Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection

Wenshu Jiao, Jian Chen, Qiuwei Wu, Canbing Li , Bin Zhou, Sheng Huang

    Research output: Contribution to journalJournal articleResearchpeer-review

    178 Downloads (Pure)


    This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, mitigate voltage fluctuations, and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden.
    Original languageEnglish
    JournalIEEE Transactions on Power Systems
    Issue number1
    Pages (from-to)680 - 690
    Publication statusPublished - 2021


    • Distributed control
    • Distribution network
    • Gradient projection (GP)
    • Model predictive control (MPC)
    • Voltage control


    Dive into the research topics of 'Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection'. Together they form a unique fingerprint.

    Cite this