@article{Huo2024, 
author = {Yanda Huo and Peng Li and Haoran Ji and Hao Yu and Jinli Zhao and Wei Xi and Jianzhong Wu and Chengshan Wang},
title = {Data-driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks},
year = {2024},
journal = {CSEE Journal of Power and Energy Systems},
volume = {10},
number = {5},
pages = {1876-1886},
keywords = {Distribution network, distributed energy storage (DES), distributed generators (DGs), data-driven, predictive voltage control},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2022.02880},
doi = {10.17775/CSEEJPES.2022.02880},
abstract = {Integration of distributed energy storage (DES) is beneficial for mitigating voltage fluctuations in highly distributed generator (DG)-penetrated active distribution networks (ADNs). Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. However, absence of network parameters and complex operational states of ADN poses challenges to model-based methods. This paper proposes a data-driven predictive voltage control method for DES. First, considering time-series constraints, a data-driven predictive control model is formulated for DES by using measurement data. Then, a data-driven coordination method is proposed for DES and DGs in each area. Through boundary information interaction, voltage mitigation effects can be improved by inter-area coordination control. Finally, control performance is tested on a modified IEEE 33-node test case. Case studies demonstrate that by fully utilizing multi-source data, the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.}
}