A predictive power ramp rate control scheme with an updating Gaussian prediction confidence estimator for PV systems

The fast growth of solar photovoltaic (PV) power generation raises the concern of grid stability due to intermittency. The traditional solution based on the broad installation of energy storage systems (ESS) is considered to be costly. Hence, the latest research focuses on the active power ramp rate control (PRRC) that decouples the heavy reliance on ESS. While power-forecasting-based PRRC can be a promising solution, the relevant research is still limited and does not cover the prediction error’s negative effect on the power ramp rate control. With the above, this paper investigates the over-regulation issue that is induced by inaccurate prediction and can lead to power losses. More importantly, this paper proposes a novel PRRC scheme incorporating a Gaussian confidence estimator and best-performed deep neural networks (DNN) predictor to implement more accurate control actions while overcoming the over-regulation problem. The experimental tests show that the proposed scheme can simultaneously achieve a more promising PRRC performance and relatively higher energy harvest efficiency compared to the prior arts. •A novel predictive PRRC scheme based on big meteorological data is proposed.•The proposed approach eliminates the reliance on ESS while simultaneously achieving promising PRRC performance and high energy harvest efficiency.•The PRRC’s overregulation problem that can bring additional power losses is firstly investigated in this study.•A novel prediction confidence estimator based on Gaussian process is proposed to address the overregulation issue by monitoring the prediction’s reliability.•A comprehensive study on the popular DNN-based solar forecasting models is implemented to select the best-performed predictor suitable for sequential meteorological data and the power system’s PRRC task.