A framework for the digital energy management and decarbonization of sustainable and renewable energy communities
持続可能で再生可能なエネルギーコミュニティのためのデジタルエネルギー管理と脱炭素化のフレームワーク (AI 翻訳)
Matilde Chierici, Martina Ferrando, Francesco Causone
🤖 gxceed AI 要約
日本語
本論文は、持続可能なエネルギーコミュニティのためのデジタルモデルから完全なデジタルツインへと進化する逐次フレームワークを提案する。再生可能エネルギーコミュニティ（REC）を事例とし、データ統合、予測モデリング、最適化、需要・供給柔軟性管理を支援する。都市・農村の両環境で再生可能エネルギー統合の拡大、ピーク負荷削減、脱炭素化を実現し、スマートシティのデジタルツインの統一方法論を提供する。
English
This paper proposes a sequential framework that evolves from a digital model to a full Digital Twin for sustainable energy communities, using Renewable Energy Communities (RECs) as a case study. The framework supports data integration, predictive modeling, optimization, and demand-supply flexibility management. It enables increased renewable integration, peak load reduction, and decarbonization in both urban and rural contexts, providing a unified methodology for smart city digital twins.
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RECの概念は発展途上だが、地域エネルギー管理のデジタル化は分散型エネルギー統合とカーボンニュートラル達成に貢献する。本フレームワークはスマートシティや地域脱炭素化施策に応用可能で、SSBJやTCFD関連のエネルギー管理データ標準化にも示唆を与える。
In the global GX context
Globally, the digital twin approach for energy communities aligns with the growing emphasis on decentralized energy systems and the need for tools to manage renewable integration and grid flexibility. This framework supports compliance with disclosure frameworks (e.g., ISSB, CSRD) by enabling data-driven energy management and decarbonization tracking at the community level.
👥 読者別の含意
🔬研究者:This work provides a structured methodology for developing digital twins tailored to renewable energy communities, extensible to other sustainable community types.
🏢実務担当者:Energy community managers can use this framework to guide digital tool implementation for optimizing renewable energy use, reducing peak loads, and enhancing grid performance.
🏛政策担当者:Policymakers can reference this framework to understand the digital infrastructure needed to support regulatory incentives for renewable energy communities and community-level decarbonization.
📄 Abstract（原文）
The increasing complexity of energy systems at the community scale, both in urban and rural contexts, requires advanced digital tools capable of supporting data integration, simulation, and optimization. This contribution proposes a sequential framework that evolves from a digital model, dedicated to the collection, interoperability, and normalization of energy data, to a full Digital Twin (DT), which integrates predictive models, optimization algorithms, and advanced control for the coordinated management of sustainable urban and rural communities. The proposed framework is conceived as a methodological structure applicable to different types of sustainable urban and rural communities. Within this broad scope, Renewable Energy Communities (RECs) are adopted as a paradigmatic application case. RECs represent a particularly suitable example due to their clear regulatory definition, structured incentive mechanisms, and explicit focus on collective renewable energy management, which make them an ideal testbed for advanced digital solutions. The framework addresses multiple planning and operational functions, including energy optimization, scenario-based simulation, predictive maintenance, and the management of demand- and supply-side flexibility. It enables increased integration of renewable sources, reduction of peak loads, improvement of local grid performance, and support for decarbonization strategies in both urban and rural environments. The proposed approach provides a unified methodological structure to guide the transition from simple digital tools to full-fledged urban DTs capable of supporting operational and planning decisions in smart cities.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1051/e3sconf/202671004006/pdffirst seen 2026-05-31 05:09:48 · last seen 2026-06-03 04:44:36
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