Learning-based load control to support resilient networked microgrid operations

Radhakrishnan, Nikitha; Schneider, Kevin P.; Tuffner, Francis K.; Du, Wei; Bhattarai, Bishnu P.

doi:10.1049/iet-stg.2019.0265

Title: Learning-based load control to support resilient networked microgrid operations

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Abstract

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly Appliance ^TM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-D ^TM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

Authors:

^[1];

^[2];

^[2]; Du, Wei ^[1]; Bhattarai, Bishnu P. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Pacific Northwest National Lab. (PNNL), Seattle, WA (United States)

Publication Date:: Thu May 21 00:00:00 EDT 2020

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1721693

Report Number(s):: PNNL-SA-143536
Journal ID: ISSN 2515-2947

Grant/Contract Number:: AC05-76RL01830

Resource Type:: Accepted Manuscript

Journal Name:: IET Smart Grid

Additional Journal Information:: Journal Volume: 3; Journal Issue: 5; Journal ID: ISSN 2515-2947

Publisher:: The Institution of Engineering and Technology

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats


                    Radhakrishnan, Nikitha, Schneider, Kevin P., Tuffner, Francis K., Du, Wei, and Bhattarai, Bishnu P. Learning-based load control to support resilient networked microgrid operations.  United States: N. p., 2020. 
Web.  doi:10.1049/iet-stg.2019.0265.

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                    Radhakrishnan, Nikitha, Schneider, Kevin P., Tuffner, Francis K., Du, Wei, & Bhattarai, Bishnu P. Learning-based load control to support resilient networked microgrid operations.  United States.  https://doi.org/10.1049/iet-stg.2019.0265

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                    Radhakrishnan, Nikitha, Schneider, Kevin P., Tuffner, Francis K., Du, Wei, and Bhattarai, Bishnu P. Thu .  
"Learning-based load control to support resilient networked microgrid operations".  United States.  https://doi.org/10.1049/iet-stg.2019.0265.  https://www.osti.gov/servlets/purl/1721693.

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@article{osti_1721693,

  title        = {Learning-based load control to support resilient networked microgrid operations},

  author       = {Radhakrishnan, Nikitha and Schneider, Kevin P. and Tuffner, Francis K. and Du, Wei and Bhattarai, Bishnu P.},

  abstractNote = {Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly Appliance TM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-D TM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.},

  doi          = {10.1049/iet-stg.2019.0265},

  journal      = {IET Smart Grid},

  number       = 5,

  volume       = 3,

  place        = {United States},

  year         = {Thu May 21 00:00:00 EDT 2020},

  month        = {Thu May 21 00:00:00 EDT 2020}

}

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