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Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables

Authors :
Zhengfa Zhang
Zhe Chen
Yifei Guo
Filipe Miguel Faria da Silva
Claus Leth Bak
Source :
Zhang, Z, Silva, F M F D, Guo, Y, Bak, C L & Chen, Z 2021, ' Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables ', Applied Energy, vol. 302, 117530 . https://doi.org/10.1016/j.apenergy.2021.117530
Publication Year :
2021

Abstract

The high penetration of renewable energy into distribution networks poses increasing challenges on voltage control. To address this issue, this paper presents a double-layer stochastic model predictive control algorithm to regulate voltage profile in active distribution networks. In the proposed algorithm, voltage regulation is achieved by coordination of an upper layer controller and a lower layer controller. In the upper layer, the number of operation of mechanical voltage regulation devices, including transformer with on-load tap changer and capacitor banks, is minimized in an hourly timescale. In the lower layer, the controller minimizes the active power curtailments and power losses with a control period of 5 min. The proposed double-layer stochastic model predictive voltage control utilizes not only the reactive power control, but also the active power curtailment to regulate bus voltages. In addition, mechanical voltage regulation devices and distributed generations are controlled in two different timescales. Case studies on a modified IEEE-33 bus system demonstrate that compared with traditional control and two-stage stochastic voltage control, the proposed algorithm can achieve an improvement of 8.05% and 7.43%, respectively.

Details

Language :
English
Database :
OpenAIRE
Journal :
Zhang, Z, Silva, F M F D, Guo, Y, Bak, C L & Chen, Z 2021, ' Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables ', Applied Energy, vol. 302, 117530 . https://doi.org/10.1016/j.apenergy.2021.117530
Accession number :
edsair.doi.dedup.....62d08cf17eaefa8da5e8b2d61ac58df6