Rural Integrated Energy System Carbon Assessment and Carbon Reduction Potential Analysis Based on Deep Reinforcement Learning
深層強化学習に基づく農村統合エネルギーシステムの炭素評価と炭素削減ポテンシャル分析 (AI 翻訳)
Haoye Jiang
🤖 gxceed AI 要約
日本語
本論文は、深層強化学習（DRL）を統合した農村統合エネルギーシステム（RIES）の炭素評価フレームワークを提案する。動的モデルと炭素フロー追跡機構を構築し、Deep Deterministic Policy Gradientアルゴリズムと安全模倣機構に基づくSI-SACアルゴリズムを導入してシステムスケジューリングを最適化する。ケース分析では、DRL駆動のスケジューリング戦略が従来の混合整数線形計画法と比較して炭素排出量を約10.4%、運用コストを約8.3%削減することを実証している。
English
This paper proposes a carbon assessment framework for Rural Integrated Energy Systems (RIES) integrating deep reinforcement learning. It constructs dynamic models and carbon flow tracking, and introduces DRL algorithms (DDPG and SI-SAC) to optimize system scheduling. Case analysis shows that the DRL-driven strategy reduces carbon emissions by 10.4% and operating costs by 8.3% compared to traditional MILP, providing a quantitative decision tool for low-carbon rural energy systems.
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
中国の「デュアルカーボン」目標を背景に、農村部のエネルギー転換を支援する評価ツール。日本でも農村部の再生可能エネルギー導入と炭素削減策に応用可能だが、現地のエネルギー構成や政策に合わせた調整が必要。
In the global GX context
This paper addresses China's 'Dual Carbon' goals by providing a DRL-based carbon assessment framework for rural energy systems. While China-specific, the methodology is globally relevant for regions pursuing rural energy decarbonization, though adaptation to local energy mixes and policies is needed.
👥 読者別の含意
🔬研究者:Demonstrates a novel application of DRL to carbon assessment and scheduling optimization in integrated energy systems, with potential for extension to other sectors.
🏢実務担当者:Offers a quantitative tool for optimizing rural energy system operations to reduce carbon emissions and costs, though direct applicability depends on local energy infrastructure.
🏛政策担当者:Provides evidence that DRL-driven scheduling can effectively lower emissions and costs, supporting policy design for rural energy transition.
📄 Abstract（原文）
With the advancement of China's "Dual Carbon" goals, the transformation of the rural energy structure urgently requires the support of scientific assessment tools. This paper proposes a carbon assessment framework for Rural Integrated Energy Systems (RIES) integrating deep reinforcement learning. First, a dynamic model of RIES equipment and a carbon flow tracking mechanism are constructed to achieve accurate measurement and real-time tracking of carbon emissions from various energy equipment within the system. Second, the Deep Deterministic Policy Gradient algorithm is employed to optimize the system scheduling strategy, and a novel SI-SAC algorithm based on a safety-imitation mechanism is proposed, effectively enhancing the algorithm's constraint satisfaction capability and learning efficiency. Finally, the carbon emission intensity under different energy configurations is quantified, and the potential of DRL in carbon reduction path optimization is explored. Case analysis shows that the DRL-driven scheduling strategy reduces system carbon emissions by approximately 10.4% and operating costs by approximately 8.3% compared to the traditional mixed integer linear programming method, confirming its effectiveness and economy in rural carbon reduction decision-making. The framework proposed in this paper provides a quantitative decision-making tool for the low-carbon, economical, and secure operation of rural energy systems, and holds significant theoretical and practical value for advancing the scientific implementation of the "Dual Carbon" goals in rural areas.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54254/2755-2721/2026.ch35455first seen 2026-07-23 05:24:24
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