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Regular Paper | Open Access

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

Yanda Huo1, Peng Li1, Haoran Ji1( ), Hao Yu1, Jinli Zhao1, Wei Xi2, Jianzhong Wu3, Chengshan Wang1
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Digital Power Grid Research Institute, China Southern Power Grid, Guangzhou 510630, China
School of Engineering, Cardiff University, Cardiff CF24 3AA, U.K
Show Author Information

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.

Keywords

Distribution networkdistributed energy storage (DES)distributed generators (DGs)data-drivenpredictive voltage control

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 5,
September 2024
Pages 1876-1886
DOI: 10.17775/CSEEJPES.2022.02880
Cite this article:
Huo Y, Li P, Ji H, et al. Data-driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks. CSEE Journal of Power and Energy Systems, 2024, 10(5): 1876-1886. https://doi.org/10.17775/CSEEJPES.2022.02880

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Received: 03 May 2022
Revised: 14 September 2022
Accepted: 03 November 2022
Published: 27 June 2023
© 2022 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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