Digital Shadowing-Enabled Deep Learning for Carbon-Aware Day-Ahead Scheduling of Integrated Energy Systems Under Forecast Uncertainty

Abstract

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may lead to infeasible schedules during deployment and weaken their reliability in digital-shadow-assisted operation. In addition, conventional cost-oriented scheduling objectives do not explicitly account for the carbon impact of real-time imbalances caused by forecast errors. To address these challenges, this paper proposes a digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems. The main methodological contribution is a feasibility-by-design neural decoder that embeds hard physical constraints directly into the network forward pass. By classifying devices into non-memory fast units, non-memory ramp-limited units, and memory-type storage devices, the decoder applies tailored transformations to enforce capacity limits, ramp-rate restrictions, state-of-charge dynamics, and terminal energy consistency by construction. Therefore, the generated schedules are physically feasible without relying on post-hoc repair. In parallel, a carbon-first objective is developed to minimize both scheduled emissions and imbalance-driven emissions, allowing the scheduler to reduce not only planned carbon output but also the carbon impact of real-time corrective actions. Forecast uncertainty is represented through a digital shadow that stores historical forecast-error patterns and generates augmented training scenarios. Case studies based on U.K. data show that the proposed framework produces fully feasible schedules and reduces annual CO2 emissions by approximately 4.0% compared with a forecast-driven baseline, with larger benefits during high-demand periods. These results demonstrate that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty.