Distributed Robust Optimization Scheduling for Integrated Energy Systems Based on Data-Driven and Green Certificate-Carbon Trading Mechanisms
データ駆動型およびグリーン証書・炭素取引メカニズムに基づく統合エネルギーシステムの分散ロバスト最適運用計画 (AI 翻訳)
Yinghui Chen, Weiqing Wang, Xiaozhu Li, Sizhe Yan, Ming Zhou
🤖 gxceed AI 要約
日本語
本論文は、統合エネルギーシステムにおける再生可能エネルギー高比率化に伴う不確実性と低炭素経済運用の課題に対し、データ駆動型シナリオ生成と多目的分布ロバスト最適化を組み合わせた新たな運用計画フレームワークを提案。LSTM-AEとK-Meansクラスタリングにより代表シナリオを生成し、廃熱回収を考慮したP2Gモデルとグリーン証書・段階的炭素価格の連携市場メカニズムを導入。カリフォルニアのデータに基づくケーススタディでは、従来のロバスト最適化と比べ総運用コスト9.0%削減、炭素排出量139.9トン削減を達成し、極端な変動下でも安全性を維持することを確認した。
English
This paper proposes a novel scheduling framework for integrated energy systems that combines data-driven scenario generation with multi-objective distributionally robust optimization. It uses LSTM-AE and K-Means clustering to generate typical scenarios, introduces a refined P2G model with waste heat recovery, and couples green certificate trading with tiered carbon pricing. Case studies on California data show 9.0% cost reduction and 139.9 tons carbon emission reduction compared to traditional robust optimization, while maintaining operational safety under extreme fluctuations.
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GX政策の下で再生可能エネルギーの統合と炭素価格付けの導入が進んでおり、本論文のグリーン証書と段階的炭素価格の連携メカニズムは日本のカーボンプライシング設計や系統運用に示唆を与える。また、データ駆動型ロバスト最適化手法は、日本特有の気象変動や需要パターンへの応用が期待される。
In the global GX context
This work contributes to global GX by addressing the operational challenges of high-renewable integrated energy systems through a novel combination of data-driven robust optimization and market mechanisms. The joint green certificate-carbon pricing model is particularly relevant for regions designing carbon markets and renewable portfolio standards, such as the EU, US, and emerging Asian markets.
👥 読者別の含意
🔬研究者:Provides a methodological advance in distributionally robust optimization for energy systems with uncertainty, integrating deep learning and multi-objective CVaR.
🏢実務担当者:Offers a practical scheduling framework that can be adapted for microgrid or district energy operators seeking cost and emission reductions.
🏛政策担当者:Demonstrates the effectiveness of coupling green certificate and carbon pricing mechanisms, providing evidence for market design.
📄 Abstract（原文）
High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/pr14010174first seen 2026-05-05 22:50:51
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。