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

Research output: Contribution to conferencePaper


Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The 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 environment 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 pages10
StatePublished - 2020
Event2020 American Control Conference (ACC) -
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference (ACC)

Bibliographical note

See NREL/CP-5D00-77796 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-76334


  • control
  • data
  • electric grid
  • flexible loads
  • linear parameter-varying modeling


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