Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks

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. Howe...

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Bibliographic Details
Published in:CSEE Journal of Power and Energy Systems 2024-09, Vol.10 (5), p.1876-1886
Main Authors: Yanda Huo, Peng Li, Haoran Ji, Hao Yu, Jinli Zhao, Wei Xi, Jianzhong Wu, Chengshan Wang
Format: Article
Language:English
Subjects:
data-driven
distributed energy storage (DES)
distributed generators (DGs)
Distribution network
predictive voltage control
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Summary: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.
ISSN:2096-0042
DOI:10.17775/CSEEJPES.2022.02880