Development of Error Absorption Headroom Setting Algorithm Using Polynomial Surfaces to Create Reserve Power in PV Power Plants†

This study proposes statistical models for mitigating the risks associated with variable power sources by establishing a headroom to absorb prediction errors when PV power plants perform day‐ahead planning without the use of battery systems or intraday market procurement. Specifically, three models are proposed in this study. The first model utilized kernel density estimation to analyze the distribution of previous errors and determine error thresholds based on the desired probability set as the headroom. The second and third models utilized different sample sets that adjusted the headroom setting in response to changes in PV power generation. These models generated polynomial surfaces using PV‐predicted values and clearness index () as features, allowing the error thresholds to vary based on the required probability. Furthermore, separate polynomial surface models were developed by constructing distinct models for both predicted and measured values. The optimal model demonstrated only 120 annual negative imbalances at 30‐min intervals, with an average headroom to PV generation prediction ratio of 65%, indicating that most of the headroom absorbed prediction errors. This research has significant implications for enabling PV power plants, which require reserve power, to enhance to the stability of the overall power grid while simultaneously serving as a reliable power source themselves.