Online Interval Forecast Based Operating Reserve Quantification Considering Net-Load Uncertainties

The increasing penetration of renewable energy sources (RES) into power grids introduces significant variability and uncertainty in net-load, posing substantial challenges to grid and market operations. Operating reserve, a critical ancillary service, helps in mitigating these challenges. Traditionally, reserve requirements have been quantified using offline learning-based frameworks. However, these frameworks often fail to incorporate recent variations in net-load, limiting their adaptability and responsiveness. This often leads to imbalances in the power system operations, especially during very short-term horizons when rapid net-load variations are most acute. This study proposes a dynamic reserve quantification (RQ) method based on an online interval forecasting approach, addressing these limitations. Additionally, existing studies propose methods for RQ, but they do not address the selection of reserve type and amount in the procurement stage based on individual resource reliability. This study further develops a reliability-aware network constrained market clearing framework to procure reserves from flexible reserve providers. A case study illustrates the proposed method's effectiveness, demonstrating improvements in forecasting accuracy and reductions in reserve procurement costs.