@inproceedings{cf7e69e4a3e54b12b0bb575e3e976c31,
title = "Grid Frequency Control Capability of Energy Storage Systems: Modeling, New Control Approach, and Real-time Validation",
abstract = "Energy storage systems (ESSs) have proved to be efficient in frequency regulation by providing flexible charging/discharging powers. This paper presents a model predictive control (MPC) with feedback correction (FC) to provide the ESS with control signals to be efficiently involved in the frequency regulation in a power system with renewable power generation. The FD is introduced to improve the accuracy of the prediction in the MPC. An approach based on the artificial neural network (ANN) is presented for optimal design of the weighting coefficients appearing in the MPC objective function. The controller performance is compared with an MPC without feedback correction, a fuzzy-PD control, and a scheme with no support from the ESS. A comparison is also made to examine the effect of weighting coefficients tuned by the ANN with those tuned by a fuzzy intelligent method and a sine-cosine algorithm. Real-time validations are provided to demonstrate the proposed method{\textquoteright}s effectiveness.",
keywords = "Artificial neural network, Energy storage system, load frequency control, model predictive control",
author = "Arman Oshnoei and Soroush Oshnoei and Kamran Jalilpoor and Sadegh Soudjani and Frede Blaabjerg",
year = "2023",
doi = "10.1109/eGrid58358.2023.10380956",
language = "English",
isbn = "979-8-3503-2701-4",
series = "IEEE Workshop on the Electronic Grid (eGRID)",
publisher = "IEEE Press",
pages = "1--6",
booktitle = "Proceedings of the 2023 8th IEEE Workshop on the Electronic Grid (eGRID)",
note = "2023 8th IEEE Workshop on the Electronic Grid (eGRID) ; Conference date: 16-10-2023 Through 18-10-2023",
}