Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework
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摘要
This paper proposes an ex-ante spatial-temporal carbon response framework for day-ahead nodal carbon intensity (NCI) forecasting and proactive dispatch, leveraging a hierarchical multi-agent deep learning model (ACGD) with DSAM and an LLM-based cooperatio n system. It couples RES-forecasting (Tier-1 ANN) with a CNN-GRU-DSAM (Tier-2) for NCI prediction, and coordinates Geographically Dispatchable Loads (MESSs and DDCs) for spatial-temporal low-carbon scheduling. Case studies on a modified IEEE 33-bus network show that reducing scheduling latency by one hour enables more than 30% system-emission reduction. The work shifts NCI from a retrospective metric to a predictive operational signal, enabling proactive carbon management in cleaner power systems [page::1]
