Data-driven predictive voltage control for distributed energy storage in active distribution networks

Huo, Yanda, Ji, Haoran, Yu, Hao, Zhao, Jinli, Xi, Wei, Wu, Jianzhong and Wang, Chengsham 2024. Data-driven predictive voltage control for distributed energy storage in active distribution networks. CSEE Journal of Power and Energy Systems 10 (5) , pp. 1876-1886. 10.17775/CSEEJPES.2022.02880

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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 interarea 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
ISSN:	2096-0042
Date of First Compliant Deposit:	30 January 2025
Date of Acceptance:	3 November 2024
Last Modified:	05 Mar 2025 14:04
URI:	https://orca.cardiff.ac.uk/id/eprint/175753

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