@article{paduani_yu_xu_lu_2022, title={A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems With Power Reserves and Fast Frequency Response Capabilities}, volume={13}, ISSN={["1949-3037"]}, url={https://doi.org/10.1109/TSTE.2021.3117688}, DOI={10.1109/TSTE.2021.3117688}, abstractNote={This paper presents a fast power-setpoint tracking algorithm to enable utility-scale photovoltaic (PV) systems to provide high quality grid services such as power reserves and fast frequency response. The algorithm unites maximum power-point estimation (MPPE) with flexible power-point tracking (FPPT) control to improve the performance of both algorithms, achieving fast and accurate PV power-setpoint tracking even under rapid solar irradiance changes. The MPPE is developed using a real-time, nonlinear curve-fitting approach based on the Levenberg-Marquardt algorithm. A modified adaptive FPPT based on the Perturb and Observe technique is developed for the power-setpoint tracking. By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations. Furthermore, to limit the maximum output power ripple, a new design is introduced for the steady-state voltage step size of the adaptive FPPT. The proposed algorithm is implemented on a testbed consisting of a 500 kVA three-phase, single-stage, utility-scale PV system on the OPAL-RT eMEGASIM platform. Results show that the proposed method outperforms the state-of-the-art.}, number={1}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Paduani, Victor Daldegan and Yu, Hui and Xu, Bei and Lu, Ning}, year={2022}, month={Jan}, pages={479–490} }