Research on the multi-timescale optimization scheduling of direct air capture systems driven by renewable energy

The growing deployment of renewable energy sources (RES) often leads to large-scale curtailment. Direct air capture (DAC) systems—energy-intensive yet dispatchable and modular—offer a promising solution for consuming curtailment while enabling negative emissions. However, the integration of DAC with RES remains underexplored. Specifically, DAC systems lack sufficient flexibility to accommodate intermittent energy supplies, stemming from inadequate temporal resolution of operational strategies and overly rigid operational assumptions. Moreover, their operation relies on historical data, lacking real-time control and coordinated scheduling with power plants. To bridge this gap, this study proposes a multi-timescale optimization scheduling framework that enables minute-level real-time control of modular DAC systems co-located with RES power plants. The approach uniquely integrates transferable and curtailable flexible operation modes within a two-phase scheduling system—combining day-ahead planning with intraday rolling optimization—while incorporating power forecast data from RES plants to eliminate perfect-foresight assumptions inherent in retrospective optimization, thereby establishing the first implementable real-time controlled co-dispatch architecture for synergistic RES-DAC integration. A case study based on real-world data from an 850 MW wind farm demonstrates that this approach can reduce daily system operation costs by a factor of five, increase the utilization rate of curtailed electricity to over 90%, and capture 1.5 million tons of CO 2 annually. Collectively, these outcomes establish an effective scheduling solution for RES-DAC integration that simultaneously enhances environmental sustainability and economic returns.