Collaborative Real-Time Operation for Long-Term and Short-Term Energy Storage: A Scheduling-Corrective Approach

Under the background of carbon peaking and carbon neutrality, the renewable-dominated power grid attracts wide attention. To address the fluctuations of renewable power in different timescales, the collaborative operation of long-term and short-term energy storage devices is required. This paper studies the year-round operation of a renewable-dominated power grid. A data-driven scheduling-correction framework is proposed, which consists of offline scheduling and real-time operation. The scheduling module generates optimal storage level sequences of long-term energy storage in previous years ahead of the operating year using historical data of renewable power. The real-time module performs two stages in each period: The reference storage level is first determined for long-term storage based on historical optimal curves and newly observed renewable generation data; then a bi-objective rolling-horizon optimization problem is solved, in which the current operating cost is minimized and the long-term storage is steered to the reference value. An appealing feature is that the proposed framework utilizes only short-term prediction of renewable power. Numerical tests show that the proposed method outperforms existing rolling horizon approach in terms of operating cost, load shedding and renewable spillage rate. The collaboration of the long-term and short-term storage is also well-achieved.