skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Abstract

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on 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. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly ApplianceTM(GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-DTMsimulation environment.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1737372
Report Number(s):
PNNL-SA-138672
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 3; Journal Issue: 5
Country of Publication:
United States
Language:
English

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.
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
Radhakrishnan, Nikitha, Schneider, Kevin P., Tuffner, Francis K., Du, Wei, and Bhattarai, Bishnu P. 2020. "Learning-Based Load Control to Support Resilient Networked Microgrid Operations". United States. https://doi.org/10.1049/iet-stg.2019.0265.
@article{osti_1737372,
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 = {Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on 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. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly ApplianceTM(GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-DTMsimulation environment.},
doi = {10.1049/iet-stg.2019.0265},
url = {https://www.osti.gov/biblio/1737372}, journal = {IEEE Transactions on Smart Grid},
number = 5,
volume = 3,
place = {United States},
year = {Thu Oct 01 00:00:00 EDT 2020},
month = {Thu Oct 01 00:00:00 EDT 2020}
}