首页|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
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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.How-ever,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 measure-ment 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.
Distribution networkdistributed energy storage(DES)distributed generators(DGs)data-drivenpredictive voltage control
Yanda Huo、Peng Li、Haoran Ji、Hao Yu、Jinli Zhao、Wei Xi、Jianzhong Wu、Chengshan Wang
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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 CF243AA,U.K
National Key R&D Program of ChinaNational Key R&D Program of China
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