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

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摘要 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.
出处 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第5期1876-1886,共11页 中国电机工程学会电力与能源系统学报(英文)
基金 supported by the National Key R&D Program of China(2020YFB0906000,2020YFB0906001).
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