Digital Twin–Driven Low‐Carbon and Economic Scheduling for Integrated Energy System With Learnt Energy–Carbon Coupling Constraints

The intractable nonlinear relationship between energy flow and carbon emission flow (CEF) challenges the low‐carbon scheduling of integrated energy systems (IES). To address this issue, this paper proposes a digital twin‐driven low‐carbon economic scheduling approach for electricity–heating–gas IES based on energy–carbon coupling constraints learning. Under optimisation with the constraint learning (OCL) framework, a neural network (NN) is trained in the digital twin environment to approximate the mapping from source‐side power injections to load‐side carbon emissions, employing a sparse training strategy to reduce NN model parameters. To improve computational efficiency, an improved ReLU linearisation method based on ideal mixed‐integer programming (MIP) formulation is developed, where strengthening inequalities are separated as needed to obtain tighter relaxations. The learnt CEF constraints are incorporated into the scheduling model, together with a stepped carbon pricing mechanism for low‐carbon demand response. Case studies show that the proposed approach reduces system‐wide carbon emissions, accurately captures energy–carbon coupling and improves the tractability of scheduling optimisation with embedded NN constraints.