Adaptive Rolling-Horizon Optimization for Low-Carbon Operation of Coupled Transportation–Power Systems

The rapid growth of electric vehicles (EVs) has created new challenges for the coordinated low-carbon operation of transportation and power systems. To address this issue, this paper proposes an adaptive rolling-horizon dynamic user equilibrium (DUE) optimization framework for the low-carbon operation of coupled transportation–power systems. The framework integrates transportation, power, and environmental dimensions into a unified objective. On the transportation side, a DUE-based traffic assignment formulation captures both road travel times and station-level queuing dynamics, providing a realistic representation of EV user behavior. This DUE-based traffic assignment model is coupled with an optimal AC power flow formulation to ensure grid feasibility and quantify network losses. To internalize environmental costs, a carbon emission flow module propagates generator-specific carbon intensities to charging stations, aligning charging decisions with their true emission sources. These components are coordinated within a rolling-horizon method in which the prediction window adapts its length to the variability of demand and renewable forecasts. The proposed method allows longer horizons to improve foresight in stable conditions and shorter ones to maintain robustness under volatility. Numerical case studies demonstrate the effectiveness and robustness of the proposed framework and its potential to support low-carbon, high-efficiency operation of coupled transportation–power systems.