Grid-Supportive Loads - A New Approach to Increasing Renewable Energy in Power Systems

Himanshu Jain, Barry Mather, Akshay Jain, Samuel Baldwin

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model's design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system.

Original languageAmerican English
Pages (from-to)2959-2972
Number of pages14
JournalIEEE Transactions on Smart Grid
Volume13
Issue number4
DOIs
StatePublished - 1 Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/JA-5D00-82408

Keywords

  • Electric vehicles
  • inverters
  • load modeling
  • power system dynamics
  • power system modeling
  • power system stability
  • renewable energy resources
  • solar power generation
  • wind power generation

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