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.