Learning-Assisted Day-Ahead Energy Scheduling for Frequency-Secure Inverter-Dominated Grids with Grid-Forming Battery Energy Storage Systems
For power system operators, this work addresses the computational bottleneck of embedding accurate frequency metrics from EMT simulations into operational optimization, enabling more efficient and secure scheduling in inverter-dominated grids.
The paper proposes a learning-assisted day-ahead energy scheduling framework that uses a surrogate model to represent frequency support dynamics of grid-forming battery energy storage systems, enabling frequency-secure scheduling with improved accuracy and computational efficiency compared to analytical methods.
As grid-forming (GFM) battery energy storage systems (BESS) are increasingly deployed to enhance power system inertial response and frequency stability, incorporating their frequency support capabilities into day-ahead energy scheduling (DAES) is essential for achieving both frequency security and operational efficiency. However, accurately determining frequency metrics in grids with coexisting GFM inverters and synchronous generators requires electromagnetic transient (EMT) simulations, which are computationally prohibitive for direct embedding in grid operational optimization models. To bridge the gap between modeling accuracy and computational efficiency, a learning-assisted DAES (LA-DAES) framework is proposed in this work. By leveraging a surrogate model to represent the frequency support dynamics of GFM BESS, the proposed framework ensures frequency security with a reasonable solve time. Comparative results demonstrate that, relative to analytical frequency-constrained DAES, the proposed LA-DAES framework more accurately captures grid frequency metrics and improves the utilization of GFM BESS.