案し、長期的な系統投資計画を支援する。IRENA FlexToolと深層ニューラルネットワーク代理モデルを用い、IEEE/NREL 118バス系統で評価した結果、ライフサイクルコストを考慮した計画が高比率の変動再エネ導入と投資指標の改善を両立できることを示した。保守から計画へのパラダイム転換を提示する。
English
This paper proposes OptimTwin, an integrated framework combining AI-driven digital twins and power system asset management to support long-term grid investment planning. Using IRENA FlexTool and a deep neural network surrogate on the IEEE/NREL 118-bus system, it shows that lifecycle-cost-aware planning can support high VRE penetration while improving investment indicators, marking a shift from reactive maintenance to synchronized cyber-physical planning.
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
日本の電力系統は再エネ導入拡大と老朽化インフラの両立が課題であり、本フレームワークは系統投資の効率化と信頼性維持に寄与する。送配電事業者や系統運用者の長期計画策定に示唆を与え、GX投資の優先順位付けに活用可能。
In the global GX context
Globally, this work addresses the challenge of integrating high shares of variable renewables while managing aging grid assets. It contributes to the literature on digital twins and AI-driven asset management, offering a methodology for lifecycle-cost-aware investment planning that aligns with the energy transition and climate finance goals.
👥 読者別の含意
🔬研究者:Provides a novel integration of AI digital twins and asset management for grid planning, with a benchmark on the 118-bus system.
🏢実務担当者:Offers a framework for utility-scale investment planning that balances lifecycle costs and renewable integration, useful for grid operators and asset managers.
🏛政策担当者:Highlights the value of AI-enabled planning tools in supporting renewable targets and infrastructure investment decisions.
📄 Abstract（原文）
<title>Abstract</title> <p>This paper presents a research-oriented synthesis of the study on OptimTwin, an integrated framework that combines artificial-intelligence-driven digital twins and power system asset management to support long-term grid investment planning. The reviewed work addresses a critical challenge facing modern power systems: the need to manage aging infrastructure, increasing renewable penetration, operational uncertainty, and investment constraints through methods that are predictive, data-driven, and economically defensible. Drawing on related literature in smart-grid digital twins, predictive maintenance, IoT-enabled monitoring, neural-network-based decision support, and power asset management, this synthesis positions OptimTwin within the broader transition from reactive maintenance toward continuously synchronized cyber-physical planning. The study uses IRENA FlexTool, a deep feed-forward neural network surrogate, and an extended IEEE/NREL 118-bus benchmark system to compare base, investment, and OptimTwin planning scenarios. Reported findings indicate that lifecycle-cost-aware planning can support high variable renewable energy penetration while improving investment indicators. This document restates the source study in original language, organizes key quantitative information into tables and figures, and evaluates the framework from the perspective of research engineering, with emphasis on reliability, lifecycle economics, methodological transparency, and utility-scale deployment readiness.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10684525/v1first seen 2026-08-15 04:31:07 · last seen 2026-08-17 04:40:41
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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。