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

Yanda Huo; Peng Li; Haoran Ji; Hao Yu; Jinli Zhao; Wei Xi; Jianzhong Wu; Chengshan Wang

doi:10.17775/CSEEJPES.2022.02880

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

Regular Papers | 更新时间：2026-10-15

- Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks
- Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第5期页码：1876-1886
- 作者机构：
  
  1. School of Electrical and Information Engineering, Tianjin University,Tianjin,China
  2. Digital Power Grid Research Institute, China Southern Power Grid,Guangzhou,China
  3. School of Engineering, Cardiff University, Cardiff, U.K
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.02880
  中图分类号：
- 纸质出版：2024-09-01
- 稿件说明：
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Yanda Huo, Peng Li, Haoran Ji, 等. Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(5):1876-1886.

Yanda Huo, Peng Li, Haoran Ji, et al. Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks[J]. CSEE Journal of Power and Energy Systems, 2024, 10(5): 1876-1886.
Yanda Huo, Peng Li, Haoran Ji, 等. Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(5):1876-1886. DOI： 10.17775/CSEEJPES.2022.02880.

Yanda Huo, Peng Li, Haoran Ji, et al. Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks[J]. CSEE Journal of Power and Energy Systems, 2024, 10(5): 1876-1886. DOI： 10.17775/CSEEJPES.2022.02880.

摘要

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.

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