Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants

Marthi, Phani Ratna Vanamali; Debnath, Suman; Xia, Qianxue; Crow, Mariesa

Title: Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants

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Abstract

Primary frequency support functionality can be an added advantage for any new grid integrated power electronics resource. With increased penetration of power electronics resources, inertial and primary frequency response capabilities are decreasing. In this research paper, an advanced model-based predictive control (MBPC) algorithm for providing frequency support to a new topology of integrated photovoltaic(PV), batterybased energy storage systems (ESS), and high-voltage direct current (HV dc) systems called multi-port autonomus reconfigurable solar (MARS) plants is based on the synchronverter-based control. The simulation of MARS-HV dc system with proposed control strategy is simulated and validated for MARS connected to low short circuit ratio (SCR) grids in PSCAD/EMTDC simulation environment. The control algorithm proposed showcases better performance in terms of frequency nadir improvement and frequency steady state improvemnt with respect to no frequency control.

Authors:

^[1];

^[1]; Xia, Qianxue ^[1]; Crow, Mariesa ^[2]

ORNL
Missouri University of Science and Technology

Publication Date:: 2021-02-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:: 1777735

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: Innovative Smart Grid Technologies Conference (IEEE ISGT) - Virtual, Washington, United States of America - 2/15/2021 10:00:00 AM-

Country of Publication:: United States

Language:: English

Citation Formats


                    Marthi, Phani Ratna Vanamali, Debnath, Suman, Xia, Qianxue, and Crow, Mariesa. Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants.  United States: N. p., 2021. 
        Web.

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                    Marthi, Phani Ratna Vanamali, Debnath, Suman, Xia, Qianxue, & Crow, Mariesa. Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants.  United States.

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                    Marthi, Phani Ratna Vanamali, Debnath, Suman, Xia, Qianxue, and Crow, Mariesa. 2021.  
        "Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants".  United States.  https://www.osti.gov/servlets/purl/1777735.

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@article{osti_1777735,

  title        = {Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants},

  author       = {Marthi, Phani Ratna Vanamali and Debnath, Suman and Xia, Qianxue and Crow, Mariesa},

  abstractNote = {Primary frequency support functionality can be an added advantage for any new grid integrated power electronics resource. With increased penetration of power electronics resources, inertial and primary frequency response capabilities are decreasing. In this research paper, an advanced model-based predictive control (MBPC) algorithm for providing frequency support to a new topology of integrated photovoltaic(PV), batterybased energy storage systems (ESS), and high-voltage direct current (HV dc) systems called multi-port autonomus reconfigurable solar (MARS) plants is based on the synchronverter-based control. The simulation of MARS-HV dc system with proposed control strategy is simulated and validated for MARS connected to low short circuit ratio (SCR) grids in PSCAD/EMTDC simulation environment. The control algorithm proposed showcases better performance in terms of frequency nadir improvement and frequency steady state improvemnt with respect to no frequency control.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/1777735},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {2}

}

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