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Carbon-Aware Self-Healing Smart Grid Using Digital Twin and Swarm Artificial Intelligence for Sustainable Power System Resilience

持続可能な電力システムのレジリエンスのためのデジタルツインと群知能を用いたカーボン・アウェア自己修復スマートグリッド (AI 翻訳)

Sarker K, Sarker J

Research Squareプレプリント2026-05-20#エネルギー転換Origin: Global
DOI: 10.21203/rs.3.rs-9656540/v1
原典: https://doi.org/10.21203/rs.3.rs-9656540/v1

🤖 gxceed AI 要約

日本語

本論文は、デジタルツインと群知能を活用したカーボン・アウェア自己修復スマートグリッド（CASH-SG）フレームワークを提案する。故障予測、自律的再構成、排出削減を統合し、IEEE標準バスシステムでのシミュレーションにより、従来手法と比べて炭素排出削減、復旧時間短縮、コスト低減、安定性向上を実証した。

English

This paper proposes a Carbon-Aware Self-Healing Smart Grid (CASH-SG) framework using Digital Twin and Swarm AI for intelligent fault prediction, autonomous reconfiguration, and emission-aware energy optimization. Simulations on IEEE standard bus systems demonstrate superior carbon reduction, faster restoration, lower cost, and enhanced stability compared to conventional approaches.

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

本フレームワークは、日本のGX政策が進める再生可能エネルギー統合とグリッドのデジタル化に貢献し得る技術的基盤を提供する。特に、電力系統のカーボン・アウェアな自律復旧は、災害多発国である日本にとって重要である。

In the global GX context

This framework contributes to global efforts in decarbonizing power grids by integrating carbon awareness into grid restoration. It aligns with the push for digitalization and AI in energy systems, offering a path to more resilient and sustainable electricity infrastructure.

👥 読者別の含意

🔬研究者:Provides a novel integration of digital twin and swarm AI for carbon-aware grid restoration, useful for researchers in smart grids and energy optimization.

🏢実務担当者:Offers a technical blueprint for utilities to incorporate carbon reduction into grid fault management and recovery.

📄 Abstract（原文）

<title>Abstract</title> <p>The increasing penetration of renewable energy resources, rapid electrification and rising carbon emissions have transformed modern power systems into highly dynamic and vulnerable infrastructures. Conventional smart grid architectures primarily focus on reliability and economic operation, while limited attention has been devoted to integrated carbon-aware autonomous grid restoration. This paper proposes a novel Carbon-Aware Self-Healing Smart Grid (CASH-SG) framework using Digital Twin technology and Swarm Artificial Intelligence for intelligent fault prediction, autonomous reconfiguration and emission-aware energy optimization. The proposed framework creates a real-time virtual replica of the physical power network through a Digital Twin model that continuously monitors grid parameters, renewable intermittency, load variations and transmission contingencies. A hybrid Swarm AI optimization mechanism integrating Particle Swarm Optimization and Gravitational Search Algorithm is developed to minimize carbon emission, transmission loss and restoration time simultaneously during fault conditions. The framework dynamically identifies critical emission zones and performs adaptive feeder reconfiguration for resilient grid recovery. MATLAB/Simulink-based simulations are performed on IEEE standard bus systems under multiple fault and renewable uncertainty scenarios. Comparative analysis demonstrates that the proposed CASH-SG framework achieves superior carbon reduction, faster fault restoration, lower operational cost and enhanced system stability compared to conventional smart grid approaches. The proposed methodology introduces a next-generation intelligent energy management architecture suitable for future sustainable and autonomous power systems.</p>

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