Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations


Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

Original languageAmerican English
Number of pages7
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-76334 for preprint

NREL Publication Number

  • NREL/CP-5D00-77796


  • aggregates
  • autoregressive processes
  • data models
  • load modeling
  • power system stability
  • quality of service
  • temperature control


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