AI 中文总结
该研究针对电力系统低碳调度延迟问题，提出多智能体注意力增强深度学习框架，可提前预测节点碳强度，在改进IEEE 33节点系统测试下减排超30%，推动主动碳管理。
AI 中文摘要
电力系统脱碳作为碳排放的主要来源，已获得社会广泛关注。节点碳强度（NCI）是面向碳的需求响应的关键因素，传统上通过事后计算确定，但这种事后方法会导致低碳调度出现延迟。为解决该问题，本文提出一种主动事前时空碳响应框架，其核心是开发一种新颖的基于深度学习的分层设计，该设计由双阶段注意力机制和基于大语言模型（LLM）的多智能体协作系统增强，以准确预测日前节点碳强度（NCI），该设计有效缓解了可再生能源不确定性的影响并提升了预测鲁棒性。在需求侧，该框架提出一种时空碳调度模型，整合了地理可调度负荷（GDLs），包括移动储能系统（MESSs）和分布式数据中心（DDCs）。借助高精度的日前节点碳强度（NCI）预测，该框架可通过快速响应碳强度波动有效降低系统排放。所提框架在改进的IEEE 33节点系统上进行测试，根据仿真结果分析了该框架对调度延迟和排放结果的影响，结果表明，在碳调度延迟减少1小时的情况下，所提模型和方法可实现超过30%的减排。本研究突破了被动碳核算的局限，向主动碳管理迈进，提供了一种智能解决方案，可加速向更清洁的电力系统转型，同时直接支持可持续生产目标。
英文摘要
As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-based multi-agent cooperation system, to accurately forecast day-ahead NCI. This design effectively mitigates the impact of renewable energy uncertainty and enhances predictive resilience. On the demand side, the framework proposes a spatial-temporal carbon scheduling model that integrates geographically dispatchable loads (GDLs), including mobile energy storage systems (MESSs) and distributed data centers (DDCs). Leveraging high-accuracy day-ahead NCI predictions, the framework can effectively reduce system emissions by quickly responding to carbon intensity fluctuations. The proposed framework is tested on the modified IEEE 33-bus system. According to the simulation results, the impacts of proposed framework on dispatching latency and emission outcomes are analyzed. The results demonstrate that under a one-hour reduction in carbon scheduling latency, the proposed model and methodology can achieve over 30% emission reduction. This research breaks through the limitations of passive carbon accounting, advancing toward proactive carbon management. It offers an intelligent solution that accelerates the transition to cleaner power systems while directly supporting sustainable production goals.
Comments31 pages, 11 figures
